Pilot Study: Confirmatory Factor Analysis

Data

Data Summary

Maximum Likelihood
DWLS
MIIV
MIIV Categorical
PLS
PLS Categorical

Last updated: 2021-02-18

Checks: 6 1

Knit directory: pools-projects/

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Data

source("code/load_packages.R")

-- Attaching packages --------------------------------------- tidyverse 1.3.0 --

v ggplot2 3.3.3     v purrr   0.3.4
v tibble  3.0.5     v dplyr   1.0.3
v tidyr   1.1.2     v stringr 1.4.0
v readr   1.4.0     v forcats 0.5.0

-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()


Attaching package: 'data.table'

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This is lavaan 0.6-7

lavaan is BETA software! Please report any bugs.

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This is semTools 0.5-4

All users of R (or SEM) are invited to submit functions or ideas for functions.

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Attaching package: 'semTools'

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This is MIIVsem 0.5.5

MIIVsem is BETA software! Please report any bugs.

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This is simsem 0.5-15

simsem is BETA software! Please report any bugs.

simsem was first developed at the University of Kansas Center for

Research Methods and Data Analysis, under NSF Grant 1053160.

#################################################################


Attaching package: 'simsem'

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Loading required package: nlme


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Attaching package: 'nFactors'

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options(digits=3, max.print = 10000)
mydata <- read.table("data/data-2020-11-16/pools_data_split2_2020_11_16.txt", sep="\t", header=T)


# transform responses to (-2, 2) scale
mydata[, 7:63] <- apply(mydata[,7:63], 2, function(x){x-3})

Data Summary

use.var <- c(paste0("Q4_",c(1:5,8:11, 15:18)), #13
             paste0("Q5_",c(1:6, 8, 12)), #8-> 14- 21
             paste0("Q6_",c(1:8, 11)), #9 -> 22-30
             paste0("Q7_",c(2, 4:5, 7:8, 12:14))) #31-38

psych::describe(
  mydata[, use.var]
)

      vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
Q4_1     1 312 -0.62 0.85   -1.0   -0.63 1.48  -2   2     4  0.28     0.06 0.05
Q4_2     2 312 -0.80 0.78   -1.0   -0.82 0.00  -2   2     4  0.64     1.07 0.04
Q4_3     3 312 -0.54 0.85   -1.0   -0.54 1.48  -2   2     4  0.35     0.47 0.05
Q4_4     4 312 -0.47 0.85    0.0   -0.46 1.48  -2   2     4 -0.01     0.12 0.05
Q4_5     5 312 -0.75 0.87   -1.0   -0.81 1.48  -2   2     4  0.61     0.32 0.05
Q4_8     6 312 -0.81 0.85   -1.0   -0.86 1.48  -2   2     4  0.50     0.16 0.05
Q4_9     7 312 -0.61 0.97   -1.0   -0.66 1.48  -2   2     4  0.45    -0.29 0.05
Q4_10    8 312 -0.46 0.80    0.0   -0.41 0.00  -2   2     4 -0.19     0.30 0.05
Q4_11    9 312 -0.52 0.95   -1.0   -0.56 1.48  -2   2     4  0.26    -0.26 0.05
Q4_15   10 312 -0.71 0.88   -1.0   -0.75 1.48  -2   2     4  0.34    -0.32 0.05
Q4_16   11 312 -0.67 0.93   -1.0   -0.72 1.48  -2   2     4  0.23    -0.50 0.05
Q4_17   12 312 -0.90 0.93   -1.0   -0.99 1.48  -2   2     4  0.53    -0.47 0.05
Q4_18   13 312 -0.72 0.79   -1.0   -0.74 0.00  -2   2     4  0.49     0.33 0.04
Q5_1    14 312 -0.47 0.95   -1.0   -0.49 1.48  -2   2     4  0.19    -0.49 0.05
Q5_2    15 312 -0.04 1.01    0.0    0.01 1.48  -2   2     4 -0.20    -0.49 0.06
Q5_3    16 312 -0.41 1.04    0.0   -0.43 1.48  -2   2     4  0.26    -0.52 0.06
Q5_4    17 312  0.46 1.10    1.0    0.55 0.00  -2   2     4 -0.80    -0.15 0.06
Q5_5    18 312  0.45 1.06    1.0    0.52 0.00  -2   2     4 -0.78    -0.16 0.06
Q5_6    19 312 -0.15 0.92    0.0   -0.14 1.48  -2   2     4  0.03    -0.07 0.05
Q5_8    20 312 -0.16 1.07    0.0   -0.13 1.48  -2   2     4 -0.09    -0.66 0.06
Q5_12   21 312 -0.20 1.01    0.0   -0.18 1.48  -2   2     4 -0.10    -0.29 0.06
Q6_1    22 312 -1.32 0.86   -1.5   -1.48 0.74  -2   2     4  1.52     2.55 0.05
Q6_2    23 312 -0.96 0.91   -1.0   -1.07 0.00  -2   2     4  0.98     0.92 0.05
Q6_3    24 312 -1.01 0.92   -1.0   -1.13 1.48  -2   2     4  1.03     1.03 0.05
Q6_4    25 312 -0.89 0.94   -1.0   -0.99 1.48  -2   2     4  0.84     0.64 0.05
Q6_5    26 312 -0.61 1.10   -1.0   -0.69 1.48  -2   2     4  0.57    -0.45 0.06
Q6_6    27 312 -1.18 0.79   -1.0   -1.29 0.00  -2   2     4  1.08     1.68 0.04
Q6_7    28 312 -0.89 0.88   -1.0   -0.95 1.48  -2   2     4  0.61     0.19 0.05
Q6_8    29 312 -0.85 0.83   -1.0   -0.90 0.00  -2   2     4  0.70     0.77 0.05
Q6_11   30 312 -0.31 0.98    0.0   -0.30 1.48  -2   2     4 -0.11    -0.29 0.06
Q7_2    31 312 -0.35 0.89    0.0   -0.33 0.00  -2   2     4 -0.35    -0.09 0.05
Q7_4    32 312 -0.21 0.94    0.0   -0.18 1.48  -2   2     4 -0.07    -0.15 0.05
Q7_5    33 312 -0.21 0.94    0.0   -0.19 0.00  -2   2     4 -0.14     0.12 0.05
Q7_7    34 312  0.57 1.07    1.0    0.66 0.00  -2   2     4 -0.86     0.08 0.06
Q7_8    35 312 -0.19 0.94    0.0   -0.16 0.00  -2   2     4 -0.07     0.03 0.05
Q7_12   36 312  0.38 1.07    1.0    0.44 1.48  -2   2     4 -0.55    -0.17 0.06
Q7_13   37 312  0.49 1.11    1.0    0.56 1.48  -2   2     4 -0.50    -0.42 0.06
Q7_14   38 312  0.56 1.04    1.0    0.64 1.48  -2   2     4 -0.69     0.00 0.06

CFA

The hypothesized four-factor solution is shown below.

The above model can be convert to code using the below model.

mod1 <- "
EL =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12
IN =~ Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14

EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN
"

mod1.2 <- "
EL =~ y1 + y2 + y3 + y4 + y5 + y6 + y7 + y8 + y9 + y10 + y11 + y12 + y13
SC =~ y14 + y15 + y16 + y17 + y18 + y19 + y20 + y21
IN =~ y22 + y23 + y24 + y25 + y26 + y27 + y28 + y29 + y30
EN =~ y31 + y32 + y33 + y34 + y35 + y36 + y37 + y38

EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN
"

Maximum Likelihood

fit1 <- lavaan::sem(mod1, data=mydata, estimator="MLM")
summary(fit1, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 62 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of free parameters                         82
                                                      
  Number of observations                           312
                                                      
Model Test User Model:
                                              Standard      Robust
  Test Statistic                              1999.775    1448.633
  Degrees of freedom                               659         659
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.380
       Satorra-Bentler correction                                 

Model Test Baseline Model:

  Test statistic                              8479.132    5614.687
  Degrees of freedom                               703         703
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.510

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.828       0.839
  Tucker-Lewis Index (TLI)                       0.816       0.828
                                                                  
  Robust Comparative Fit Index (CFI)                         0.853
  Robust Tucker-Lewis Index (TLI)                            0.843

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)             -12758.826  -12758.826
  Loglikelihood unrestricted model (H1)     -11758.939  -11758.939
                                                                  
  Akaike (AIC)                               25681.652   25681.652
  Bayesian (BIC)                             25988.578   25988.578
  Sample-size adjusted Bayesian (BIC)        25728.502   25728.502

Root Mean Square Error of Approximation:

  RMSEA                                          0.081       0.062
  90 Percent confidence interval - lower         0.077       0.058
  90 Percent confidence interval - upper         0.085       0.066
  P-value RMSEA <= 0.05                          0.000       0.000
                                                                  
  Robust RMSEA                                               0.073
  90 Percent confidence interval - lower                     0.068
  90 Percent confidence interval - upper                     0.078

Standardized Root Mean Square Residual:

  SRMR                                           0.080       0.080

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model          Structured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_1              1.000                               0.663    0.779
    Q4_2              0.898    0.058   15.367    0.000    0.595    0.761
    Q4_3              1.027    0.051   20.126    0.000    0.680    0.804
    Q4_4              1.036    0.054   19.336    0.000    0.686    0.810
    Q4_5              0.986    0.070   14.139    0.000    0.653    0.751
    Q4_8              0.927    0.065   14.179    0.000    0.614    0.720
    Q4_9              0.969    0.072   13.461    0.000    0.642    0.666
    Q4_10             0.928    0.059   15.844    0.000    0.615    0.769
    Q4_11             1.074    0.063   17.014    0.000    0.712    0.749
    Q4_15             0.958    0.061   15.583    0.000    0.635    0.719
    Q4_16             0.937    0.076   12.297    0.000    0.621    0.668
    Q4_17             0.837    0.081   10.341    0.000    0.555    0.597
    Q4_18             0.972    0.056   17.259    0.000    0.644    0.816
  SC =~                                                                 
    Q5_1              1.000                               0.547    0.575
    Q5_2              1.144    0.112   10.236    0.000    0.625    0.619
    Q5_3              1.250    0.122   10.266    0.000    0.683    0.657
    Q5_4              1.593    0.164    9.693    0.000    0.871    0.792
    Q5_5              1.548    0.151   10.220    0.000    0.846    0.800
    Q5_6              1.311    0.121   10.836    0.000    0.716    0.779
    Q5_8              1.527    0.147   10.411    0.000    0.835    0.780
    Q5_12             1.111    0.116    9.556    0.000    0.607    0.600
  IN =~                                                                 
    Q6_1              1.000                               0.586    0.686
    Q6_2              1.218    0.090   13.533    0.000    0.714    0.786
    Q6_3              1.258    0.103   12.216    0.000    0.738    0.799
    Q6_4              1.237    0.106   11.622    0.000    0.725    0.776
    Q6_5              0.855    0.119    7.190    0.000    0.501    0.456
    Q6_6              1.051    0.103   10.186    0.000    0.616    0.784
    Q6_7              1.227    0.112   10.925    0.000    0.719    0.822
    Q6_8              1.165    0.106   10.988    0.000    0.683    0.820
    Q6_11             1.030    0.121    8.506    0.000    0.604    0.619
  EN =~                                                                 
    Q7_2              1.000                               0.652    0.736
    Q7_4              0.927    0.069   13.499    0.000    0.605    0.642
    Q7_5              1.078    0.077   13.945    0.000    0.703    0.747
    Q7_7              1.150    0.105   10.923    0.000    0.750    0.704
    Q7_8              1.106    0.081   13.616    0.000    0.721    0.771
    Q7_12             1.085    0.106   10.239    0.000    0.708    0.665
    Q7_13             0.637    0.125    5.075    0.000    0.415    0.376
    Q7_14             1.020    0.108    9.463    0.000    0.666    0.639

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.205    0.040    5.123    0.000    0.566    0.566
    IN                0.268    0.043    6.216    0.000    0.689    0.689
    EN                0.306    0.045    6.788    0.000    0.707    0.707
  SC ~~                                                                 
    IN                0.169    0.034    5.002    0.000    0.528    0.528
    EN                0.275    0.043    6.357    0.000    0.771    0.771
  IN ~~                                                                 
    EN                0.257    0.042    6.112    0.000    0.671    0.671

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.439    0.060    7.309    0.000    1.000    1.000
    SC                0.299    0.057    5.252    0.000    1.000    1.000
    IN                0.344    0.066    5.207    0.000    1.000    1.000
    EN                0.426    0.062    6.838    0.000    1.000    1.000
   .Q4_1              0.284    0.032    8.766    0.000    0.284    0.393
   .Q4_2              0.258    0.024   10.671    0.000    0.258    0.421
   .Q4_3              0.254    0.028    8.991    0.000    0.254    0.354
   .Q4_4              0.246    0.023   10.568    0.000    0.246    0.343
   .Q4_5              0.330    0.042    7.928    0.000    0.330    0.436
   .Q4_8              0.351    0.049    7.182    0.000    0.351    0.482
   .Q4_9              0.519    0.047   11.144    0.000    0.519    0.557
   .Q4_10             0.261    0.025   10.523    0.000    0.261    0.409
   .Q4_11             0.397    0.039   10.268    0.000    0.397    0.439
   .Q4_15             0.376    0.036   10.382    0.000    0.376    0.482
   .Q4_16             0.478    0.041   11.732    0.000    0.478    0.553
   .Q4_17             0.555    0.051   10.956    0.000    0.555    0.643
   .Q4_18             0.208    0.021    9.942    0.000    0.208    0.334
   .Q5_1              0.604    0.049   12.256    0.000    0.604    0.669
   .Q5_2              0.630    0.058   10.789    0.000    0.630    0.617
   .Q5_3              0.614    0.054   11.337    0.000    0.614    0.568
   .Q5_4              0.451    0.047    9.586    0.000    0.451    0.373
   .Q5_5              0.403    0.052    7.747    0.000    0.403    0.360
   .Q5_6              0.333    0.035    9.624    0.000    0.333    0.393
   .Q5_8              0.448    0.061    7.364    0.000    0.448    0.392
   .Q5_12             0.654    0.067    9.699    0.000    0.654    0.640
   .Q6_1              0.387    0.064    6.062    0.000    0.387    0.529
   .Q6_2              0.315    0.040    7.808    0.000    0.315    0.382
   .Q6_3              0.308    0.040    7.624    0.000    0.308    0.362
   .Q6_4              0.347    0.047    7.445    0.000    0.347    0.397
   .Q6_5              0.955    0.086   11.116    0.000    0.955    0.792
   .Q6_6              0.238    0.027    8.758    0.000    0.238    0.385
   .Q6_7              0.249    0.027    9.142    0.000    0.249    0.325
   .Q6_8              0.228    0.029    7.853    0.000    0.228    0.328
   .Q6_11             0.587    0.053   11.066    0.000    0.587    0.617
   .Q7_2              0.360    0.039    9.197    0.000    0.360    0.459
   .Q7_4              0.521    0.051   10.141    0.000    0.521    0.587
   .Q7_5              0.392    0.047    8.255    0.000    0.392    0.442
   .Q7_7              0.574    0.054   10.600    0.000    0.574    0.505
   .Q7_8              0.355    0.043    8.219    0.000    0.355    0.406
   .Q7_12             0.633    0.067    9.385    0.000    0.633    0.558
   .Q7_13             1.045    0.082   12.716    0.000    1.045    0.858
   .Q7_14             0.643    0.060   10.756    0.000    0.643    0.592

Local Fit Assessment

# Residual Analysis
out <- residuals(fit1, type="cor.bollen")
kable(out[[2]], format="html", digit=3)%>%
  kable_styling(full_width = T)%>%
  scroll_box(width="100%", height="800px")

	Q4_1	Q4_2	Q4_3	Q4_4	Q4_5	Q4_8	Q4_9	Q4_10	Q4_11	Q4_15	Q4_16	Q4_17	Q4_18	Q5_1	Q5_2	Q5_3	Q5_4	Q5_5	Q5_6	Q5_8	Q5_12	Q6_1	Q6_2	Q6_3	Q6_4	Q6_5	Q6_6	Q6_7	Q6_8	Q6_11	Q7_2	Q7_4	Q7_5	Q7_7	Q7_8	Q7_12	Q7_13	Q7_14
Q4_1	0.000	0.066	0.094	0.041	-0.075	-0.089	-0.069	0.030	0.000	-0.035	0.000	-0.033	-0.004	0.149	0.007	0.049	-0.066	-0.042	0.029	-0.049	0.076	0.011	-0.034	-0.067	-0.082	0.045	-0.019	-0.012	-0.044	0.154	-0.014	0.069	-0.057	-0.054	0.007	-0.066	0.050	-0.047
Q4_2	0.066	0.000	0.064	0.006	0.013	0.013	-0.074	-0.052	-0.023	-0.013	-0.069	-0.061	0.060	0.149	-0.045	0.020	-0.162	-0.153	-0.005	-0.104	0.025	-0.022	-0.023	-0.062	-0.064	0.041	0.001	0.005	-0.014	0.108	-0.056	-0.001	-0.023	-0.131	-0.045	-0.081	0.009	-0.077
Q4_3	0.094	0.064	0.000	0.083	-0.050	-0.064	-0.088	0.000	-0.033	-0.024	0.016	-0.076	0.002	0.184	-0.038	0.040	-0.134	-0.104	-0.034	-0.069	0.088	-0.010	-0.063	-0.086	-0.061	0.021	0.021	-0.033	-0.028	0.166	-0.003	0.026	-0.090	-0.078	-0.042	-0.005	0.045	-0.023
Q4_4	0.041	0.006	0.083	0.000	-0.007	-0.005	-0.049	0.038	0.011	-0.067	-0.065	-0.060	-0.021	0.100	-0.041	0.023	-0.097	-0.054	0.054	-0.011	0.085	-0.100	-0.041	-0.073	-0.053	0.016	0.020	-0.029	-0.055	0.210	0.079	0.104	0.008	-0.029	0.055	-0.050	-0.015	-0.019
Q4_5	-0.075	0.013	-0.050	-0.007	0.000	0.123	0.041	0.005	0.024	0.008	-0.048	0.038	-0.002	0.149	0.004	0.112	-0.106	-0.099	0.068	0.055	0.099	-0.029	0.017	-0.044	-0.080	0.032	0.017	0.052	-0.030	0.148	0.059	-0.010	-0.019	-0.023	0.037	-0.122	-0.153	-0.036
Q4_8	-0.089	0.013	-0.064	-0.005	0.123	0.000	0.102	-0.010	-0.012	0.031	-0.021	-0.012	-0.016	0.151	-0.001	0.085	-0.099	-0.046	0.049	0.048	0.114	-0.013	-0.037	-0.029	0.008	0.069	0.068	0.084	0.031	0.101	0.068	0.006	0.041	-0.086	0.093	-0.063	-0.054	-0.042
Q4_9	-0.069	-0.074	-0.088	-0.049	0.041	0.102	0.000	0.013	0.062	0.059	-0.048	0.022	0.030	0.208	0.077	0.098	-0.042	-0.014	0.052	0.015	0.208	0.033	0.010	-0.026	-0.009	0.096	-0.003	0.075	-0.033	0.136	0.191	0.041	0.056	-0.019	0.091	0.118	0.056	0.006
Q4_10	0.030	-0.052	0.000	0.038	0.005	-0.010	0.013	0.000	-0.009	-0.025	0.028	-0.029	-0.036	0.109	0.037	0.018	-0.047	-0.023	0.034	0.011	0.203	-0.020	-0.013	-0.041	-0.070	0.024	0.028	0.069	-0.025	0.233	0.096	0.061	0.025	0.024	0.063	0.017	0.001	0.085
Q4_11	0.000	-0.023	-0.033	0.011	0.024	-0.012	0.062	-0.009	0.000	-0.057	-0.017	0.038	-0.034	0.241	0.049	0.097	-0.109	-0.059	0.035	0.002	0.180	0.032	-0.011	-0.025	-0.080	0.132	0.064	0.120	0.079	0.193	0.145	0.086	0.181	0.081	0.139	0.064	0.031	-0.007
Q4_15	-0.035	-0.013	-0.024	-0.067	0.008	0.031	0.059	-0.025	-0.057	0.000	0.154	0.044	0.042	0.189	0.071	0.129	-0.045	-0.018	0.022	0.017	0.219	0.036	-0.010	-0.032	0.008	0.085	-0.020	0.068	-0.023	0.153	-0.036	-0.050	-0.049	-0.096	-0.054	-0.004	-0.025	0.053
Q4_16	0.000	-0.069	0.016	-0.065	-0.048	-0.021	-0.048	0.028	-0.017	0.154	0.000	0.110	0.016	0.158	0.064	0.118	-0.013	0.011	0.049	0.035	0.259	0.077	-0.038	-0.070	-0.012	0.089	-0.014	0.033	-0.026	0.176	-0.010	0.027	-0.029	-0.032	0.039	0.005	0.006	-0.012
Q4_17	-0.033	-0.061	-0.076	-0.060	0.038	-0.012	0.022	-0.029	0.038	0.044	0.110	0.000	0.067	0.014	-0.003	-0.025	-0.056	-0.073	-0.032	-0.039	0.131	-0.001	-0.021	0.004	-0.003	0.055	0.092	0.098	0.020	0.128	0.085	0.009	0.031	-0.016	0.027	-0.052	-0.046	-0.051
Q4_18	-0.004	0.060	0.002	-0.021	-0.002	-0.016	0.030	-0.036	-0.034	0.042	0.016	0.067	0.000	0.131	0.030	0.017	-0.156	-0.111	0.011	-0.058	0.140	-0.036	-0.048	-0.082	-0.090	0.074	0.002	0.069	-0.004	0.113	0.012	0.020	-0.032	-0.165	-0.067	-0.085	-0.071	-0.089
Q5_1	0.149	0.149	0.184	0.100	0.149	0.151	0.208	0.109	0.241	0.189	0.158	0.014	0.131	0.000	0.164	0.095	-0.073	-0.091	-0.046	-0.043	0.029	0.102	0.105	0.104	0.129	0.073	0.144	0.146	0.143	0.258	0.111	0.081	0.150	0.065	0.057	0.112	0.041	0.054
Q5_2	0.007	-0.045	-0.038	-0.041	0.004	-0.001	0.077	0.037	0.049	0.071	0.064	-0.003	0.030	0.164	0.000	0.136	-0.005	-0.012	-0.051	-0.077	-0.012	-0.010	0.049	0.007	0.104	0.042	0.085	0.019	0.048	0.207	0.084	-0.016	-0.025	-0.037	-0.115	0.041	0.000	0.092
Q5_3	0.049	0.020	0.040	0.023	0.112	0.085	0.098	0.018	0.097	0.129	0.118	-0.025	0.017	0.095	0.136	0.000	-0.032	-0.053	0.017	-0.008	-0.037	0.077	0.052	-0.030	0.080	0.124	-0.014	0.029	-0.009	0.147	0.011	-0.041	0.014	-0.035	-0.053	-0.049	-0.088	0.020
Q5_4	-0.066	-0.162	-0.134	-0.097	-0.106	-0.099	-0.042	-0.047	-0.109	-0.045	-0.013	-0.056	-0.156	-0.073	-0.005	-0.032	0.000	0.164	-0.041	0.002	-0.083	-0.141	-0.106	-0.136	-0.043	0.053	-0.090	-0.054	-0.140	0.155	-0.060	-0.040	-0.079	0.038	-0.120	0.012	-0.012	0.094
Q5_5	-0.042	-0.153	-0.104	-0.054	-0.099	-0.046	-0.014	-0.023	-0.059	-0.018	0.011	-0.073	-0.111	-0.091	-0.012	-0.053	0.164	0.000	-0.009	-0.015	-0.080	-0.116	-0.116	-0.148	-0.050	0.005	-0.105	-0.094	-0.094	0.145	-0.007	-0.057	-0.070	-0.010	-0.109	0.041	0.022	0.126
Q5_6	0.029	-0.005	-0.034	0.054	0.068	0.049	0.052	0.034	0.035	0.022	0.049	-0.032	0.011	-0.046	-0.051	0.017	-0.041	-0.009	0.000	0.078	0.007	-0.066	-0.020	-0.051	0.009	0.080	0.039	0.017	-0.023	0.225	0.004	0.044	0.010	-0.012	0.052	-0.072	-0.111	0.016
Q5_8	-0.049	-0.104	-0.069	-0.011	0.055	0.048	0.015	0.011	0.002	0.017	0.035	-0.039	-0.058	-0.043	-0.077	-0.008	0.002	-0.015	0.078	0.000	0.048	-0.064	-0.030	-0.113	0.024	0.033	0.005	0.026	-0.034	0.189	-0.021	-0.066	-0.024	-0.019	0.020	-0.074	-0.123	0.034
Q5_12	0.076	0.025	0.088	0.085	0.099	0.114	0.208	0.203	0.180	0.219	0.259	0.131	0.140	0.029	-0.012	-0.037	-0.083	-0.080	0.007	0.048	0.000	0.070	0.127	0.068	0.102	0.184	0.170	0.286	0.152	0.298	0.123	0.113	0.122	0.093	0.144	0.089	0.059	0.128
Q6_1	0.011	-0.022	-0.010	-0.100	-0.029	-0.013	0.033	-0.020	0.032	0.036	0.077	-0.001	-0.036	0.102	-0.010	0.077	-0.141	-0.116	-0.066	-0.064	0.070	0.000	0.132	0.071	0.037	-0.005	-0.033	-0.040	-0.038	-0.125	-0.022	-0.082	-0.103	-0.216	-0.129	-0.076	0.066	-0.150
Q6_2	-0.034	-0.023	-0.063	-0.041	0.017	-0.037	0.010	-0.013	-0.011	-0.010	-0.038	-0.021	-0.048	0.105	0.049	0.052	-0.106	-0.116	-0.020	-0.030	0.127	0.132	0.000	0.053	0.117	-0.052	-0.066	-0.084	-0.001	-0.051	-0.006	0.062	-0.030	-0.126	-0.110	-0.090	-0.048	-0.103
Q6_3	-0.067	-0.062	-0.086	-0.073	-0.044	-0.029	-0.026	-0.041	-0.025	-0.032	-0.070	0.004	-0.082	0.104	0.007	-0.030	-0.136	-0.148	-0.051	-0.113	0.068	0.071	0.053	0.000	0.031	-0.030	-0.009	0.011	-0.029	-0.052	-0.006	0.001	-0.037	-0.139	-0.073	-0.106	-0.057	-0.082
Q6_4	-0.082	-0.064	-0.061	-0.053	-0.080	0.008	-0.009	-0.070	-0.080	0.008	-0.012	-0.003	-0.090	0.129	0.104	0.080	-0.043	-0.050	0.009	0.024	0.102	0.037	0.117	0.031	0.000	-0.127	-0.044	-0.069	0.042	-0.039	0.035	0.016	-0.020	-0.100	-0.089	-0.021	0.016	-0.046
Q6_5	0.045	0.041	0.021	0.016	0.032	0.069	0.096	0.024	0.132	0.085	0.089	0.055	0.074	0.073	0.042	0.124	0.053	0.005	0.080	0.033	0.184	-0.005	-0.052	-0.030	-0.127	0.000	0.044	0.086	-0.023	0.070	0.009	0.030	0.042	0.017	0.072	0.042	0.072	0.019
Q6_6	-0.019	0.001	0.021	0.020	0.017	0.068	-0.003	0.028	0.064	-0.020	-0.014	0.092	0.002	0.144	0.085	-0.014	-0.090	-0.105	0.039	0.005	0.170	-0.033	-0.066	-0.009	-0.044	0.044	0.000	0.071	0.003	0.035	0.142	0.051	0.096	-0.086	0.015	-0.059	-0.016	-0.031
Q6_7	-0.012	0.005	-0.033	-0.029	0.052	0.084	0.075	0.069	0.120	0.068	0.033	0.098	0.069	0.146	0.019	0.029	-0.054	-0.094	0.017	0.026	0.286	-0.040	-0.084	0.011	-0.069	0.086	0.071	0.000	0.024	0.002	0.104	0.060	0.123	-0.071	0.043	-0.010	0.014	-0.024
Q6_8	-0.044	-0.014	-0.028	-0.055	-0.030	0.031	-0.033	-0.025	0.079	-0.023	-0.026	0.020	-0.004	0.143	0.048	-0.009	-0.140	-0.094	-0.023	-0.034	0.152	-0.038	-0.001	-0.029	0.042	-0.023	0.003	0.024	0.000	-0.023	0.107	0.134	0.122	-0.095	0.012	-0.020	0.003	-0.070
Q6_11	0.154	0.108	0.166	0.210	0.148	0.101	0.136	0.233	0.193	0.153	0.176	0.128	0.113	0.258	0.207	0.147	0.155	0.145	0.225	0.189	0.298	-0.125	-0.051	-0.052	-0.039	0.070	0.035	0.002	-0.023	0.000	0.250	0.243	0.189	0.217	0.217	0.199	0.076	0.241
Q7_2	-0.014	-0.056	-0.003	0.079	0.059	0.068	0.191	0.096	0.145	-0.036	-0.010	0.085	0.012	0.111	0.084	0.011	-0.060	-0.007	0.004	-0.021	0.123	-0.022	-0.006	-0.006	0.035	0.009	0.142	0.104	0.107	0.250	0.000	0.066	0.024	-0.038	0.007	-0.075	-0.070	-0.082
Q7_4	0.069	-0.001	0.026	0.104	-0.010	0.006	0.041	0.061	0.086	-0.050	0.027	0.009	0.020	0.081	-0.016	-0.041	-0.040	-0.057	0.044	-0.066	0.113	-0.082	0.062	0.001	0.016	0.030	0.051	0.060	0.134	0.243	0.066	0.000	0.083	-0.021	-0.006	-0.105	-0.146	-0.082
Q7_5	-0.057	-0.023	-0.090	0.008	-0.019	0.041	0.056	0.025	0.181	-0.049	-0.029	0.031	-0.032	0.150	-0.025	0.014	-0.079	-0.070	0.010	-0.024	0.122	-0.103	-0.030	-0.037	-0.020	0.042	0.096	0.123	0.122	0.189	0.024	0.083	0.000	-0.044	0.041	-0.054	-0.074	-0.064
Q7_7	-0.054	-0.131	-0.078	-0.029	-0.023	-0.086	-0.019	0.024	0.081	-0.096	-0.032	-0.016	-0.165	0.065	-0.037	-0.035	0.038	-0.010	-0.012	-0.019	0.093	-0.216	-0.126	-0.139	-0.100	0.017	-0.086	-0.071	-0.095	0.217	-0.038	-0.021	-0.044	0.000	0.065	0.090	0.072	0.032
Q7_8	0.007	-0.045	-0.042	0.055	0.037	0.093	0.091	0.063	0.139	-0.054	0.039	0.027	-0.067	0.057	-0.115	-0.053	-0.120	-0.109	0.052	0.020	0.144	-0.129	-0.110	-0.073	-0.089	0.072	0.015	0.043	0.012	0.217	0.007	-0.006	0.041	0.065	0.000	-0.039	-0.052	-0.044
Q7_12	-0.066	-0.081	-0.005	-0.050	-0.122	-0.063	0.118	0.017	0.064	-0.004	0.005	-0.052	-0.085	0.112	0.041	-0.049	0.012	0.041	-0.072	-0.074	0.089	-0.076	-0.090	-0.106	-0.021	0.042	-0.059	-0.010	-0.020	0.199	-0.075	-0.105	-0.054	0.090	-0.039	0.000	0.285	0.203
Q7_13	0.050	0.009	0.045	-0.015	-0.153	-0.054	0.056	0.001	0.031	-0.025	0.006	-0.046	-0.071	0.041	0.000	-0.088	-0.012	0.022	-0.111	-0.123	0.059	0.066	-0.048	-0.057	0.016	0.072	-0.016	0.014	0.003	0.076	-0.070	-0.146	-0.074	0.072	-0.052	0.285	0.000	0.132
Q7_14	-0.047	-0.077	-0.023	-0.019	-0.036	-0.042	0.006	0.085	-0.007	0.053	-0.012	-0.051	-0.089	0.054	0.092	0.020	0.094	0.126	0.016	0.034	0.128	-0.150	-0.103	-0.082	-0.046	0.019	-0.031	-0.024	-0.070	0.241	-0.082	-0.082	-0.064	0.032	-0.044	0.203	0.132	0.000

ggcorrplot(out[[2]], type = "lower")

# modification indices
modindices(fit1, minimum.value = 10, sort = TRUE)

      lhs op   rhs    mi    epc sepc.lv sepc.all sepc.nox
673  Q5_4 ~~  Q5_5 104.4  0.320   0.320    0.750    0.750
200    EN =~ Q6_11  87.8  0.979   0.639    0.655    0.655
901 Q7_12 ~~ Q7_13  59.2  0.374   0.374    0.460    0.460
133    SC =~ Q6_11  54.3  0.768   0.420    0.431    0.431
103    EL =~ Q6_11  47.5  0.698   0.462    0.474    0.474
902 Q7_12 ~~ Q7_14  47.0  0.272   0.272    0.426    0.426
785  Q6_2 ~~  Q6_4  38.0  0.135   0.135    0.407    0.407
191    EN =~ Q5_12  37.3  0.848   0.553    0.547    0.547
768  Q6_1 ~~  Q6_2  33.6  0.128   0.128    0.368    0.368
162    IN =~ Q5_12  31.4  0.574   0.336    0.333    0.333
498 Q4_15 ~~ Q4_16  31.1  0.142   0.142    0.334    0.334
87     EL =~  Q5_1  29.3  0.480   0.318    0.334    0.334
343  Q4_5 ~~  Q4_8  26.2  0.106   0.106    0.312    0.312
788  Q6_2 ~~  Q6_7  25.8 -0.097  -0.097   -0.348   -0.348
94     EL =~ Q5_12  25.3  0.466   0.309    0.306    0.306
179    EN =~ Q4_11  24.7  0.458   0.298    0.314    0.314
202  Q4_1 ~~  Q4_3  24.7  0.085   0.085    0.315    0.315
90     EL =~  Q5_4  24.2 -0.409  -0.271   -0.247   -0.247
184    EN =~  Q5_1  23.6  0.645   0.421    0.443    0.443
604  Q5_1 ~~  Q5_2  23.1  0.179   0.179    0.290    0.290
274  Q4_3 ~~  Q4_4  23.0  0.077   0.077    0.309    0.309
757 Q5_12 ~~  Q6_7  22.7  0.121   0.121    0.299    0.299
155    IN =~  Q5_1  21.8  0.457   0.268    0.282    0.282
714  Q5_6 ~~  Q5_8  19.4  0.120   0.120    0.311    0.311
628  Q5_2 ~~  Q5_3  19.2  0.167   0.167    0.268    0.268
492 Q4_11 ~~  Q7_5  19.0  0.107   0.107    0.272    0.272
813  Q6_4 ~~  Q6_5  18.6 -0.151  -0.151   -0.263   -0.263
192    EN =~  Q6_1  18.5 -0.370  -0.241   -0.282   -0.282
838  Q6_6 ~~  Q6_7  18.2  0.071   0.071    0.291    0.291
159    IN =~  Q5_5  17.6 -0.367  -0.215   -0.203   -0.203
374  Q4_5 ~~ Q7_13  17.0 -0.143  -0.143   -0.244   -0.244
775  Q6_1 ~~ Q6_11  17.0 -0.118  -0.118   -0.248   -0.248
166    IN =~  Q7_7  16.9 -0.492  -0.288   -0.271   -0.271
815  Q6_4 ~~  Q6_7  16.5 -0.081  -0.081   -0.276   -0.276
158    IN =~  Q5_4  15.8 -0.365  -0.214   -0.195   -0.195
205  Q4_1 ~~  Q4_8  15.3 -0.076  -0.076   -0.241   -0.241
194    EN =~  Q6_3  15.1 -0.312  -0.204   -0.220   -0.220
713  Q5_5 ~~ Q7_14  14.8  0.125   0.125    0.246    0.246
647  Q5_2 ~~  Q7_8  14.6 -0.114  -0.114   -0.240   -0.240
277  Q4_3 ~~  Q4_9  14.6 -0.085  -0.085   -0.235   -0.235
887  Q7_4 ~~ Q7_13  14.5 -0.167  -0.167   -0.227   -0.227
607  Q5_1 ~~  Q5_5  14.0 -0.120  -0.120   -0.244   -0.244
376  Q4_8 ~~  Q4_9  13.4  0.094   0.094    0.220    0.220
729  Q5_6 ~~  Q7_8  13.1  0.082   0.082    0.239    0.239
431  Q4_9 ~~  Q7_2  13.0  0.096   0.096    0.221    0.221
886  Q7_4 ~~ Q7_12  12.8 -0.128  -0.128   -0.222   -0.222
91     EL =~  Q5_5  12.7 -0.283  -0.187   -0.177   -0.177
787  Q6_2 ~~  Q6_6  12.6 -0.064  -0.064   -0.235   -0.235
484 Q4_11 ~~  Q6_4  12.4 -0.081  -0.081   -0.218   -0.218
204  Q4_1 ~~  Q4_5  12.4 -0.067  -0.067   -0.219   -0.219
676  Q5_4 ~~ Q5_12  12.0 -0.123  -0.123   -0.225   -0.225
141    SC =~ Q7_14  12.0  0.579   0.317    0.304    0.304
127    SC =~  Q6_3  11.8 -0.275  -0.150   -0.163   -0.163
903 Q7_13 ~~ Q7_14  11.8  0.168   0.168    0.204    0.204
862  Q6_8 ~~  Q7_5  11.8  0.067   0.067    0.224    0.224
696  Q5_5 ~~ Q5_12  11.6 -0.115  -0.115   -0.223   -0.223
163    IN =~  Q7_2  11.5  0.329   0.193    0.217    0.217
526 Q4_16 ~~ Q4_17  11.4  0.103   0.103    0.199    0.199
895  Q7_7 ~~ Q7_12  11.3  0.129   0.129    0.214    0.214
183    EN =~ Q4_18  11.2 -0.228  -0.149   -0.189   -0.189
827  Q6_5 ~~  Q6_7  11.0  0.101   0.101    0.207    0.207
373  Q4_5 ~~ Q7_12  10.9 -0.092  -0.092   -0.201   -0.201
883  Q7_4 ~~  Q7_5  10.7  0.095   0.095    0.211    0.211
535 Q4_16 ~~ Q5_12  10.7  0.109   0.109    0.194    0.194
238  Q4_2 ~~  Q4_3  10.7  0.053   0.053    0.206    0.206
656  Q5_3 ~~  Q6_1  10.5  0.096   0.096    0.196    0.196
97     EL =~  Q6_3  10.5 -0.252  -0.167   -0.181   -0.181
177    EN =~  Q4_9  10.5  0.335   0.218    0.226    0.226
769  Q6_1 ~~  Q6_3  10.4  0.071   0.071    0.206    0.206
747  Q5_8 ~~  Q7_8  10.4  0.085   0.085    0.213    0.213
314  Q4_4 ~~ Q4_15  10.2 -0.061  -0.061   -0.200   -0.200
525 Q4_15 ~~ Q7_14  10.2  0.095   0.095    0.193    0.193
730  Q5_6 ~~ Q7_12  10.2 -0.093  -0.093   -0.203   -0.203
875 Q6_11 ~~ Q7_14  10.2  0.117   0.117    0.191    0.191
689  Q5_4 ~~  Q7_7  10.1  0.105   0.105    0.206    0.206
248  Q4_2 ~~ Q4_18  10.1  0.047   0.047    0.201    0.201
882  Q7_2 ~~ Q7_14  10.0 -0.098  -0.098   -0.203   -0.203

ROPE Probability Approx

# set up data
dat2 <- mydata[, use.var]
colnames(dat2) <- c(paste0("y", 1:38))

fit1 <- lavaan::sem(mod1.2, data=dat2)

# Probility method
source("code/utility_functions.R")

# ========================================== #
# ========================================== #
#   function: get_prior_dens()
# ========================================== #
# use: gets the appropriate prior for the 
#       parameter of interest
#
get_prior_dens <- function(pvalue, pname,...){
  if(pname %like% 'lambda'){
    out <- dnorm(pvalue, 0, 1, log=T)
  }
  if(pname %like% 'dphi'){
    out <- dgamma(pvalue, 1, 0.5, log=T)
  }
  if(pname %like% 'odphi'){
    out <- dnorm(pvalue, 0, 1, log=T)
  }
  if(pname %like% 'dpsi'){
    out <- dgamma(pvalue, 1, 0.5, log=T)
  }
  if(pname %like% 'odpsi'){
    out <- dnorm(pvalue, 0, 1, log=T)
  }
  if(pname %like% 'eta'){
    out <- dnorm(pvalue, 0, 10, log=T)
  }
  if(pname %like% 'tau'){
    out <- dnorm(pvalue, 0, 32, log=T)
  }
  return(out)
}

# ========================================== #
# ========================================== #
#   function: get_log_post()
# ========================================== #
# use: uses the model, parameters, and data to
#       to calculate log posterior
#
# arguments:
# p        - names vector of parameters
# sample.data - data frame of raw data
# cfa.model - list of model components
#
get_log_post <- function(p, sample.data, cfa.model,...) {
  
  out <- use_cfa_model(p, cov(sample.data), cfa.model)
  
  log_lik <- sum(apply(sample.data, 1, dmvnorm,
                       mean=out[['tau']],
                       sigma=out[['Sigma']], log=T))
  
  log_prior<-0
  if(length(p)==1){
    log_prior <- get_prior_dens(p, names(p))
  } else {
    i <- 1
    for(i in 1:length(p)){
      log_prior <- log_prior + get_prior_dens(p[i], names(p)[i])
    }
  }
  log_post <- log_lik + log_prior
  log_post
}

# ========================================== #
# ========================================== #
#   function: use_cfa_model()
# ========================================== #
# use: take in parameters, data, and model to 
#         obtain the log-likelihood
#
# arguments:
# theta - vector of parameters being optimized
# sample.cov - samplecovariance matrix
# cfa.model - list of model parameters
use_cfa_model <- function(theta, sample.cov, cfa.model,...){
  # Compue sample statistics
  p<-ncol(sample.cov)
  S<-sample.cov
  
  # unpack model
  lambda <- cfa.model[[1]]
  phi <- cfa.model[[2]]
  psi <- cfa.model[[3]]
  #tau <- cfaModel[[4]]
  #eta <- cfaModel[[5]]
  
  # number factor loadings
  lam.num <- length(which(is.na(lambda)))
  lambda[which(is.na(lambda))] <- theta[1:lam.num]
  nF = ncol(lambda)
  # number elements in factor (co)variance matrix
  phi.num <- length(which(is.na(phi)))
  dphi.num <- sum(is.na(diag(phi))==T)
  odphi.num <- sum(is.na(phi[lower.tri(phi)])==T)
  if(phi.num > 0){
    if(dphi.num == 0){
      phi[which(is.na(phi))] <- theta[(lam.num+1):(lam.num+phi.num)]
    } else {
      diag(phi) <- theta[(lam.num+1):(lam.num+dphi.num)]
      phi[which(is.na(phi))] <- theta[(lam.num+dphi.num+1):(lam.num+phi.num)]
    }
  }
  phi <- low2full(phi) # map lower to upper
  
  # number elements in error (co)variance matrix
  psi.num <- length(which(is.na(psi)))
  dpsi.num <- sum(is.na(diag(psi))==T)
  odpsi.num <- sum(is.na(psi[lower.tri(psi)])==T)
  if(psi.num > 0){
    if(dpsi.num == 0){
      psi[which(is.na(psi))] <- theta[(lam.num+1):(lam.num+psi.num)]
    } else {
      diag(psi) <- theta[(lam.num+1):(lam.num+dpsi.num)]
      psi[which(is.na(psi))] <- theta[(lam.num+dpsi.num+1):(lam.num+psi.num)]
    }
  }
  psi <- low2full(psi)
  # number of factor scores
  #eta.num <- length(eta)
  #eta <- matrix(theta[(lam.num+phi.num+psi.num+tau.num+1):(lam.num+phi.num+psi.num+tau.num+eta.num)],
  #              nrow=nF)
  # mean center eta
  #for(i in 1:nF){
  #  eta[i, ] <- eta[i,] - mean(eta[,i])
  #}
  
  # # number of intercepts
  # tau.num <- length(tau)
  # tau <- matrix(theta[(lam.num+phi.num+psi.num+1):(lam.num+phi.num+psi.num+tau.num)], ncol=1)
  # tau <- repeat_col(tau, ncol(eta))
  
  # compute model observed outcomes
  #Y <- tau + lambda%*%eta
  tau <- numeric(p)
  # compute model implied (co)variance matrix
  Sigma<-lambda%*%phi%*%(t(lambda)) + psi
  
  #return fit value 
  out <- list(Sigma, lambda, phi, psi, tau)
  names(out) <- c('Sigma', 'lambda', 'phi', 'psi', 'tau')
  return(out)
}



# ========================================== #
# ========================================== #
#   function: laplace_local_fit()
# ========================================== #
# use: uses the fittes lavaan object to run
#       the proposed method
#
# arguments:
# fit       - fitted lavaan model
# standardized - logical for whether to standardize
# cut.load  - cutoff for value of loading to care about default = 0.3 
# cut.cov   - cutoff for value of covariances to care about default = 0.1
# opt       - list of parameters to pass to interior functions
# sum.print - logical indicator of whether to print the summary table upon completion
# counter   - logical indicator of whether to print out a (.) after each
#               parameter is completed
#
#laplace_local_fit <- function(fit, cut.load = 0.3, cut.cov = 0.1, standardize=T,
#                              opt=list(scale.cov=1, no.samples=1000),
#                              all.parameters=F,
#                              sum.print=F, pb=T,...){
  
  
fit=fit1
cut.load = 0.6
cut.cov = 0.25
standardize=T
opt=list(scale.cov=1, no.samples=1000)
all.parameters=F
sum.print=F
pb=T
                              
  # Observed Data
  sampleData <- fit@Data@X[[1]]
  # sample covariance matrix
  sampleCov <- fit@SampleStats@cov[[1]]
  
  # extract model
  extractedLavaan <- lavMatrixRepresentation(partable(fit))
  
  factNames <- unique(extractedLavaan[extractedLavaan[,"mat"]=="lambda", "lhs"])
  varNames <- unique(extractedLavaan[extractedLavaan[,"mat"]=="lambda", "rhs"])
  # extract factor loading matrix
  lambda <- extractedLavaan[ extractedLavaan$mat == "lambda" ,]
  lambda <- convert2matrix(lambda$row, lambda$col, lambda$est)
  colnames(lambda) <- factNames
  rownames(lambda) <- varNames
  # extract factor covariance matrix
  phi <- extractedLavaan[ extractedLavaan$mat == "psi" ,]
  phi <- convert2matrix(phi[,'row'], phi[,'col'], phi[,'est'])
  phi <- up2full(phi)
  colnames(phi) <- rownames(phi) <- factNames
  # extract error covariance matrix
  psi <- extractedLavaan[ extractedLavaan$mat == "theta" ,]
  psi <- convert2matrix(psi[,'row'], psi[,'col'], psi[,'est'])
  psi[upper.tri(psi)] <- 0
  colnames(psi) <- rownames(psi) <- varNames
  
  
  # need to create list of all NA parameters in the above matrices
  
  if(all.parameters == T){
    lambdaA <- lambda
    phiA <- phi
    psiA <- psi
    
    lambdaA[!is.na(lambdaA)] <- NA
    phiA[!is.na(phiA)] <- NA
    psiA[!is.na(psiA)] <- NA
    
  } else{
    lambdaA <- lambda
    phiA <- phi
    psiA <- psi
    
  }
  
  lamList <- as.matrix(which(is.na(lambdaA), arr.ind = T))
  il <- nrow(lamList)
  phiList <- as.matrix(which(is.na(phiA), arr.ind = T))
  ip <- il + nrow(phiList)
  psiList <- as.matrix(which(is.na(psiA), arr.ind = T))
  it <- ip + nrow(psiList)
  modList <- rbind(lamList, phiList, psiList)
  # number of variables
  # create names for each condition
  vnlamList <- lamList
  vnlamList[,2] <- paste0(factor(vnlamList[,2], levels = order(unique(vnlamList[,2])),labels=factNames))
  vnlamList[,1] <- rownames(lamList)
  vnlamList[,2] <- paste0(vnlamList[,2],"=~",vnlamList[,1])
  vnphiList <- phiList
  if(nrow(phiList)>0){
    vnphiList[,1] <- paste0(factor(phiList[,1], levels = order(unique(vnphiList[,1])),labels=factNames))
    vnphiList[,2] <- paste0(factor(phiList[,2], levels = order(unique(phiList[,2])),labels=factNames))
  }
  vnpsiList <- psiList
  vnpsiList[,1] <- rownames(psiList)
  vnpsiList[,2] <- paste0(vnpsiList[,1],"~~y", psiList[,2])
  nameList <- rbind(vnlamList, vnphiList, vnpsiList)
  # ========================================================== #
  # ========================================================== #
  # iterate around this function
  fitResults <- matrix(nrow=opt[[2]], ncol=it)
  # progress bar
  if(pb==T) progress_bar <- txtProgressBar(min = 0, max = it, style = 3)


  |                                                                            
  |                                                                      |   0%

  iter <- 1
  for(iter in 1:it){
    
    # extract iteration information from modList
    x <- modList[iter, ]
    
    # do we need to update lambda?
    if(iter <= il){
      Q <- lambda
      Q[is.na(Q)] <- 0
      Q[x[1], x[2]] <- NA
      lambdaMod <- Q
    } else {
      Q <- lambda
      Q[is.na(Q)] <- 0
      lambdaMod <- Q
    }
    
    # update phi?
    if(iter > il & iter <= ip){
      Q <- phi
      Q[is.na(Q)] <- 0
      Q[x[1], x[2]] <- NA
      phiMod <- Q
    } else {
      Q <- phi
      Q[is.na(Q)] <- 0
      phiMod <- Q
    }
    
    # update psi?
    if(iter > ip){
      Q <- psi
      Q[is.na(Q)] <- 0
      Q[x[1], x[2]] <- NA
      psiMod <- Q
    } else {
      Q <- psi
      Q[is.na(Q)] <- 0
      psiMod <- Q
    }
    
    # combine into a single list
    cfaModel <- list(lambdaMod, phiMod, psiMod) #, tauMod, etaMod
    
    #print(cfaModel)
    # get starting values
    inits <- get_starting_values(cfaModel) 
    
    # use optim() to run simulation
    fit <- optim(inits, get_log_post, control = list(fnscale = -1),
                 hessian = TRUE,
                 sample.data=sampleData, cfa.model=cfaModel)
    param_mean <- fit$par # numerical deriv
    # compute hess at param_mean
    #hess <- numDeriv::hessian(model, param_mean, ...)
    #param_cov_mat <- solve(-hess)
    param_cov_mat <- solve(-fit$hessian)
    
    # scaled covariance matrix (artifically inflate uncertainty)
    scale.cov = opt[[1]]
    A <- diag(scale.cov, nrow=nrow(param_cov_mat), ncol=ncol(param_cov_mat))
    param_cov_mat <- A%*%param_cov_mat%*%t(A)
    
    # sample
    no.samples=opt[[2]]
    fitResults[,iter] <- mcmc(rmvnorm(no.samples, param_mean, param_cov_mat))
    
    if(pb == T) setTxtProgressBar(progress_bar, iter)
  }

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |                                                                      |   1%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=                                                                     |   1%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=                                                                     |   2%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==                                                                    |   2%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==                                                                    |   3%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==                                                                    |   4%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===                                                                   |   4%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===                                                                   |   5%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====                                                                  |   5%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====                                                                  |   6%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====                                                                 |   6%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====                                                                 |   7%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====                                                                 |   8%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======                                                                |   8%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======                                                                |   9%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======                                                               |   9%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======                                                               |  10%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======                                                               |  11%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========                                                              |  11%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========                                                              |  12%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========                                                             |  12%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========                                                             |  13%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========                                                            |  14%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========                                                            |  15%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========                                                           |  15%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========                                                           |  16%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============                                                          |  17%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============                                                          |  18%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============                                                         |  18%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============                                                         |  19%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============                                                        |  19%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============                                                        |  20%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============                                                        |  21%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============                                                       |  21%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============                                                       |  22%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================                                                      |  22%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================                                                      |  23%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================                                                      |  24%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================                                                     |  24%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================                                                     |  25%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================                                                    |  25%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================                                                    |  26%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================                                                   |  26%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================                                                   |  27%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================                                                   |  28%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================                                                  |  28%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================                                                  |  29%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====================                                                 |  29%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====================                                                 |  30%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====================                                                 |  31%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================                                                |  31%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================                                                |  32%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======================                                               |  32%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======================                                               |  33%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======================                                               |  34%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========================                                              |  34%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========================                                              |  35%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========================                                             |  35%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========================                                             |  36%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================                                            |  36%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================                                            |  37%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================                                            |  38%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========================                                           |  38%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========================                                           |  39%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================                                          |  39%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================                                          |  40%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================                                          |  41%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============================                                         |  41%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============================                                         |  42%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============================                                        |  42%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============================                                        |  43%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================                                       |  44%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================                                       |  45%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================================                                      |  45%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================================                                      |  46%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================                                     |  47%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================                                     |  48%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================================                                    |  48%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================================                                    |  49%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================                                   |  49%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================                                   |  50%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================                                   |  51%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================================                                  |  51%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================================                                  |  52%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====================================                                 |  52%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====================================                                 |  53%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================================                                |  54%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================================                                |  55%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======================================                               |  55%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======================================                               |  56%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========================================                              |  57%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========================================                              |  58%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========================================                             |  58%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========================================                             |  59%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================================                            |  59%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================================                            |  60%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================================                            |  61%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========================================                           |  61%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========================================                           |  62%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================================                          |  62%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================================                          |  63%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================================                          |  64%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============================================                         |  64%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============================================                         |  65%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============================================                        |  65%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============================================                        |  66%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================================                       |  66%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================================                       |  67%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================================                       |  68%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================================================                      |  68%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================================================                      |  69%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================================                     |  69%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================================                     |  70%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================================                     |  71%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================================================                    |  71%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================================================                    |  72%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================================                   |  72%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================================                   |  73%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================================                   |  74%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================================================                  |  74%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================================================                  |  75%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====================================================                 |  75%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=====================================================                 |  76%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================================================                |  76%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================================================                |  77%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================================================                |  78%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======================================================               |  78%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=======================================================               |  79%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========================================================              |  79%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========================================================              |  80%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |========================================================              |  81%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========================================================             |  81%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=========================================================             |  82%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================================================            |  82%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==========================================================            |  83%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========================================================           |  84%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===========================================================           |  85%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================================================          |  85%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |============================================================          |  86%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============================================================         |  87%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=============================================================         |  88%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============================================================        |  88%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==============================================================        |  89%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================================================       |  89%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================================================       |  90%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===============================================================       |  91%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================================================================      |  91%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |================================================================      |  92%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================================================     |  92%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================================================     |  93%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |=================================================================     |  94%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================================================================    |  94%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |==================================================================    |  95%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================================================   |  95%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================================================   |  96%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================================================================  |  96%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================================================================  |  97%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |====================================================================  |  98%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================================================== |  98%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |===================================================================== |  99%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================================================================|  99%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly


  |                                                                            
  |======================================================================| 100%

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

Warning in optim(inits, get_log_post, control = list(fnscale = -1), hessian = TRUE, : one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly

  # ========================================================== #
  # ========================================================== #
  
  colnames(fitResults) <- nameList[,2, drop=T]
  
  # Next, standardized (if desired) default
  if(standardize==T){
    # standardize
    obs.var <- extractedLavaan[extractedLavaan[,"mat"]=="theta", ]
    obs.var <- obs.var[which(obs.var$lhs == obs.var$rhs), c("lhs", "est")]
    
    fct.var <- extractedLavaan[extractedLavaan[,"mat"]=="psi", ]
    fct.var <- fct.var[which(fct.var$lhs == fct.var$rhs), c("lhs", "est")]
    
    all.var <- rbind(obs.var, fct.var)
    
    fitResults <- fitResults
    p <- colnames(fitResults)
    i <- 1
    for(i in 1:length(p)){
      unstd <- fitResults[,i]
      
      if(p[i] %like% "=~"){
        pp <- strsplit(p[i], "=~") %>% unlist()
        sigjj <- sqrt(all.var[all.var[,1] == pp[1], 2])
        sigii <- sqrt(all.var[all.var[,1] == pp[2], 2])
        std <- unstd*sqrt(sigjj/sigii) # bollen (1989, p. 349)
      }
      
      if(p[i] %like% "~~"){
        pp <- strsplit(p[i], "~~") %>% unlist()
        sigjj <- sqrt(all.var[all.var[,1] == pp[1], 2])
        sigii <- sqrt(all.var[all.var[,1] == pp[2], 2])
        std <- unstd/(sigjj * sigii) # bollen (1989, p. 349)
      }
      
      fitResults[,i] <- std
    }
  }
  # now, compute and format summary statistics
  sumResults <- data.frame(matrix(nrow=ncol(fitResults), ncol=9))
  colnames(sumResults) <- c("Parameter","Prob", "mean", "sd", "p0.025", "p0.25", "p0.5", "p0.75", "p0.975")
  sumResults[,1] <- colnames(fitResults)
  
  sumResults[,3:9] <- t(apply(fitResults, 2, function(x){
    c(mean(x, na.rm=T), sd(x, na.rm=T),
      quantile(x, c(0.025, 0.25, 0.5, 0.75, 0.975), na.rm=T))
  }))
  
  # compute probability of meaningfulness
  # depends on parameter
  # cut.load = 0.3
  # cut.cov = 0.1
  p <- colnames(fitResults)
  for(i in 1:ncol(fitResults)){
    x <- fitResults[,i, drop=T]
    if(p[i] %like% "=~"){
      pv <- mean(ifelse(abs(x) >= cut.load, 1, 0))
    }
    if(p[i] %like% "~~"){
      pv <- mean(ifelse(abs(x) >= cut.cov, 1, 0))
    }
    sumResults[i, 2] <- pv
  }
  sumResults <- arrange(sumResults, desc(Prob))
  colnames(sumResults) <- c("Parameter","Pr(|theta|>cutoff)", "mean", "sd", "p0.025", "p0.25", "p0.5", "p0.75", "p0.975")
  sumResults[,2:9] <- round(sumResults[,2:9], 3)
  cat("\n")

  if(sum.print==T) print(sumResults, row.names = FALSE)
  
  # convert to data.frame
  fitResults <- as.data.frame(fitResults)
  out <- list(fitResults, sumResults)
  names(out) <- c("All Results", "Summary")
  
#  return(out)
#}



#out.lplf <- laplace_local_fit(fit = fit1, standardize = T, opt=list(scale.cov=1, no.samples=10))


kable(sumResults, format="html", digits=3)%>%
  kable_styling(full_width = T)%>%
  scroll_box(width="100%", height="600px")

Parameter	Pr(\|theta\|>cutoff)	mean	sd	p0.025	p0.25	p0.5	p0.75	p0.975
y18~~y14	1.000	-0.445	0.044	-0.537	-0.473	-0.446	-0.416	-0.362
y18~~y17	1.000	0.566	0.027	0.514	0.546	0.567	0.584	0.620
y22~~y17	1.000	-0.377	0.036	-0.450	-0.401	-0.375	-0.353	-0.304
y27~~y17	1.000	-0.381	0.040	-0.455	-0.409	-0.381	-0.354	-0.301
y34~~y17	1.000	0.444	0.035	0.380	0.422	0.442	0.468	0.512
y38~~y17	1.000	0.419	0.037	0.347	0.393	0.418	0.443	0.496
y27~~y18	1.000	-0.415	0.039	-0.492	-0.442	-0.414	-0.389	-0.339
y34~~y18	1.000	0.375	0.036	0.305	0.350	0.375	0.401	0.447
y36~~y18	1.000	0.392	0.038	0.317	0.367	0.393	0.417	0.463
y38~~y18	1.000	0.463	0.037	0.391	0.437	0.463	0.487	0.535
y30~~y22	1.000	-0.418	0.038	-0.491	-0.443	-0.419	-0.394	-0.343
y34~~y22	1.000	-0.451	0.033	-0.515	-0.473	-0.453	-0.429	-0.387
y38~~y22	1.000	-0.396	0.035	-0.463	-0.418	-0.397	-0.373	-0.329
y34~~y27	1.000	-0.410	0.036	-0.484	-0.432	-0.410	-0.388	-0.340
y34~~y30	1.000	0.373	0.035	0.305	0.350	0.373	0.398	0.440
y38~~y30	1.000	0.369	0.036	0.298	0.346	0.370	0.393	0.443
y36~~y34	1.000	0.374	0.033	0.313	0.351	0.374	0.396	0.438
y38~~y34	1.000	0.364	0.032	0.304	0.341	0.364	0.385	0.426
y37~~y36	1.000	0.501	0.033	0.437	0.478	0.500	0.523	0.570
y38~~y36	1.000	0.478	0.029	0.419	0.458	0.478	0.497	0.534
y17~~y14	0.999	-0.404	0.045	-0.491	-0.435	-0.404	-0.374	-0.314
y38~~y27	0.999	-0.362	0.036	-0.432	-0.387	-0.362	-0.336	-0.293
y22~~y18	0.998	-0.351	0.037	-0.429	-0.376	-0.351	-0.329	-0.278
y36~~y27	0.997	-0.360	0.039	-0.440	-0.386	-0.360	-0.333	-0.286
y38~~y37	0.997	0.350	0.035	0.279	0.326	0.349	0.373	0.419
y22~~y16	0.996	0.349	0.037	0.272	0.325	0.348	0.372	0.419
y18~~y16	0.994	-0.366	0.047	-0.455	-0.399	-0.367	-0.335	-0.274
y38~~y31	0.988	-0.352	0.047	-0.446	-0.381	-0.351	-0.321	-0.263
y36~~y17	0.987	0.350	0.041	0.269	0.324	0.352	0.377	0.426
y37~~y18	0.986	0.335	0.040	0.260	0.308	0.335	0.362	0.415
y38~~y14	0.980	-0.332	0.040	-0.412	-0.359	-0.332	-0.307	-0.255
y27~~y22	0.979	0.315	0.031	0.253	0.294	0.314	0.335	0.377
y36~~y31	0.972	-0.344	0.049	-0.438	-0.376	-0.344	-0.313	-0.248
y31~~y27	0.970	0.331	0.043	0.248	0.302	0.331	0.359	0.418
y28~~y23	0.942	-0.364	0.069	-0.493	-0.412	-0.368	-0.319	-0.224
y37~~y34	0.942	0.307	0.035	0.241	0.284	0.307	0.332	0.376
y6~~y5	0.935	0.319	0.045	0.231	0.288	0.318	0.349	0.404
y22~~y14	0.933	0.304	0.036	0.229	0.281	0.304	0.329	0.370
y25~~y23	0.930	0.322	0.048	0.232	0.288	0.323	0.356	0.412
y11~~y10	0.924	0.320	0.049	0.217	0.290	0.319	0.354	0.410
y17~~y16	0.924	-0.320	0.049	-0.409	-0.353	-0.322	-0.288	-0.222
y34~~y14	0.918	-0.304	0.039	-0.382	-0.329	-0.304	-0.279	-0.227
y27~~y12	0.907	0.305	0.040	0.231	0.277	0.306	0.333	0.382
y16~~y14	0.906	0.302	0.040	0.221	0.277	0.301	0.328	0.373
y37~~y17	0.862	0.296	0.042	0.221	0.267	0.295	0.325	0.376
y34~~y31	0.857	-0.299	0.046	-0.385	-0.332	-0.300	-0.269	-0.208
y27~~y14	0.856	0.291	0.040	0.208	0.265	0.291	0.317	0.366
y18~~y2	0.849	-0.297	0.045	-0.381	-0.329	-0.298	-0.267	-0.207
y30~~y17	0.839	0.293	0.044	0.207	0.265	0.292	0.321	0.377
y34~~y2	0.838	-0.291	0.041	-0.372	-0.319	-0.291	-0.263	-0.216
y4~~y3	0.837	0.297	0.047	0.205	0.266	0.299	0.329	0.382
y36~~y30	0.832	0.291	0.043	0.209	0.262	0.292	0.318	0.374
y34~~y16	0.831	-0.290	0.040	-0.370	-0.315	-0.290	-0.263	-0.212
IN=~y34	0.829	-0.639	0.041	-0.721	-0.667	-0.639	-0.612	-0.562
y37~~y5	0.828	-0.291	0.045	-0.380	-0.320	-0.292	-0.260	-0.202
y37~~y27	0.821	-0.287	0.040	-0.361	-0.314	-0.289	-0.260	-0.205
y22~~y4	0.818	-0.286	0.040	-0.360	-0.313	-0.286	-0.259	-0.209
y31~~y17	0.806	-0.293	0.048	-0.392	-0.326	-0.293	-0.259	-0.203
y30~~y18	0.792	0.285	0.043	0.206	0.255	0.284	0.314	0.368
y38~~y12	0.745	-0.277	0.039	-0.353	-0.305	-0.278	-0.249	-0.203
y30~~y4	0.736	0.280	0.048	0.186	0.248	0.280	0.312	0.372
y36~~y22	0.720	-0.272	0.038	-0.346	-0.295	-0.272	-0.247	-0.202
y37~~y31	0.690	-0.273	0.047	-0.358	-0.306	-0.276	-0.241	-0.180
y14~~y2	0.678	0.272	0.045	0.186	0.242	0.270	0.304	0.360
y28~~y21	0.678	0.277	0.058	0.166	0.239	0.276	0.316	0.389
y36~~y16	0.676	-0.271	0.044	-0.360	-0.301	-0.271	-0.242	-0.186
y38~~y16	0.665	-0.268	0.042	-0.349	-0.295	-0.269	-0.239	-0.185
y34~~y6	0.664	-0.268	0.040	-0.348	-0.294	-0.268	-0.240	-0.188
y28~~y25	0.650	-0.278	0.070	-0.410	-0.326	-0.277	-0.230	-0.146
y21~~y17	0.647	-0.267	0.049	-0.365	-0.299	-0.265	-0.235	-0.169
y26~~y25	0.639	-0.271	0.057	-0.376	-0.310	-0.271	-0.232	-0.163
y21~~y18	0.625	-0.265	0.051	-0.372	-0.299	-0.263	-0.232	-0.166
y3~~y1	0.607	0.264	0.049	0.169	0.231	0.263	0.296	0.364
y12~~y4	0.603	-0.265	0.052	-0.367	-0.300	-0.264	-0.229	-0.164
y20~~y19	0.589	0.262	0.050	0.164	0.228	0.260	0.296	0.359
y36~~y5	0.587	-0.259	0.047	-0.354	-0.290	-0.260	-0.227	-0.169
EL=~y34	0.579	-0.610	0.046	-0.702	-0.640	-0.609	-0.580	-0.526
y30~~y16	0.577	-0.257	0.044	-0.339	-0.288	-0.258	-0.228	-0.168
y36~~y19	0.577	-0.260	0.050	-0.365	-0.291	-0.261	-0.226	-0.161
y17~~y6	0.562	-0.256	0.045	-0.347	-0.286	-0.255	-0.227	-0.168
y17~~y2	0.543	-0.256	0.044	-0.347	-0.283	-0.256	-0.226	-0.173
y35~~y19	0.542	0.254	0.055	0.151	0.218	0.256	0.292	0.368
y30~~y6	0.534	-0.254	0.047	-0.347	-0.284	-0.254	-0.223	-0.158
y34~~y13	0.530	-0.254	0.044	-0.341	-0.282	-0.253	-0.225	-0.168
y36~~y12	0.518	-0.252	0.043	-0.342	-0.279	-0.252	-0.223	-0.171
y22~~y2	0.511	0.251	0.039	0.179	0.223	0.252	0.277	0.324
y38~~y2	0.507	-0.252	0.042	-0.336	-0.280	-0.251	-0.225	-0.171
y37~~y32	0.501	-0.251	0.049	-0.341	-0.285	-0.250	-0.217	-0.156
y37~~y16	0.494	-0.251	0.044	-0.337	-0.282	-0.249	-0.220	-0.167
y36~~y32	0.491	-0.248	0.053	-0.348	-0.283	-0.249	-0.213	-0.140
y12~~y3	0.485	-0.244	0.054	-0.351	-0.280	-0.247	-0.205	-0.138
y14~~y4	0.483	-0.247	0.049	-0.344	-0.281	-0.247	-0.213	-0.156
y19~~y17	0.482	-0.247	0.056	-0.359	-0.283	-0.247	-0.209	-0.136
y27~~y6	0.479	0.248	0.041	0.168	0.221	0.248	0.273	0.330
y18~~y12	0.478	-0.248	0.044	-0.334	-0.280	-0.248	-0.220	-0.159
y6~~y3	0.475	-0.248	0.056	-0.356	-0.285	-0.246	-0.210	-0.143
y13~~y2	0.465	0.245	0.046	0.157	0.214	0.246	0.277	0.336
y30~~y2	0.458	-0.245	0.047	-0.342	-0.276	-0.244	-0.212	-0.156
y36~~y6	0.452	-0.244	0.047	-0.335	-0.276	-0.244	-0.212	-0.150
y30~~y27	0.447	-0.243	0.042	-0.326	-0.271	-0.244	-0.215	-0.159
y31~~y12	0.436	0.242	0.048	0.146	0.210	0.242	0.273	0.336
y10~~y4	0.425	-0.240	0.058	-0.353	-0.278	-0.239	-0.200	-0.131
y27~~y2	0.391	0.242	0.041	0.165	0.215	0.240	0.268	0.327
y37~~y19	0.381	-0.235	0.051	-0.334	-0.270	-0.234	-0.200	-0.139
y35~~y15	0.377	-0.231	0.059	-0.347	-0.268	-0.234	-0.192	-0.110
y7~~y3	0.368	-0.231	0.060	-0.348	-0.273	-0.231	-0.191	-0.115
y38~~y6	0.364	-0.234	0.043	-0.316	-0.263	-0.234	-0.205	-0.149
y35~~y20	0.364	0.229	0.058	0.121	0.190	0.229	0.266	0.344
y18~~y4	0.359	0.234	0.045	0.147	0.202	0.234	0.264	0.325
y31~~y22	0.347	0.234	0.040	0.153	0.207	0.234	0.261	0.313
y23~~y22	0.343	0.234	0.041	0.154	0.208	0.235	0.260	0.316
y8~~y2	0.334	-0.225	0.055	-0.332	-0.260	-0.224	-0.188	-0.114
y30~~y8	0.333	0.228	0.049	0.129	0.196	0.230	0.261	0.320
y13~~y12	0.326	0.230	0.046	0.144	0.200	0.229	0.261	0.321
y33~~y3	0.323	-0.220	0.059	-0.330	-0.263	-0.221	-0.177	-0.104
y34~~y12	0.314	-0.232	0.039	-0.311	-0.258	-0.232	-0.206	-0.155
y5~~y1	0.309	-0.217	0.062	-0.338	-0.260	-0.217	-0.174	-0.094
y20~~y5	0.300	0.222	0.057	0.112	0.184	0.222	0.258	0.333
y37~~y20	0.294	-0.224	0.050	-0.319	-0.257	-0.225	-0.193	-0.120
y36~~y2	0.284	-0.223	0.046	-0.311	-0.255	-0.224	-0.191	-0.134
y33~~y29	0.278	0.212	0.061	0.090	0.170	0.213	0.255	0.329
y37~~y12	0.277	-0.222	0.045	-0.308	-0.254	-0.223	-0.192	-0.137
y36~~y20	0.273	-0.216	0.051	-0.308	-0.254	-0.219	-0.181	-0.116
y38~~y8	0.269	0.223	0.043	0.134	0.195	0.226	0.253	0.304
y22~~y12	0.266	0.225	0.040	0.144	0.199	0.225	0.252	0.300
y18~~y5	0.256	-0.221	0.047	-0.318	-0.251	-0.220	-0.191	-0.128
y6~~y1	0.245	-0.214	0.055	-0.330	-0.249	-0.215	-0.178	-0.107
y16~~y2	0.242	0.218	0.046	0.131	0.186	0.217	0.248	0.305
y15~~y14	0.240	0.218	0.046	0.131	0.187	0.216	0.248	0.310
y31~~y14	0.238	0.216	0.046	0.126	0.183	0.217	0.248	0.300
y37~~y6	0.237	-0.215	0.047	-0.304	-0.249	-0.215	-0.185	-0.118
y16~~y15	0.232	0.215	0.047	0.119	0.182	0.214	0.246	0.306
y31~~y18	0.232	-0.217	0.048	-0.315	-0.247	-0.215	-0.186	-0.128
y21~~y11	0.230	0.209	0.054	0.106	0.173	0.208	0.247	0.308
y35~~y17	0.215	-0.209	0.051	-0.308	-0.244	-0.209	-0.175	-0.104
y18~~y6	0.210	-0.212	0.046	-0.304	-0.243	-0.212	-0.182	-0.119
y35~~y6	0.209	0.205	0.054	0.102	0.168	0.205	0.241	0.309
y33~~y9	0.209	0.204	0.057	0.097	0.164	0.204	0.242	0.321
y30~~y12	0.205	-0.213	0.045	-0.303	-0.243	-0.212	-0.185	-0.128
y25~~y9	0.193	-0.200	0.058	-0.314	-0.239	-0.200	-0.161	-0.088
y35~~y18	0.188	-0.206	0.050	-0.301	-0.239	-0.205	-0.172	-0.107
y5~~y3	0.187	-0.197	0.060	-0.320	-0.238	-0.197	-0.158	-0.079
y37~~y3	0.186	0.206	0.049	0.108	0.174	0.208	0.240	0.302
y16~~y4	0.184	-0.205	0.049	-0.305	-0.239	-0.203	-0.171	-0.114
y34~~y4	0.183	0.212	0.043	0.127	0.184	0.212	0.239	0.296
y16~~y8	0.181	-0.203	0.051	-0.307	-0.238	-0.203	-0.169	-0.107
y33~~y32	0.181	0.202	0.054	0.088	0.167	0.204	0.238	0.301
y11~~y4	0.179	-0.198	0.056	-0.306	-0.238	-0.198	-0.160	-0.090
y17~~y13	0.179	-0.206	0.050	-0.305	-0.238	-0.205	-0.175	-0.103
EL=~y17	0.175	-0.555	0.046	-0.642	-0.586	-0.554	-0.521	-0.469
y36~~y3	0.172	0.203	0.050	0.103	0.170	0.204	0.238	0.295
y30~~y14	0.167	-0.208	0.043	-0.297	-0.237	-0.208	-0.180	-0.126
y16~~y5	0.156	0.198	0.050	0.093	0.167	0.198	0.231	0.290
y28~~y26	0.154	0.192	0.058	0.080	0.152	0.192	0.227	0.309
y38~~y19	0.152	-0.203	0.046	-0.290	-0.235	-0.203	-0.171	-0.113
y12~~y11	0.149	0.201	0.047	0.111	0.169	0.202	0.231	0.289
y20~~y15	0.149	-0.183	0.064	-0.304	-0.223	-0.182	-0.141	-0.059
y33~~y14	0.147	0.196	0.052	0.098	0.161	0.195	0.232	0.299
IN=~y38	0.144	-0.556	0.043	-0.642	-0.583	-0.555	-0.529	-0.466
y14~~y8	0.139	-0.196	0.049	-0.292	-0.230	-0.196	-0.163	-0.103
y36~~y33	0.132	-0.192	0.054	-0.304	-0.227	-0.190	-0.156	-0.091
y30~~y13	0.131	-0.194	0.049	-0.291	-0.227	-0.194	-0.161	-0.099
y17~~y4	0.128	0.197	0.047	0.102	0.165	0.197	0.230	0.286
y31~~y7	0.126	0.186	0.053	0.086	0.150	0.186	0.221	0.293
y16~~y6	0.124	0.190	0.050	0.089	0.158	0.191	0.224	0.285
y38~~y33	0.124	-0.191	0.051	-0.293	-0.225	-0.188	-0.155	-0.094
y20~~y6	0.121	0.188	0.053	0.080	0.151	0.190	0.224	0.291
y31~~y16	0.120	0.191	0.049	0.094	0.160	0.189	0.224	0.288
y32~~y29	0.120	0.177	0.060	0.066	0.137	0.176	0.216	0.301
y21~~y4	0.116	-0.185	0.054	-0.289	-0.220	-0.185	-0.150	-0.074
y37~~y33	0.115	-0.190	0.052	-0.289	-0.225	-0.193	-0.156	-0.082
y36~~y14	0.113	-0.200	0.041	-0.283	-0.229	-0.200	-0.173	-0.119
y13~~y8	0.111	-0.174	0.062	-0.290	-0.216	-0.176	-0.132	-0.049
y7~~y1	0.110	-0.175	0.062	-0.296	-0.218	-0.174	-0.131	-0.059
y10~~y9	0.107	-0.179	0.058	-0.295	-0.218	-0.182	-0.140	-0.065
y19~~y2	0.104	0.185	0.053	0.079	0.148	0.185	0.220	0.293
y34~~y9	0.095	0.198	0.041	0.121	0.172	0.198	0.225	0.281
y38~~y32	0.094	-0.189	0.045	-0.276	-0.220	-0.189	-0.156	-0.103
y17~~y12	0.091	-0.191	0.044	-0.277	-0.222	-0.192	-0.162	-0.105
y37~~y14	0.091	-0.191	0.044	-0.275	-0.221	-0.188	-0.161	-0.109
y30~~y24	0.091	-0.178	0.054	-0.284	-0.215	-0.177	-0.141	-0.071
y17~~y5	0.089	-0.186	0.049	-0.278	-0.220	-0.185	-0.152	-0.089
y24~~y23	0.089	0.176	0.054	0.080	0.138	0.174	0.212	0.283
y13~~y9	0.086	-0.167	0.059	-0.286	-0.205	-0.166	-0.128	-0.055
y8~~y4	0.085	0.178	0.051	0.081	0.144	0.175	0.212	0.279
y27~~y16	0.085	0.186	0.043	0.100	0.157	0.185	0.215	0.269
IN=~y18	0.083	-0.545	0.040	-0.625	-0.572	-0.547	-0.517	-0.469
y33~~y18	0.083	-0.177	0.049	-0.276	-0.209	-0.175	-0.146	-0.084
y32~~y31	0.083	0.182	0.050	0.084	0.150	0.185	0.214	0.282
y21~~y10	0.081	0.173	0.054	0.074	0.135	0.172	0.210	0.274
y28~~y3	0.079	-0.165	0.059	-0.280	-0.205	-0.166	-0.126	-0.050
y33~~y28	0.079	0.162	0.062	0.041	0.119	0.159	0.205	0.281
y14~~y6	0.078	0.181	0.047	0.089	0.149	0.181	0.212	0.272
y38~~y13	0.078	-0.184	0.046	-0.275	-0.214	-0.182	-0.154	-0.095
y31~~y6	0.075	0.178	0.052	0.074	0.143	0.178	0.214	0.278
y31~~y30	0.071	-0.176	0.048	-0.270	-0.209	-0.174	-0.142	-0.086
y13~~y4	0.070	-0.162	0.059	-0.278	-0.198	-0.163	-0.121	-0.052
y33~~y17	0.066	-0.173	0.053	-0.277	-0.208	-0.174	-0.138	-0.067
y35~~y25	0.066	-0.158	0.063	-0.281	-0.201	-0.156	-0.115	-0.033
EL=~y18	0.065	-0.534	0.044	-0.627	-0.563	-0.535	-0.505	-0.445
y7~~y6	0.062	0.175	0.049	0.079	0.142	0.175	0.207	0.277
y37~~y13	0.062	-0.176	0.050	-0.273	-0.210	-0.177	-0.142	-0.080
y19~~y18	0.062	-0.167	0.056	-0.275	-0.206	-0.170	-0.128	-0.060
y27~~y19	0.062	0.184	0.046	0.085	0.155	0.184	0.214	0.271
y32~~y19	0.060	0.156	0.056	0.045	0.119	0.155	0.191	0.269
y37~~y30	0.057	0.184	0.042	0.100	0.155	0.183	0.212	0.267
y20~~y18	0.056	-0.164	0.053	-0.269	-0.202	-0.162	-0.127	-0.066
y32~~y23	0.054	0.154	0.058	0.039	0.115	0.153	0.192	0.266
EL=~y38	0.053	-0.526	0.047	-0.615	-0.558	-0.525	-0.495	-0.435
y6~~y2	0.053	0.170	0.048	0.074	0.137	0.172	0.204	0.265
y19~~y5	0.053	0.155	0.058	0.039	0.117	0.156	0.194	0.273
y34~~y8	0.053	0.184	0.043	0.097	0.155	0.184	0.213	0.264
y16~~y10	0.051	0.165	0.051	0.065	0.131	0.165	0.199	0.260
y34~~y33	0.051	-0.169	0.046	-0.262	-0.200	-0.168	-0.137	-0.086
y24~~y22	0.050	0.180	0.041	0.102	0.151	0.180	0.206	0.259
y28~~y13	0.049	0.147	0.062	0.028	0.106	0.146	0.189	0.267
y38~~y4	0.045	0.176	0.044	0.092	0.147	0.175	0.207	0.262
y35~~y21	0.044	0.152	0.058	0.037	0.111	0.150	0.193	0.269
IN=~y17	0.042	-0.529	0.041	-0.611	-0.558	-0.530	-0.502	-0.453
y28~~y4	0.042	-0.142	0.061	-0.264	-0.182	-0.139	-0.098	-0.027
y22~~y6	0.042	0.180	0.039	0.101	0.154	0.179	0.206	0.258
y29~~y9	0.040	0.147	0.058	0.035	0.106	0.147	0.188	0.265
y18~~y13	0.039	-0.167	0.047	-0.261	-0.198	-0.167	-0.136	-0.071
y25~~y21	0.038	-0.143	0.060	-0.259	-0.180	-0.145	-0.102	-0.031
y36~~y35	0.036	-0.149	0.054	-0.253	-0.184	-0.148	-0.114	-0.047
y9~~y2	0.035	-0.150	0.054	-0.260	-0.185	-0.151	-0.113	-0.043
y12~~y5	0.034	0.158	0.051	0.058	0.125	0.157	0.193	0.256
y23~~y5	0.034	0.152	0.057	0.043	0.111	0.153	0.189	0.262
y17~~y8	0.034	0.165	0.049	0.073	0.130	0.166	0.199	0.260
y30~~y23	0.034	-0.151	0.054	-0.252	-0.189	-0.150	-0.113	-0.047
y33~~y30	0.034	-0.149	0.054	-0.253	-0.184	-0.149	-0.110	-0.043
y18~~y8	0.033	0.160	0.050	0.061	0.127	0.160	0.193	0.256
y13~~y10	0.033	0.151	0.055	0.041	0.115	0.154	0.189	0.256
y31~~y5	0.032	0.157	0.053	0.052	0.121	0.158	0.195	0.255
y31~~y15	0.030	0.151	0.051	0.050	0.118	0.153	0.186	0.254
y29~~y25	0.030	0.139	0.060	0.026	0.098	0.140	0.180	0.253
y35~~y33	0.030	0.139	0.057	0.030	0.099	0.136	0.178	0.257
y35~~y23	0.029	-0.126	0.064	-0.252	-0.169	-0.128	-0.083	0.001
y28~~y15	0.028	-0.127	0.062	-0.256	-0.169	-0.127	-0.086	0.001
y12~~y8	0.027	-0.149	0.053	-0.251	-0.186	-0.148	-0.112	-0.049
y28~~y9	0.027	0.130	0.060	0.015	0.088	0.128	0.170	0.255
y21~~y20	0.027	0.154	0.052	0.054	0.118	0.155	0.189	0.252
y7~~y2	0.026	-0.139	0.056	-0.250	-0.175	-0.139	-0.102	-0.027
y33~~y27	0.026	0.167	0.044	0.081	0.138	0.166	0.197	0.250
y31~~y13	0.025	0.145	0.055	0.040	0.106	0.145	0.185	0.250
y34~~y19	0.025	-0.167	0.044	-0.250	-0.195	-0.165	-0.136	-0.081
y21~~y12	0.024	0.152	0.050	0.054	0.119	0.153	0.187	0.246
y23~~y16	0.024	0.140	0.055	0.029	0.102	0.142	0.176	0.248
y30~~y3	0.023	0.145	0.051	0.047	0.108	0.144	0.180	0.248
y29~~y7	0.023	-0.122	0.062	-0.248	-0.162	-0.120	-0.079	-0.003
y37~~y22	0.023	-0.174	0.040	-0.250	-0.201	-0.173	-0.145	-0.098
y37~~y35	0.023	-0.143	0.054	-0.249	-0.181	-0.142	-0.109	-0.038
y19~~y13	0.022	0.134	0.059	0.017	0.093	0.137	0.175	0.246
y33~~y1	0.021	-0.119	0.062	-0.244	-0.159	-0.118	-0.077	0.008
y6~~y4	0.021	-0.143	0.054	-0.245	-0.180	-0.144	-0.107	-0.037
y33~~y6	0.020	0.131	0.058	0.013	0.092	0.132	0.169	0.238
y29~~y24	0.019	-0.116	0.068	-0.242	-0.163	-0.117	-0.069	0.018
y2~~y1	0.018	0.142	0.051	0.044	0.108	0.142	0.175	0.244
y33~~y16	0.018	0.136	0.053	0.031	0.100	0.136	0.171	0.240
y32~~y1	0.017	0.116	0.061	-0.004	0.077	0.117	0.158	0.241
y10~~y7	0.017	0.119	0.056	0.013	0.081	0.116	0.156	0.239
y12~~y10	0.017	0.141	0.050	0.045	0.108	0.140	0.173	0.241
y21~~y13	0.017	0.125	0.058	0.019	0.084	0.123	0.163	0.238
y4~~y2	0.016	-0.135	0.052	-0.238	-0.172	-0.134	-0.098	-0.031
y36~~y13	0.016	-0.152	0.048	-0.243	-0.186	-0.151	-0.119	-0.060
y38~~y35	0.016	-0.139	0.050	-0.239	-0.171	-0.139	-0.108	-0.039
y21~~y1	0.014	-0.118	0.057	-0.226	-0.155	-0.119	-0.081	0.001
y35~~y5	0.014	0.118	0.059	0.006	0.077	0.118	0.158	0.235
y19~~y15	0.014	-0.115	0.061	-0.240	-0.154	-0.116	-0.075	0.008
y19~~y16	0.014	0.134	0.052	0.030	0.099	0.134	0.168	0.239
y28~~y1	0.013	-0.100	0.063	-0.226	-0.142	-0.099	-0.054	0.016
y35~~y3	0.013	-0.117	0.061	-0.236	-0.160	-0.113	-0.076	0.001
y36~~y9	0.013	0.144	0.050	0.045	0.111	0.145	0.179	0.238
y33~~y2	0.012	0.138	0.052	0.035	0.103	0.138	0.174	0.236
y19~~y3	0.012	-0.120	0.060	-0.237	-0.161	-0.122	-0.077	-0.004
y38~~y3	0.012	0.153	0.045	0.058	0.124	0.154	0.185	0.236
y15~~y4	0.012	-0.126	0.058	-0.234	-0.169	-0.125	-0.088	-0.014
y27~~y4	0.012	-0.149	0.046	-0.238	-0.180	-0.148	-0.118	-0.063
y36~~y8	0.012	0.132	0.052	0.033	0.098	0.131	0.167	0.237
y12~~y6	0.011	0.138	0.048	0.045	0.105	0.137	0.170	0.231
y15~~y13	0.011	0.115	0.060	0.000	0.077	0.114	0.158	0.230
y32~~y18	0.011	-0.136	0.050	-0.228	-0.170	-0.137	-0.103	-0.039
y27~~y20	0.011	0.141	0.048	0.048	0.110	0.139	0.171	0.233
y29~~y28	0.011	0.092	0.063	-0.025	0.049	0.090	0.132	0.225
y31~~y3	0.010	-0.127	0.057	-0.235	-0.167	-0.127	-0.086	-0.018
y25~~y15	0.010	0.109	0.061	-0.017	0.067	0.110	0.150	0.225
y26~~y23	0.010	-0.112	0.061	-0.231	-0.153	-0.112	-0.072	0.012
y9~~y7	0.009	0.128	0.053	0.027	0.089	0.126	0.163	0.233
y19~~y8	0.009	-0.110	0.060	-0.226	-0.152	-0.112	-0.070	0.001
y27~~y21	0.009	0.141	0.047	0.049	0.110	0.140	0.172	0.236
y33~~y31	0.009	0.120	0.053	0.019	0.083	0.121	0.156	0.225
EL=~y22	0.008	0.493	0.042	0.410	0.466	0.492	0.520	0.576
y4~~y1	0.008	0.115	0.055	0.004	0.077	0.116	0.153	0.222
y10~~y8	0.008	-0.103	0.059	-0.229	-0.141	-0.104	-0.065	0.015
y22~~y11	0.008	0.151	0.042	0.062	0.124	0.152	0.181	0.227
y33~~y12	0.008	0.121	0.052	0.020	0.085	0.121	0.155	0.224
y25~~y13	0.008	-0.096	0.059	-0.220	-0.134	-0.097	-0.058	0.023
y27~~y13	0.008	0.140	0.046	0.051	0.109	0.140	0.170	0.234
y31~~y26	0.008	-0.115	0.054	-0.220	-0.153	-0.117	-0.079	-0.008
y7~~y4	0.007	-0.119	0.057	-0.228	-0.159	-0.120	-0.078	-0.002
y25~~y5	0.007	-0.096	0.060	-0.216	-0.136	-0.096	-0.054	0.016
y27~~y5	0.007	0.131	0.046	0.038	0.101	0.132	0.161	0.222
y9~~y6	0.007	-0.117	0.056	-0.227	-0.155	-0.116	-0.078	-0.012
y19~~y9	0.007	-0.109	0.059	-0.223	-0.148	-0.107	-0.070	0.006
y14~~y13	0.007	0.128	0.049	0.030	0.098	0.130	0.159	0.224
y32~~y13	0.007	0.110	0.057	-0.009	0.071	0.109	0.150	0.226
y21~~y14	0.007	0.134	0.048	0.044	0.101	0.133	0.167	0.226
y20~~y17	0.007	-0.119	0.055	-0.227	-0.156	-0.120	-0.081	-0.014
y10~~y1	0.006	-0.095	0.060	-0.213	-0.133	-0.097	-0.053	0.025
y10~~y3	0.006	-0.104	0.060	-0.219	-0.146	-0.104	-0.063	0.015
y20~~y3	0.006	-0.098	0.062	-0.222	-0.138	-0.096	-0.055	0.019
y37~~y4	0.006	0.137	0.048	0.041	0.103	0.137	0.169	0.231
y8~~y6	0.006	-0.115	0.054	-0.221	-0.150	-0.115	-0.080	-0.008
y19~~y6	0.006	0.118	0.054	0.016	0.081	0.118	0.155	0.224
y36~~y7	0.006	0.123	0.052	0.021	0.088	0.125	0.158	0.223
y32~~y10	0.006	-0.096	0.058	-0.207	-0.134	-0.094	-0.057	0.015
y35~~y10	0.006	-0.099	0.062	-0.216	-0.140	-0.100	-0.058	0.022
y33~~y19	0.006	0.087	0.063	-0.043	0.048	0.090	0.130	0.203
y37~~y24	0.006	-0.115	0.053	-0.219	-0.149	-0.114	-0.080	-0.016
EL=~y14	0.005	0.485	0.045	0.397	0.456	0.485	0.514	0.571
y20~~y1	0.005	-0.096	0.061	-0.211	-0.137	-0.100	-0.055	0.028
y31~~y1	0.005	-0.101	0.056	-0.214	-0.136	-0.102	-0.064	0.011
y9~~y4	0.005	0.109	0.056	0.001	0.071	0.107	0.147	0.218
y22~~y8	0.005	-0.148	0.041	-0.227	-0.176	-0.148	-0.121	-0.067
y27~~y8	0.005	-0.128	0.045	-0.218	-0.158	-0.128	-0.100	-0.042
y25~~y10	0.005	0.098	0.060	-0.016	0.058	0.097	0.138	0.211
y34~~y10	0.005	-0.131	0.044	-0.218	-0.160	-0.132	-0.102	-0.044
y33~~y21	0.005	0.095	0.059	-0.015	0.054	0.096	0.135	0.206
y34~~y21	0.005	-0.131	0.044	-0.218	-0.162	-0.131	-0.101	-0.046
y36~~y24	0.005	-0.119	0.051	-0.215	-0.153	-0.123	-0.083	-0.022
y33~~y25	0.005	-0.101	0.062	-0.213	-0.144	-0.102	-0.061	0.018
y32~~y28	0.005	-0.063	0.064	-0.188	-0.104	-0.062	-0.020	0.065
SC=~y30	0.004	0.418	0.071	0.273	0.370	0.420	0.467	0.552
y8~~y1	0.004	0.085	0.057	-0.021	0.045	0.085	0.125	0.199
y17~~y3	0.004	0.113	0.050	0.015	0.081	0.114	0.146	0.210
y18~~y3	0.004	0.121	0.049	0.020	0.092	0.120	0.153	0.221
y10~~y6	0.004	0.123	0.052	0.017	0.089	0.124	0.159	0.220
y11~~y7	0.004	-0.094	0.059	-0.205	-0.135	-0.095	-0.055	0.016
y33~~y8	0.004	-0.088	0.060	-0.203	-0.130	-0.086	-0.045	0.025
y23~~y9	0.004	-0.107	0.059	-0.215	-0.148	-0.108	-0.070	0.014
y24~~y9	0.004	-0.094	0.059	-0.199	-0.135	-0.094	-0.055	0.023
y26~~y9	0.004	0.104	0.056	-0.005	0.069	0.102	0.143	0.212
y22~~y10	0.004	0.139	0.043	0.054	0.110	0.140	0.168	0.220
y31~~y21	0.004	0.110	0.056	-0.003	0.072	0.110	0.147	0.216
y28~~y27	0.004	0.124	0.047	0.032	0.092	0.123	0.154	0.221
y5~~y2	0.003	0.122	0.051	0.017	0.087	0.121	0.157	0.220
y37~~y2	0.003	-0.131	0.045	-0.217	-0.162	-0.132	-0.102	-0.041
y5~~y4	0.003	-0.106	0.058	-0.216	-0.145	-0.105	-0.065	0.003
y7~~y5	0.003	0.086	0.054	-0.019	0.051	0.087	0.123	0.194
y14~~y5	0.003	0.114	0.052	0.010	0.082	0.114	0.149	0.211
y38~~y5	0.003	-0.137	0.044	-0.226	-0.167	-0.138	-0.108	-0.051
y21~~y7	0.003	0.102	0.057	-0.008	0.065	0.102	0.142	0.214
y25~~y8	0.003	-0.084	0.063	-0.207	-0.129	-0.084	-0.041	0.039
y37~~y8	0.003	0.095	0.049	-0.004	0.062	0.095	0.127	0.187
y20~~y9	0.003	-0.091	0.060	-0.206	-0.132	-0.091	-0.053	0.028
y30~~y9	0.003	0.125	0.047	0.032	0.093	0.125	0.159	0.219
y29~~y10	0.003	-0.083	0.062	-0.209	-0.125	-0.085	-0.039	0.039
y16~~y12	0.003	0.128	0.048	0.029	0.095	0.129	0.160	0.218
y33~~y13	0.003	0.095	0.059	-0.016	0.053	0.095	0.136	0.209
y24~~y20	0.003	-0.085	0.062	-0.205	-0.127	-0.085	-0.045	0.037
y28~~y20	0.003	0.056	0.063	-0.064	0.012	0.055	0.098	0.176
y34~~y20	0.003	-0.126	0.046	-0.222	-0.158	-0.126	-0.095	-0.037
y37~~y23	0.003	-0.086	0.055	-0.191	-0.122	-0.088	-0.049	0.026
y35~~y26	0.003	0.082	0.060	-0.039	0.039	0.081	0.123	0.201
y35~~y28	0.003	0.085	0.064	-0.042	0.042	0.088	0.128	0.204
y32~~y2	0.002	0.090	0.056	-0.023	0.055	0.091	0.125	0.195
y21~~y3	0.002	-0.098	0.058	-0.207	-0.137	-0.097	-0.059	0.014
y23~~y3	0.002	-0.070	0.061	-0.192	-0.112	-0.070	-0.028	0.049
y34~~y3	0.002	0.125	0.044	0.040	0.097	0.125	0.156	0.207
y25~~y4	0.002	0.066	0.058	-0.044	0.027	0.068	0.105	0.177
y33~~y4	0.002	-0.081	0.059	-0.196	-0.118	-0.080	-0.042	0.025
y36~~y4	0.002	0.112	0.051	0.013	0.079	0.112	0.144	0.209
y29~~y5	0.002	-0.062	0.060	-0.174	-0.103	-0.060	-0.021	0.055
y30~~y5	0.002	-0.113	0.049	-0.216	-0.144	-0.110	-0.080	-0.018
y13~~y7	0.002	0.072	0.058	-0.043	0.034	0.071	0.111	0.184
y28~~y8	0.002	0.075	0.062	-0.039	0.032	0.075	0.115	0.198
y14~~y10	0.002	0.122	0.049	0.027	0.087	0.122	0.156	0.213
y25~~y11	0.002	0.061	0.058	-0.046	0.020	0.061	0.103	0.175
y26~~y11	0.002	0.065	0.060	-0.048	0.024	0.064	0.104	0.184
y29~~y11	0.002	-0.060	0.062	-0.179	-0.101	-0.060	-0.019	0.058
y33~~y11	0.002	-0.059	0.063	-0.182	-0.104	-0.059	-0.016	0.057
y24~~y12	0.002	0.089	0.053	-0.019	0.054	0.090	0.122	0.194
y16~~y13	0.002	0.098	0.054	-0.004	0.062	0.099	0.136	0.199
y29~~y13	0.002	0.071	0.062	-0.049	0.031	0.070	0.112	0.191
y24~~y14	0.002	0.109	0.051	0.008	0.074	0.109	0.144	0.208
y30~~y19	0.002	-0.072	0.055	-0.176	-0.107	-0.072	-0.036	0.036
y22~~y20	0.002	0.117	0.043	0.033	0.089	0.119	0.146	0.198
y23~~y20	0.002	0.049	0.062	-0.069	0.005	0.046	0.093	0.174
y26~~y20	0.002	-0.046	0.062	-0.168	-0.086	-0.047	-0.004	0.075
y30~~y20	0.002	-0.096	0.052	-0.196	-0.131	-0.098	-0.061	0.005
y38~~y20	0.002	-0.130	0.046	-0.222	-0.161	-0.133	-0.099	-0.038
y32~~y21	0.002	0.089	0.058	-0.020	0.048	0.089	0.128	0.199
y36~~y21	0.002	-0.111	0.049	-0.202	-0.144	-0.113	-0.077	-0.017
y38~~y21	0.002	-0.122	0.044	-0.207	-0.153	-0.122	-0.092	-0.034
y34~~y32	0.002	-0.121	0.046	-0.214	-0.151	-0.119	-0.089	-0.032
y24~~y1	0.001	0.020	0.062	-0.101	-0.020	0.019	0.061	0.141
y25~~y1	0.001	-0.043	0.064	-0.171	-0.085	-0.044	-0.003	0.085
y37~~y1	0.001	0.106	0.049	0.005	0.073	0.106	0.140	0.202
y15~~y3	0.001	-0.057	0.060	-0.175	-0.097	-0.059	-0.018	0.068
y24~~y3	0.001	-0.069	0.062	-0.192	-0.111	-0.069	-0.029	0.056
y24~~y4	0.001	-0.045	0.060	-0.162	-0.084	-0.045	-0.004	0.068
y32~~y4	0.001	0.071	0.058	-0.045	0.032	0.072	0.109	0.179
y10~~y5	0.001	0.060	0.056	-0.049	0.024	0.059	0.097	0.173
y11~~y5	0.001	-0.073	0.057	-0.181	-0.111	-0.071	-0.035	0.037
y24~~y5	0.001	0.053	0.062	-0.066	0.009	0.051	0.095	0.182
y28~~y5	0.001	0.044	0.062	-0.080	0.000	0.044	0.088	0.164
y13~~y6	0.001	0.055	0.055	-0.050	0.018	0.053	0.092	0.163
y25~~y6	0.001	0.057	0.058	-0.054	0.017	0.055	0.097	0.170
y29~~y6	0.001	0.033	0.059	-0.082	-0.006	0.032	0.076	0.141
y32~~y7	0.001	-0.075	0.058	-0.191	-0.113	-0.075	-0.037	0.037
y20~~y8	0.001	-0.053	0.061	-0.172	-0.091	-0.053	-0.013	0.073
y26~~y8	0.001	-0.054	0.058	-0.170	-0.091	-0.051	-0.017	0.054
y29~~y8	0.001	-0.072	0.063	-0.193	-0.116	-0.076	-0.031	0.059
y31~~y8	0.001	-0.062	0.057	-0.171	-0.102	-0.060	-0.023	0.040
y11~~y9	0.001	-0.064	0.059	-0.180	-0.107	-0.061	-0.024	0.048
y22~~y9	0.001	-0.116	0.042	-0.200	-0.146	-0.115	-0.086	-0.036
y32~~y9	0.001	-0.077	0.059	-0.193	-0.116	-0.077	-0.037	0.041
y35~~y9	0.001	0.065	0.057	-0.046	0.028	0.065	0.101	0.181
y37~~y9	0.001	0.103	0.049	0.002	0.069	0.104	0.136	0.200
y15~~y10	0.001	0.075	0.058	-0.038	0.036	0.075	0.115	0.185
y19~~y10	0.001	-0.040	0.060	-0.156	-0.080	-0.042	-0.001	0.080
y23~~y10	0.001	0.032	0.063	-0.088	-0.014	0.034	0.076	0.157
y24~~y10	0.001	0.044	0.060	-0.076	0.002	0.045	0.084	0.155
y13~~y11	0.001	0.072	0.058	-0.040	0.030	0.074	0.114	0.182
y14~~y12	0.001	0.115	0.045	0.034	0.083	0.115	0.145	0.207
y35~~y12	0.001	0.073	0.056	-0.024	0.033	0.071	0.109	0.185
y35~~y13	0.001	-0.082	0.060	-0.202	-0.124	-0.082	-0.044	0.038
y26~~y16	0.001	0.061	0.054	-0.045	0.025	0.065	0.096	0.165
y28~~y16	0.001	-0.061	0.055	-0.169	-0.096	-0.060	-0.027	0.045
y29~~y17	0.001	-0.092	0.052	-0.198	-0.125	-0.092	-0.056	0.009
y23~~y18	0.001	-0.090	0.052	-0.190	-0.124	-0.090	-0.056	0.015
y24~~y18	0.001	-0.092	0.054	-0.200	-0.131	-0.091	-0.052	0.010
y21~~y19	0.001	0.075	0.056	-0.039	0.039	0.075	0.112	0.185
y25~~y19	0.001	-0.048	0.065	-0.177	-0.091	-0.049	-0.007	0.081
y29~~y19	0.001	-0.050	0.064	-0.181	-0.092	-0.052	-0.010	0.076
y25~~y20	0.001	0.055	0.064	-0.076	0.014	0.056	0.097	0.182
y33~~y20	0.001	0.048	0.064	-0.084	0.001	0.052	0.091	0.172
y29~~y21	0.001	0.015	0.061	-0.107	-0.025	0.016	0.055	0.133
y32~~y24	0.001	0.027	0.060	-0.093	-0.014	0.028	0.067	0.142
y32~~y25	0.001	-0.035	0.061	-0.152	-0.078	-0.035	0.004	0.086
y30~~y26	0.001	0.087	0.051	-0.015	0.054	0.087	0.120	0.191
y35~~y27	0.001	0.095	0.047	0.000	0.063	0.096	0.126	0.187
y31~~y29	0.001	0.070	0.059	-0.037	0.028	0.072	0.110	0.186
y35~~y31	0.001	0.069	0.055	-0.036	0.033	0.070	0.107	0.174
EL=~y15	0.000	0.031	0.046	-0.057	-0.002	0.032	0.063	0.120
EL=~y16	0.000	0.354	0.047	0.262	0.323	0.354	0.386	0.445
EL=~y19	0.000	0.158	0.043	0.073	0.129	0.158	0.189	0.240
EL=~y20	0.000	0.100	0.045	0.015	0.070	0.099	0.131	0.191
EL=~y21	0.000	0.270	0.049	0.173	0.236	0.269	0.302	0.368
EL=~y23	0.000	0.028	0.039	-0.049	0.001	0.027	0.055	0.101
EL=~y24	0.000	0.019	0.041	-0.059	-0.011	0.020	0.046	0.096
EL=~y25	0.000	-0.070	0.042	-0.149	-0.098	-0.072	-0.042	0.014
EL=~y26	0.000	0.039	0.052	-0.060	0.004	0.039	0.073	0.140
EL=~y27	0.000	0.442	0.039	0.368	0.416	0.441	0.467	0.515
EL=~y28	0.000	0.033	0.040	-0.047	0.008	0.031	0.058	0.112
EL=~y29	0.000	-0.022	0.040	-0.101	-0.047	-0.022	0.006	0.055
EL=~y30	0.000	-0.184	0.045	-0.271	-0.217	-0.186	-0.155	-0.092
EL=~y31	0.000	0.276	0.042	0.192	0.249	0.276	0.304	0.355
EL=~y32	0.000	0.131	0.046	0.043	0.099	0.133	0.162	0.220
EL=~y33	0.000	0.096	0.042	0.011	0.069	0.095	0.123	0.177
EL=~y35	0.000	0.092	0.044	0.007	0.061	0.092	0.121	0.178
EL=~y36	0.000	-0.408	0.050	-0.506	-0.443	-0.409	-0.374	-0.308
EL=~y37	0.000	-0.363	0.051	-0.460	-0.397	-0.364	-0.329	-0.259
SC=~y1	0.000	-0.004	0.063	-0.128	-0.048	-0.004	0.037	0.124
SC=~y2	0.000	-0.125	0.058	-0.247	-0.161	-0.124	-0.088	-0.015
SC=~y3	0.000	-0.102	0.062	-0.219	-0.145	-0.101	-0.059	0.024
SC=~y4	0.000	-0.021	0.059	-0.146	-0.057	-0.020	0.019	0.091
SC=~y5	0.000	0.028	0.065	-0.091	-0.016	0.025	0.070	0.157
SC=~y6	0.000	0.047	0.064	-0.089	0.006	0.049	0.091	0.167
SC=~y7	0.000	0.113	0.071	-0.016	0.066	0.110	0.159	0.254
SC=~y8	0.000	0.052	0.059	-0.067	0.012	0.050	0.092	0.167
SC=~y9	0.000	0.068	0.067	-0.072	0.024	0.067	0.112	0.200
SC=~y10	0.000	0.077	0.062	-0.041	0.036	0.077	0.116	0.201
SC=~y11	0.000	0.117	0.068	-0.020	0.072	0.117	0.163	0.250
SC=~y12	0.000	-0.029	0.071	-0.174	-0.078	-0.030	0.017	0.110
SC=~y13	0.000	-0.079	0.058	-0.193	-0.119	-0.079	-0.038	0.032
SC=~y22	0.000	-0.093	0.068	-0.221	-0.138	-0.092	-0.050	0.039
SC=~y23	0.000	-0.064	0.063	-0.188	-0.105	-0.062	-0.021	0.057
SC=~y24	0.000	-0.173	0.066	-0.305	-0.218	-0.172	-0.127	-0.044
SC=~y25	0.000	0.034	0.065	-0.094	-0.009	0.033	0.076	0.167
SC=~y26	0.000	0.126	0.078	-0.034	0.073	0.128	0.181	0.275
SC=~y27	0.000	0.040	0.060	-0.075	-0.004	0.038	0.083	0.160
SC=~y28	0.000	0.054	0.063	-0.067	0.014	0.055	0.097	0.181
SC=~y29	0.000	-0.048	0.060	-0.165	-0.088	-0.050	-0.009	0.072
SC=~y31	0.000	0.035	0.067	-0.094	-0.010	0.035	0.082	0.169
SC=~y32	0.000	-0.013	0.069	-0.147	-0.061	-0.012	0.035	0.114
SC=~y33	0.000	-0.013	0.067	-0.146	-0.059	-0.016	0.034	0.116
SC=~y34	0.000	-0.024	0.075	-0.173	-0.075	-0.026	0.029	0.125
SC=~y35	0.000	-0.058	0.069	-0.194	-0.101	-0.058	-0.013	0.071
SC=~y36	0.000	-0.023	0.075	-0.169	-0.071	-0.025	0.027	0.121
SC=~y37	0.000	-0.084	0.083	-0.244	-0.139	-0.084	-0.027	0.077
SC=~y38	0.000	0.116	0.080	-0.054	0.065	0.119	0.168	0.269
IN=~y1	0.000	-0.021	0.036	-0.094	-0.045	-0.021	0.004	0.051
IN=~y2	0.000	0.230	0.035	0.162	0.206	0.229	0.254	0.298
IN=~y3	0.000	-0.128	0.035	-0.199	-0.150	-0.127	-0.106	-0.064
IN=~y4	0.000	-0.202	0.034	-0.272	-0.223	-0.202	-0.179	-0.140
IN=~y5	0.000	0.127	0.037	0.056	0.102	0.127	0.152	0.200
IN=~y6	0.000	0.231	0.038	0.159	0.204	0.231	0.256	0.306
IN=~y7	0.000	0.019	0.042	-0.056	-0.009	0.017	0.047	0.104
IN=~y8	0.000	-0.111	0.034	-0.178	-0.133	-0.111	-0.088	-0.048
IN=~y9	0.000	-0.095	0.040	-0.172	-0.121	-0.094	-0.069	-0.014
IN=~y10	0.000	0.105	0.037	0.033	0.080	0.104	0.129	0.176
IN=~y11	0.000	0.068	0.041	-0.011	0.040	0.068	0.095	0.145
IN=~y12	0.000	0.322	0.042	0.241	0.293	0.323	0.350	0.402
IN=~y13	0.000	0.117	0.031	0.058	0.095	0.117	0.138	0.177
IN=~y14	0.000	0.457	0.042	0.377	0.428	0.457	0.486	0.543
IN=~y15	0.000	0.057	0.042	-0.025	0.029	0.058	0.085	0.137
IN=~y16	0.000	0.338	0.043	0.256	0.310	0.337	0.367	0.417
IN=~y19	0.000	0.142	0.038	0.064	0.119	0.143	0.168	0.214
IN=~y20	0.000	0.117	0.042	0.033	0.088	0.117	0.144	0.200
IN=~y21	0.000	0.258	0.044	0.170	0.230	0.259	0.285	0.341
IN=~y31	0.000	0.291	0.039	0.218	0.263	0.291	0.319	0.367
IN=~y32	0.000	0.141	0.040	0.063	0.113	0.141	0.171	0.214
IN=~y33	0.000	0.133	0.039	0.058	0.107	0.133	0.163	0.209
IN=~y35	0.000	0.058	0.039	-0.020	0.030	0.057	0.086	0.135
IN=~y36	0.000	-0.415	0.044	-0.505	-0.444	-0.414	-0.387	-0.328
IN=~y37	0.000	-0.364	0.048	-0.459	-0.398	-0.362	-0.332	-0.266
EN=~y1	0.000	-0.038	0.055	-0.149	-0.077	-0.038	0.000	0.066
EN=~y2	0.000	-0.028	0.053	-0.133	-0.064	-0.027	0.008	0.080
EN=~y3	0.000	-0.107	0.054	-0.210	-0.145	-0.109	-0.067	0.000
EN=~y4	0.000	-0.028	0.056	-0.141	-0.065	-0.028	0.009	0.082
EN=~y5	0.000	0.014	0.055	-0.096	-0.021	0.012	0.053	0.122
EN=~y6	0.000	0.088	0.058	-0.029	0.050	0.089	0.127	0.200
EN=~y7	0.000	0.132	0.063	0.002	0.091	0.133	0.174	0.250
EN=~y8	0.000	0.051	0.054	-0.057	0.017	0.052	0.086	0.159
EN=~y9	0.000	0.149	0.060	0.028	0.108	0.150	0.190	0.257
EN=~y10	0.000	-0.010	0.057	-0.119	-0.049	-0.009	0.028	0.106
EN=~y11	0.000	0.037	0.063	-0.084	-0.005	0.037	0.081	0.155
EN=~y12	0.000	0.111	0.068	-0.024	0.068	0.112	0.157	0.239
EN=~y13	0.000	-0.068	0.055	-0.179	-0.103	-0.069	-0.032	0.040
EN=~y14	0.000	0.293	0.067	0.159	0.246	0.293	0.338	0.423
EN=~y15	0.000	0.006	0.067	-0.120	-0.040	0.004	0.053	0.135
EN=~y16	0.000	0.079	0.066	-0.044	0.033	0.078	0.125	0.207
EN=~y17	0.000	-0.262	0.065	-0.394	-0.305	-0.260	-0.217	-0.134
EN=~y18	0.000	-0.233	0.065	-0.362	-0.273	-0.233	-0.190	-0.107
EN=~y19	0.000	0.051	0.061	-0.076	0.011	0.055	0.092	0.170
EN=~y20	0.000	-0.011	0.064	-0.137	-0.054	-0.011	0.031	0.122
EN=~y21	0.000	0.267	0.070	0.124	0.222	0.264	0.315	0.403
EN=~y22	0.000	-0.010	0.063	-0.130	-0.052	-0.011	0.029	0.115
EN=~y23	0.000	-0.100	0.056	-0.206	-0.137	-0.100	-0.062	0.012
EN=~y24	0.000	-0.120	0.057	-0.226	-0.158	-0.122	-0.082	-0.010
EN=~y25	0.000	-0.067	0.058	-0.184	-0.105	-0.065	-0.028	0.046
EN=~y26	0.000	0.078	0.072	-0.056	0.027	0.078	0.131	0.219
EN=~y27	0.000	0.194	0.057	0.083	0.157	0.193	0.231	0.307
EN=~y28	0.000	0.078	0.056	-0.033	0.042	0.078	0.116	0.194
EN=~y29	0.000	0.041	0.056	-0.070	0.004	0.041	0.079	0.148
EN=~y30	0.000	0.307	0.066	0.176	0.263	0.309	0.351	0.430
y9~~y1	0.000	0.004	0.058	-0.113	-0.032	0.005	0.041	0.122
y11~~y1	0.000	-0.006	0.059	-0.125	-0.046	-0.003	0.036	0.107
y12~~y1	0.000	-0.060	0.052	-0.163	-0.098	-0.060	-0.023	0.036
y13~~y1	0.000	-0.020	0.064	-0.145	-0.062	-0.022	0.023	0.106
y14~~y1	0.000	0.003	0.051	-0.097	-0.031	0.002	0.037	0.104
y15~~y1	0.000	-0.010	0.060	-0.123	-0.052	-0.009	0.030	0.106
y16~~y1	0.000	-0.008	0.055	-0.117	-0.041	-0.008	0.028	0.099
y17~~y1	0.000	0.067	0.048	-0.029	0.033	0.069	0.100	0.165
y18~~y1	0.000	0.059	0.050	-0.038	0.026	0.059	0.094	0.153
y19~~y1	0.000	0.027	0.062	-0.100	-0.014	0.029	0.068	0.144
y22~~y1	0.000	0.048	0.042	-0.033	0.020	0.049	0.076	0.130
y23~~y1	0.000	0.043	0.064	-0.079	-0.003	0.043	0.087	0.170
y26~~y1	0.000	0.005	0.059	-0.116	-0.036	0.005	0.044	0.116
y27~~y1	0.000	-0.034	0.047	-0.125	-0.066	-0.034	-0.002	0.064
y29~~y1	0.000	-0.030	0.061	-0.151	-0.072	-0.032	0.011	0.083
y30~~y1	0.000	0.031	0.050	-0.064	-0.003	0.030	0.064	0.130
y34~~y1	0.000	0.027	0.044	-0.058	-0.002	0.026	0.056	0.113
y35~~y1	0.000	0.019	0.062	-0.103	-0.023	0.018	0.058	0.140
y36~~y1	0.000	-0.036	0.053	-0.148	-0.072	-0.035	-0.001	0.068
y38~~y1	0.000	-0.011	0.046	-0.104	-0.042	-0.013	0.020	0.079
y3~~y2	0.000	0.058	0.052	-0.043	0.023	0.059	0.095	0.155
y10~~y2	0.000	0.044	0.051	-0.055	0.009	0.045	0.080	0.141
y11~~y2	0.000	-0.076	0.055	-0.180	-0.113	-0.076	-0.040	0.033
y12~~y2	0.000	0.090	0.047	-0.001	0.059	0.091	0.122	0.185
y15~~y2	0.000	-0.003	0.054	-0.113	-0.039	-0.003	0.030	0.104
y20~~y2	0.000	0.034	0.057	-0.079	-0.005	0.035	0.073	0.138
y21~~y2	0.000	-0.002	0.053	-0.111	-0.035	0.000	0.034	0.097
y23~~y2	0.000	0.094	0.054	-0.008	0.056	0.093	0.132	0.199
y24~~y2	0.000	0.078	0.056	-0.030	0.042	0.077	0.116	0.189
y25~~y2	0.000	-0.011	0.054	-0.114	-0.047	-0.013	0.024	0.098
y26~~y2	0.000	-0.012	0.051	-0.111	-0.044	-0.009	0.021	0.083
y28~~y2	0.000	-0.052	0.054	-0.156	-0.087	-0.053	-0.015	0.058
y29~~y2	0.000	0.030	0.058	-0.079	-0.012	0.028	0.069	0.143
y31~~y2	0.000	0.077	0.052	-0.025	0.040	0.079	0.113	0.175
y35~~y2	0.000	0.034	0.057	-0.075	-0.006	0.032	0.075	0.146
y8~~y3	0.000	0.049	0.056	-0.064	0.011	0.048	0.087	0.156
y9~~y3	0.000	-0.035	0.060	-0.153	-0.074	-0.036	0.005	0.084
y11~~y3	0.000	0.010	0.060	-0.110	-0.030	0.010	0.053	0.126
y13~~y3	0.000	-0.056	0.063	-0.176	-0.100	-0.056	-0.015	0.074
y14~~y3	0.000	0.002	0.053	-0.102	-0.034	0.002	0.037	0.104
y16~~y3	0.000	-0.057	0.055	-0.163	-0.097	-0.055	-0.020	0.049
y22~~y3	0.000	-0.086	0.045	-0.177	-0.116	-0.083	-0.055	-0.007
y25~~y3	0.000	0.040	0.061	-0.077	-0.002	0.041	0.079	0.156
y26~~y3	0.000	-0.039	0.060	-0.154	-0.078	-0.040	-0.001	0.080
y27~~y3	0.000	-0.073	0.046	-0.165	-0.103	-0.071	-0.042	0.013
y29~~y3	0.000	0.030	0.064	-0.098	-0.012	0.031	0.072	0.155
y32~~y3	0.000	0.003	0.059	-0.111	-0.038	0.006	0.044	0.119
y19~~y4	0.000	0.006	0.058	-0.105	-0.035	0.008	0.047	0.116
y20~~y4	0.000	-0.051	0.059	-0.164	-0.091	-0.051	-0.012	0.065
y23~~y4	0.000	-0.016	0.059	-0.127	-0.056	-0.018	0.023	0.106
y26~~y4	0.000	-0.045	0.056	-0.150	-0.084	-0.044	-0.004	0.055
y29~~y4	0.000	-0.056	0.061	-0.181	-0.096	-0.056	-0.015	0.054
y31~~y4	0.000	-0.088	0.053	-0.190	-0.125	-0.090	-0.051	0.012
y35~~y4	0.000	0.014	0.058	-0.099	-0.024	0.017	0.054	0.125
y8~~y5	0.000	-0.048	0.057	-0.164	-0.086	-0.047	-0.008	0.058
y9~~y5	0.000	-0.004	0.056	-0.112	-0.041	-0.005	0.031	0.109
y13~~y5	0.000	0.059	0.058	-0.054	0.020	0.059	0.098	0.172
y15~~y5	0.000	-0.025	0.058	-0.136	-0.066	-0.024	0.013	0.092
y21~~y5	0.000	-0.015	0.056	-0.130	-0.051	-0.015	0.023	0.096
y22~~y5	0.000	0.110	0.043	0.021	0.083	0.110	0.139	0.193
y26~~y5	0.000	-0.038	0.059	-0.142	-0.081	-0.041	0.004	0.074
y32~~y5	0.000	0.000	0.059	-0.113	-0.043	-0.001	0.041	0.115
y33~~y5	0.000	0.023	0.059	-0.088	-0.017	0.022	0.062	0.139
y34~~y5	0.000	-0.086	0.045	-0.179	-0.116	-0.084	-0.056	0.002
y11~~y6	0.000	0.004	0.057	-0.105	-0.034	0.003	0.040	0.120
y15~~y6	0.000	-0.031	0.055	-0.141	-0.068	-0.031	0.008	0.075
y21~~y6	0.000	0.045	0.054	-0.059	0.009	0.045	0.084	0.150
y23~~y6	0.000	-0.041	0.057	-0.153	-0.078	-0.039	-0.003	0.069
y24~~y6	0.000	0.051	0.056	-0.062	0.013	0.051	0.090	0.159
y26~~y6	0.000	0.004	0.054	-0.106	-0.032	0.005	0.045	0.108
y28~~y6	0.000	0.055	0.057	-0.062	0.017	0.053	0.094	0.167
y32~~y6	0.000	0.006	0.055	-0.103	-0.030	0.006	0.044	0.113
y8~~y7	0.000	0.027	0.057	-0.082	-0.009	0.028	0.064	0.143
y12~~y7	0.000	0.028	0.053	-0.075	-0.008	0.029	0.062	0.131
y14~~y7	0.000	0.057	0.052	-0.039	0.022	0.056	0.092	0.158
y15~~y7	0.000	0.047	0.058	-0.070	0.010	0.047	0.085	0.159
y16~~y7	0.000	0.017	0.053	-0.082	-0.019	0.017	0.052	0.126
y17~~y7	0.000	-0.027	0.050	-0.127	-0.060	-0.026	0.005	0.072
y18~~y7	0.000	-0.016	0.049	-0.106	-0.050	-0.017	0.019	0.080
y19~~y7	0.000	-0.038	0.060	-0.158	-0.077	-0.039	0.002	0.078
y20~~y7	0.000	-0.053	0.060	-0.167	-0.095	-0.052	-0.010	0.063
y22~~y7	0.000	0.029	0.043	-0.054	-0.002	0.029	0.059	0.111
y23~~y7	0.000	0.037	0.062	-0.079	-0.006	0.038	0.080	0.150
y24~~y7	0.000	0.000	0.060	-0.117	-0.041	-0.001	0.039	0.119
y25~~y7	0.000	0.031	0.059	-0.089	-0.006	0.031	0.070	0.148
y26~~y7	0.000	0.064	0.059	-0.055	0.025	0.065	0.103	0.179
y27~~y7	0.000	-0.047	0.046	-0.135	-0.078	-0.047	-0.017	0.043
y28~~y7	0.000	0.069	0.064	-0.055	0.027	0.069	0.113	0.194
y30~~y7	0.000	-0.014	0.052	-0.110	-0.052	-0.014	0.020	0.095
y33~~y7	0.000	-0.016	0.059	-0.131	-0.055	-0.014	0.026	0.097
y34~~y7	0.000	-0.038	0.043	-0.127	-0.068	-0.038	-0.008	0.044
y35~~y7	0.000	0.030	0.063	-0.092	-0.011	0.028	0.071	0.152
y37~~y7	0.000	0.040	0.050	-0.058	0.007	0.041	0.073	0.137
y38~~y7	0.000	-0.035	0.045	-0.127	-0.066	-0.034	-0.004	0.048
y9~~y8	0.000	0.034	0.057	-0.073	-0.005	0.032	0.072	0.147
y11~~y8	0.000	0.036	0.057	-0.077	0.000	0.037	0.072	0.148
y15~~y8	0.000	-0.002	0.060	-0.119	-0.041	-0.003	0.038	0.123
y21~~y8	0.000	0.047	0.059	-0.064	0.007	0.050	0.085	0.165
y23~~y8	0.000	-0.019	0.059	-0.135	-0.058	-0.021	0.020	0.099
y24~~y8	0.000	-0.038	0.059	-0.153	-0.079	-0.038	0.001	0.076
y32~~y8	0.000	-0.053	0.058	-0.171	-0.093	-0.051	-0.016	0.061
y35~~y8	0.000	-0.031	0.062	-0.153	-0.074	-0.033	0.008	0.099
y12~~y9	0.000	-0.043	0.051	-0.144	-0.078	-0.040	-0.009	0.055
y14~~y9	0.000	0.003	0.050	-0.099	-0.028	0.002	0.036	0.095
y15~~y9	0.000	0.017	0.058	-0.096	-0.020	0.017	0.055	0.134
y16~~y9	0.000	-0.066	0.053	-0.167	-0.102	-0.066	-0.029	0.039
y17~~y9	0.000	0.029	0.050	-0.069	-0.004	0.030	0.062	0.128
y18~~y9	0.000	0.077	0.050	-0.022	0.043	0.080	0.111	0.172
y21~~y9	0.000	0.001	0.058	-0.115	-0.038	-0.001	0.042	0.111
y27~~y9	0.000	-0.121	0.045	-0.206	-0.151	-0.122	-0.090	-0.031
y31~~y9	0.000	-0.046	0.055	-0.148	-0.085	-0.048	-0.007	0.064
y38~~y9	0.000	0.055	0.044	-0.034	0.026	0.055	0.086	0.134
y17~~y10	0.000	-0.087	0.049	-0.186	-0.119	-0.085	-0.055	0.013
y18~~y10	0.000	-0.083	0.049	-0.180	-0.115	-0.082	-0.049	0.012
y20~~y10	0.000	0.035	0.061	-0.083	-0.005	0.035	0.075	0.157
y26~~y10	0.000	0.046	0.056	-0.065	0.008	0.046	0.083	0.157
y27~~y10	0.000	0.024	0.049	-0.070	-0.010	0.025	0.057	0.122
y28~~y10	0.000	0.059	0.063	-0.069	0.016	0.060	0.101	0.177
y30~~y10	0.000	-0.080	0.051	-0.179	-0.114	-0.079	-0.044	0.014
y31~~y10	0.000	-0.042	0.056	-0.155	-0.082	-0.040	-0.003	0.065
y33~~y10	0.000	-0.035	0.060	-0.151	-0.074	-0.037	0.007	0.083
y36~~y10	0.000	-0.023	0.051	-0.122	-0.056	-0.025	0.012	0.078
y37~~y10	0.000	-0.082	0.051	-0.179	-0.116	-0.082	-0.047	0.015
y38~~y10	0.000	0.016	0.045	-0.076	-0.011	0.018	0.045	0.105
y14~~y11	0.000	0.046	0.050	-0.049	0.015	0.046	0.078	0.149
y15~~y11	0.000	0.028	0.060	-0.091	-0.012	0.029	0.069	0.143
y16~~y11	0.000	0.095	0.052	-0.003	0.058	0.095	0.131	0.197
y17~~y11	0.000	-0.043	0.050	-0.139	-0.078	-0.044	-0.009	0.058
y18~~y11	0.000	-0.044	0.049	-0.139	-0.078	-0.045	-0.011	0.058
y19~~y11	0.000	-0.022	0.061	-0.139	-0.064	-0.022	0.020	0.094
y20~~y11	0.000	0.019	0.059	-0.098	-0.021	0.018	0.059	0.135
y23~~y11	0.000	-0.036	0.063	-0.161	-0.077	-0.034	0.006	0.086
y24~~y11	0.000	-0.055	0.060	-0.170	-0.098	-0.055	-0.012	0.069
y27~~y11	0.000	0.017	0.046	-0.072	-0.013	0.017	0.048	0.104
y28~~y11	0.000	-0.014	0.062	-0.139	-0.056	-0.013	0.029	0.106
y30~~y11	0.000	0.001	0.051	-0.096	-0.033	0.001	0.035	0.105
y31~~y11	0.000	-0.056	0.051	-0.159	-0.090	-0.056	-0.022	0.044
y32~~y11	0.000	0.012	0.056	-0.093	-0.026	0.011	0.047	0.121
y34~~y11	0.000	-0.058	0.041	-0.139	-0.085	-0.059	-0.032	0.025
y35~~y11	0.000	0.050	0.062	-0.074	0.010	0.050	0.093	0.169
y36~~y11	0.000	-0.027	0.053	-0.132	-0.061	-0.027	0.007	0.070
y37~~y11	0.000	-0.036	0.051	-0.132	-0.068	-0.037	0.000	0.062
y38~~y11	0.000	-0.060	0.045	-0.147	-0.090	-0.061	-0.029	0.027
y15~~y12	0.000	0.018	0.052	-0.083	-0.017	0.019	0.053	0.121
y19~~y12	0.000	0.055	0.053	-0.052	0.019	0.057	0.090	0.157
y20~~y12	0.000	0.085	0.053	-0.010	0.048	0.086	0.124	0.189
y23~~y12	0.000	-0.009	0.053	-0.109	-0.043	-0.009	0.026	0.096
y25~~y12	0.000	-0.009	0.055	-0.116	-0.046	-0.007	0.029	0.092
y26~~y12	0.000	-0.013	0.052	-0.111	-0.049	-0.014	0.021	0.085
y28~~y12	0.000	0.057	0.054	-0.055	0.023	0.059	0.093	0.160
y29~~y12	0.000	-0.017	0.054	-0.125	-0.053	-0.015	0.021	0.087
y32~~y12	0.000	0.041	0.053	-0.066	0.003	0.040	0.076	0.143
y20~~y13	0.000	0.040	0.060	-0.077	-0.001	0.040	0.081	0.156
y22~~y13	0.000	0.131	0.044	0.044	0.101	0.130	0.161	0.216
y23~~y13	0.000	0.008	0.063	-0.122	-0.034	0.006	0.052	0.136
y24~~y13	0.000	-0.003	0.059	-0.123	-0.043	-0.003	0.035	0.117
y26~~y13	0.000	0.047	0.058	-0.068	0.008	0.048	0.087	0.160
y19~~y14	0.000	0.036	0.052	-0.069	0.001	0.036	0.072	0.134
y20~~y14	0.000	0.031	0.053	-0.078	-0.007	0.031	0.069	0.128
y23~~y14	0.000	0.045	0.053	-0.062	0.008	0.045	0.080	0.146
y25~~y14	0.000	-0.028	0.054	-0.128	-0.065	-0.028	0.011	0.075
y26~~y14	0.000	-0.056	0.051	-0.153	-0.091	-0.055	-0.020	0.041
y28~~y14	0.000	-0.026	0.053	-0.131	-0.063	-0.024	0.011	0.081
y29~~y14	0.000	0.033	0.053	-0.071	-0.002	0.031	0.069	0.140
y32~~y14	0.000	0.100	0.049	0.003	0.066	0.102	0.134	0.194
y35~~y14	0.000	0.042	0.055	-0.072	0.005	0.043	0.078	0.151
y17~~y15	0.000	-0.027	0.050	-0.126	-0.061	-0.025	0.008	0.066
y18~~y15	0.000	-0.039	0.050	-0.135	-0.073	-0.039	-0.007	0.058
y21~~y15	0.000	-0.011	0.059	-0.129	-0.049	-0.008	0.028	0.096
y22~~y15	0.000	0.009	0.045	-0.078	-0.021	0.010	0.043	0.094
y23~~y15	0.000	0.056	0.061	-0.057	0.014	0.055	0.097	0.175
y24~~y15	0.000	0.029	0.062	-0.093	-0.011	0.027	0.071	0.150
y26~~y15	0.000	-0.050	0.058	-0.165	-0.089	-0.051	-0.011	0.062
y27~~y15	0.000	0.067	0.045	-0.024	0.037	0.067	0.099	0.157
y29~~y15	0.000	0.030	0.062	-0.089	-0.013	0.032	0.073	0.146
y30~~y15	0.000	0.005	0.052	-0.103	-0.030	0.004	0.040	0.111
y32~~y15	0.000	-0.010	0.059	-0.119	-0.051	-0.014	0.030	0.110
y33~~y15	0.000	-0.040	0.062	-0.161	-0.082	-0.039	0.003	0.082
y34~~y15	0.000	-0.064	0.045	-0.154	-0.095	-0.062	-0.032	0.026
y36~~y15	0.000	0.040	0.050	-0.064	0.006	0.040	0.074	0.136
y37~~y15	0.000	0.011	0.052	-0.092	-0.023	0.012	0.046	0.116
y38~~y15	0.000	0.033	0.047	-0.062	0.000	0.034	0.066	0.125
y20~~y16	0.000	0.079	0.052	-0.020	0.046	0.079	0.114	0.182
y21~~y16	0.000	0.043	0.050	-0.062	0.013	0.045	0.075	0.143
y24~~y16	0.000	0.056	0.054	-0.055	0.016	0.056	0.092	0.164
y25~~y16	0.000	0.061	0.057	-0.054	0.022	0.064	0.098	0.170
y29~~y16	0.000	-0.073	0.054	-0.175	-0.109	-0.072	-0.036	0.034
y32~~y16	0.000	0.037	0.052	-0.062	0.002	0.035	0.073	0.138
y35~~y16	0.000	0.015	0.055	-0.086	-0.023	0.016	0.050	0.125
y23~~y17	0.000	-0.071	0.052	-0.176	-0.107	-0.070	-0.036	0.029
y24~~y17	0.000	-0.067	0.052	-0.170	-0.101	-0.066	-0.031	0.032
y25~~y17	0.000	0.056	0.051	-0.040	0.019	0.057	0.089	0.153
y26~~y17	0.000	0.060	0.049	-0.033	0.025	0.059	0.092	0.159
y28~~y17	0.000	0.027	0.052	-0.070	-0.009	0.026	0.061	0.130
y32~~y17	0.000	-0.091	0.051	-0.190	-0.123	-0.091	-0.056	0.004
y25~~y18	0.000	0.044	0.051	-0.050	0.010	0.044	0.080	0.145
y26~~y18	0.000	-0.005	0.048	-0.099	-0.036	-0.005	0.029	0.084
y28~~y18	0.000	-0.065	0.050	-0.163	-0.098	-0.063	-0.034	0.030
y29~~y18	0.000	0.027	0.051	-0.077	-0.005	0.027	0.058	0.128
y22~~y19	0.000	0.110	0.046	0.017	0.081	0.110	0.141	0.199
y23~~y19	0.000	0.021	0.061	-0.096	-0.020	0.018	0.064	0.143
y24~~y19	0.000	0.060	0.060	-0.056	0.020	0.061	0.099	0.176
y26~~y19	0.000	0.027	0.058	-0.097	-0.010	0.028	0.066	0.133
y28~~y19	0.000	-0.047	0.065	-0.164	-0.094	-0.047	-0.002	0.080
y31~~y19	0.000	0.074	0.056	-0.037	0.038	0.071	0.111	0.184
y29~~y20	0.000	-0.023	0.066	-0.165	-0.066	-0.027	0.023	0.107
y31~~y20	0.000	0.060	0.056	-0.044	0.021	0.061	0.099	0.171
y32~~y20	0.000	-0.058	0.059	-0.169	-0.100	-0.058	-0.016	0.053
y22~~y21	0.000	0.099	0.041	0.013	0.072	0.099	0.125	0.177
y23~~y21	0.000	0.016	0.060	-0.096	-0.023	0.013	0.054	0.142
y24~~y21	0.000	-0.047	0.061	-0.166	-0.090	-0.048	-0.005	0.073
y26~~y21	0.000	0.066	0.056	-0.040	0.028	0.067	0.104	0.172
y30~~y21	0.000	-0.065	0.049	-0.160	-0.098	-0.066	-0.032	0.034
y37~~y21	0.000	-0.064	0.049	-0.160	-0.096	-0.063	-0.032	0.036
y25~~y22	0.000	0.033	0.045	-0.053	0.004	0.032	0.063	0.125
y26~~y22	0.000	-0.015	0.043	-0.098	-0.044	-0.015	0.013	0.067
y28~~y22	0.000	-0.081	0.046	-0.163	-0.114	-0.081	-0.049	0.013
y29~~y22	0.000	-0.072	0.046	-0.159	-0.103	-0.072	-0.040	0.016
y32~~y22	0.000	0.034	0.041	-0.050	0.007	0.035	0.060	0.116
y33~~y22	0.000	0.032	0.043	-0.050	0.001	0.030	0.062	0.118
y35~~y22	0.000	0.034	0.044	-0.050	0.004	0.032	0.064	0.124
y27~~y23	0.000	-0.055	0.047	-0.147	-0.087	-0.056	-0.023	0.033
y29~~y23	0.000	-0.006	0.063	-0.126	-0.048	-0.004	0.038	0.115
y31~~y23	0.000	-0.003	0.056	-0.108	-0.040	-0.003	0.034	0.112
y33~~y23	0.000	-0.037	0.062	-0.158	-0.081	-0.037	0.005	0.086
y34~~y23	0.000	-0.065	0.046	-0.153	-0.098	-0.065	-0.033	0.024
y36~~y23	0.000	-0.093	0.052	-0.190	-0.128	-0.093	-0.060	0.013
y38~~y23	0.000	-0.095	0.047	-0.194	-0.127	-0.094	-0.063	-0.008
y25~~y24	0.000	0.089	0.059	-0.018	0.049	0.088	0.131	0.207
y26~~y24	0.000	-0.067	0.061	-0.188	-0.107	-0.065	-0.027	0.052
y27~~y24	0.000	0.068	0.046	-0.017	0.035	0.066	0.099	0.162
y28~~y24	0.000	0.035	0.062	-0.089	-0.007	0.036	0.076	0.157
y31~~y24	0.000	0.044	0.059	-0.079	0.004	0.045	0.082	0.161
y33~~y24	0.000	-0.016	0.062	-0.134	-0.059	-0.019	0.027	0.102
y34~~y24	0.000	-0.092	0.046	-0.177	-0.123	-0.093	-0.063	0.001
y35~~y24	0.000	0.035	0.063	-0.084	-0.008	0.031	0.079	0.162
y38~~y24	0.000	-0.068	0.045	-0.156	-0.098	-0.067	-0.039	0.019
y27~~y25	0.000	-0.096	0.050	-0.190	-0.130	-0.098	-0.063	0.006
y30~~y25	0.000	-0.056	0.053	-0.158	-0.091	-0.057	-0.019	0.045
y31~~y25	0.000	-0.014	0.060	-0.130	-0.054	-0.015	0.028	0.102
y34~~y25	0.000	0.020	0.045	-0.068	-0.011	0.020	0.052	0.106
y36~~y25	0.000	0.073	0.053	-0.031	0.039	0.070	0.109	0.174
y37~~y25	0.000	0.055	0.053	-0.048	0.019	0.056	0.088	0.162
y38~~y25	0.000	0.039	0.048	-0.061	0.007	0.040	0.071	0.128
y27~~y26	0.000	0.039	0.044	-0.046	0.010	0.038	0.068	0.132
y29~~y26	0.000	-0.057	0.061	-0.177	-0.100	-0.055	-0.017	0.063
y32~~y26	0.000	-0.056	0.057	-0.161	-0.097	-0.056	-0.018	0.058
y33~~y26	0.000	-0.042	0.060	-0.167	-0.082	-0.041	-0.001	0.070
y34~~y26	0.000	0.030	0.044	-0.053	0.000	0.030	0.059	0.124
y36~~y26	0.000	0.035	0.052	-0.064	0.000	0.034	0.070	0.137
y37~~y26	0.000	0.058	0.050	-0.036	0.024	0.057	0.093	0.152
y38~~y26	0.000	0.016	0.044	-0.071	-0.015	0.017	0.045	0.102
y29~~y27	0.000	0.003	0.048	-0.093	-0.028	0.001	0.034	0.096
y32~~y27	0.000	0.069	0.047	-0.026	0.038	0.070	0.099	0.162
y30~~y28	0.000	0.015	0.055	-0.092	-0.025	0.016	0.054	0.125
y31~~y28	0.000	0.007	0.058	-0.111	-0.031	0.010	0.043	0.117
y34~~y28	0.000	-0.017	0.046	-0.101	-0.049	-0.018	0.013	0.080
y36~~y28	0.000	-0.002	0.054	-0.108	-0.040	-0.002	0.033	0.103
y37~~y28	0.000	0.009	0.055	-0.095	-0.029	0.009	0.044	0.121
y38~~y28	0.000	-0.001	0.049	-0.093	-0.034	-0.001	0.032	0.098
y30~~y29	0.000	-0.037	0.053	-0.144	-0.070	-0.037	-0.001	0.069
y34~~y29	0.000	-0.038	0.047	-0.128	-0.068	-0.038	-0.009	0.053
y35~~y29	0.000	0.026	0.067	-0.101	-0.021	0.026	0.069	0.157
y36~~y29	0.000	0.001	0.052	-0.106	-0.034	-0.001	0.037	0.105
y37~~y29	0.000	0.002	0.054	-0.105	-0.035	0.004	0.037	0.107
y38~~y29	0.000	-0.060	0.047	-0.157	-0.091	-0.059	-0.026	0.028
y32~~y30	0.000	-0.037	0.051	-0.134	-0.071	-0.038	-0.004	0.066
y35~~y30	0.000	-0.022	0.054	-0.128	-0.059	-0.022	0.014	0.085
y35~~y32	0.000	0.009	0.060	-0.107	-0.029	0.010	0.051	0.125
y35~~y34	0.000	0.022	0.047	-0.071	-0.010	0.024	0.053	0.111

Modified Model

mod2 <- "
EL =~ Q4_3 + Q4_4 + Q4_5 + Q4_9 + Q4_11 + Q4_15 + Q4_18
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_5 + Q5_6 + Q5_12
IN =~ Q6_2 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_14

EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN

Q4_3 ~~ Q4_4
Q5_5 + Q5_2 ~~ Q5_6
Q6_2 ~~ Q6_8
Q7_7 ~~ Q7_8
"

fit2 <- lavaan::cfa(mod2, data=mydata, estimator = "MLM")
summary(fit2, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 62 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of free parameters                         61
                                                      
  Number of observations                           312
                                                      
Model Test User Model:
                                              Standard      Robust
  Test Statistic                               723.005     540.595
  Degrees of freedom                               264         264
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.337
       Satorra-Bentler correction                                 

Model Test Baseline Model:

  Test statistic                              4736.187    3156.822
  Degrees of freedom                               300         300
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.500

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.897       0.903
  Tucker-Lewis Index (TLI)                       0.882       0.890
                                                                  
  Robust Comparative Fit Index (CFI)                         0.914
  Robust Tucker-Lewis Index (TLI)                            0.902

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -8522.977   -8522.977
  Loglikelihood unrestricted model (H1)      -8161.475   -8161.475
                                                                  
  Akaike (AIC)                               17167.955   17167.955
  Bayesian (BIC)                             17396.278   17396.278
  Sample-size adjusted Bayesian (BIC)        17202.807   17202.807

Root Mean Square Error of Approximation:

  RMSEA                                          0.075       0.058
  90 Percent confidence interval - lower         0.068       0.052
  90 Percent confidence interval - upper         0.081       0.064
  P-value RMSEA <= 0.05                          0.000       0.016
                                                                  
  Robust RMSEA                                               0.067
  90 Percent confidence interval - lower                     0.059
  90 Percent confidence interval - upper                     0.075

Standardized Root Mean Square Residual:

  SRMR                                           0.063       0.063

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model          Structured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_3              1.000                               0.629    0.743
    Q4_4              1.037    0.051   20.319    0.000    0.652    0.770
    Q4_5              1.049    0.077   13.662    0.000    0.660    0.758
    Q4_9              1.076    0.093   11.507    0.000    0.676    0.701
    Q4_11             1.163    0.083   14.020    0.000    0.732    0.770
    Q4_15             1.004    0.071   14.060    0.000    0.631    0.716
    Q4_18             1.015    0.069   14.748    0.000    0.639    0.809
  SC =~                                                                 
    Q5_1              1.000                               0.636    0.669
    Q5_2              1.069    0.097   11.060    0.000    0.679    0.673
    Q5_3              1.121    0.097   11.600    0.000    0.712    0.685
    Q5_5              1.069    0.111    9.654    0.000    0.679    0.642
    Q5_6              1.045    0.099   10.588    0.000    0.664    0.721
    Q5_12             1.012    0.093   10.824    0.000    0.643    0.636
  IN =~                                                                 
    Q6_2              1.000                               0.597    0.657
    Q6_5              0.925    0.122    7.583    0.000    0.552    0.503
    Q6_6              1.060    0.095   11.169    0.000    0.633    0.806
    Q6_7              1.250    0.122   10.277    0.000    0.746    0.852
    Q6_8              1.094    0.090   12.147    0.000    0.653    0.784
    Q6_11             1.110    0.126    8.787    0.000    0.663    0.680
  EN =~                                                                 
    Q7_2              1.000                               0.682    0.770
    Q7_4              0.933    0.070   13.359    0.000    0.637    0.676
    Q7_5              1.072    0.076   14.177    0.000    0.732    0.777
    Q7_7              0.982    0.101    9.749    0.000    0.670    0.629
    Q7_8              1.027    0.078   13.158    0.000    0.701    0.749
    Q7_14             0.873    0.102    8.520    0.000    0.596    0.572

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.277    0.047    5.871    0.000    0.694    0.694
    IN                0.284    0.048    5.938    0.000    0.756    0.756
    EN                0.319    0.047    6.797    0.000    0.744    0.744
  SC ~~                                                                 
    IN                0.254    0.046    5.502    0.000    0.670    0.670
    EN                0.357    0.048    7.382    0.000    0.824    0.824
  IN ~~                                                                 
    EN                0.321    0.051    6.352    0.000    0.789    0.789
 .Q4_3 ~~                                                               
   .Q4_4              0.117    0.024    4.895    0.000    0.117    0.381
 .Q5_5 ~~                                                               
   .Q5_6              0.152    0.042    3.653    0.000    0.152    0.293
 .Q5_2 ~~                                                               
   .Q5_6             -0.063    0.035   -1.805    0.071   -0.063   -0.132
 .Q6_2 ~~                                                               
   .Q6_8              0.097    0.036    2.654    0.008    0.097    0.273
 .Q7_7 ~~                                                               
   .Q7_8              0.136    0.045    3.040    0.002    0.136    0.264

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.396    0.062    6.400    0.000    1.000    1.000
    SC                0.404    0.063    6.444    0.000    1.000    1.000
    IN                0.356    0.073    4.906    0.000    1.000    1.000
    EN                0.466    0.065    7.217    0.000    1.000    1.000
   .Q4_3              0.321    0.034    9.370    0.000    0.321    0.448
   .Q4_4              0.292    0.025   11.714    0.000    0.292    0.407
   .Q4_5              0.321    0.042    7.703    0.000    0.321    0.425
   .Q4_9              0.474    0.048    9.855    0.000    0.474    0.509
   .Q4_11             0.368    0.038    9.722    0.000    0.368    0.408
   .Q4_15             0.380    0.040    9.395    0.000    0.380    0.488
   .Q4_18             0.215    0.026    8.149    0.000    0.215    0.345
   .Q5_1              0.499    0.045   11.027    0.000    0.499    0.553
   .Q5_2              0.559    0.059    9.527    0.000    0.559    0.548
   .Q5_3              0.574    0.059    9.727    0.000    0.574    0.531
   .Q5_5              0.658    0.068    9.621    0.000    0.658    0.588
   .Q5_6              0.408    0.048    8.515    0.000    0.408    0.481
   .Q5_12             0.609    0.064    9.570    0.000    0.609    0.595
   .Q6_2              0.469    0.056    8.358    0.000    0.469    0.568
   .Q6_5              0.901    0.085   10.586    0.000    0.901    0.747
   .Q6_6              0.217    0.027    8.131    0.000    0.217    0.351
   .Q6_7              0.210    0.030    6.938    0.000    0.210    0.274
   .Q6_8              0.268    0.036    7.459    0.000    0.268    0.386
   .Q6_11             0.512    0.051   10.125    0.000    0.512    0.538
   .Q7_2              0.320    0.039    8.308    0.000    0.320    0.408
   .Q7_4              0.481    0.048    9.934    0.000    0.481    0.542
   .Q7_5              0.351    0.044    7.909    0.000    0.351    0.396
   .Q7_7              0.687    0.061   11.183    0.000    0.687    0.605
   .Q7_8              0.385    0.048    8.068    0.000    0.385    0.440
   .Q7_14             0.731    0.061   11.909    0.000    0.731    0.673

Next, use the Vuong test of nonnested models to compare the relative fit.

fit1 <- lavaan::cfa(mod1, data=mydata)
fit2 <- lavaan::cfa(mod2, data=mydata)

nonnest2::vuongtest(fit1, fit2)


Model 1 
 Class: lavaan 
 Call: lavaan::lavaan(model = mod1, data = mydata, model.type = "cfa", ...

Model 2 
 Class: lavaan 
 Call: lavaan::lavaan(model = mod2, data = mydata, model.type = "cfa", ...

Variance test 
  H0: Model 1 and Model 2 are indistinguishable 
  H1: Model 1 and Model 2 are distinguishable 
    w2 = 29.013,   p = 1.7e-08

Non-nested likelihood ratio test 
  H0: Model fits are equal for the focal population 
  H1A: Model 1 fits better than Model 2 
    z = -44.521,   p = 1
  H1B: Model 2 fits better than Model 1 
    z = -44.521,   p = < 2.2e-16

Final Model with Reliability Estimates

mod3 <- "
EL =~ 1*Q4_3 + lam44*Q4_4 + lam45*Q4_5 + lam49*Q4_9 + lam411*Q4_11 + lam415*Q4_15 + lam418*Q4_18
SC =~ 1*Q5_1 + lam52*Q5_2 + lam53*Q5_3 + lam55*Q5_5 + lam56*Q5_6 + lam512*Q5_12
IN =~ 1*Q6_2 + lam65*Q6_5 + lam66*Q6_6 + lam67*Q6_7 + lam68*Q6_8 + lam611*Q6_11
EN =~ 1*Q7_2 + lam74*Q7_4 + lam75*Q7_5 + lam77*Q7_7 + lam78*Q7_8 + lam714*Q7_14

# Factor covarainces
EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN

# Item residual variances
Q4_3 ~~ psi43*Q4_3
Q4_4 ~~ psi44*Q4_4
Q4_5 ~~ psi45*Q4_5
Q4_9 ~~ psi49*Q4_9
Q4_11 ~~ psi411*Q4_11
Q4_15 ~~ psi415*Q4_15
Q4_18 ~~ psi418*Q4_18
Q5_1 ~~ psi51*Q5_1
Q5_2 ~~ psi52*Q5_2
Q5_3 ~~ psi53*Q5_3
Q5_5 ~~ psi55*Q5_5
Q5_6 ~~ psi56*Q5_6
Q5_12 ~~ psi512*Q5_12
Q6_2 ~~ psi62*Q6_2
Q6_5 ~~ psi65*Q6_5
Q6_6 ~~ psi66*Q6_6
Q6_7 ~~ psi67*Q6_7
Q6_8 ~~ psi68*Q6_8
Q6_11 ~~ psi611*Q6_11
Q7_2 ~~ psi72*Q7_2
Q7_4 ~~ psi74*Q7_4
Q7_5 ~~ psi75*Q7_5
Q7_7 ~~ psi77*Q7_7
Q7_8 ~~ psi78*Q7_8
Q7_14 ~~ psi714*Q7_14

Q4_3 ~~ Q4_4
Q5_5 + Q5_2 ~~ Q5_6
Q6_2 ~~ Q6_8
Q7_7 ~~ Q7_8

# Factor Reliabilities
rEL := (1**2 + lam44**2 + lam45**2 + lam49**2 + lam411**2 + lam415**2 + lam418**2)/(1**2 + lam44**2 + lam45**2 + lam49**2 + lam411**2 + lam415**2 + lam418**2 + psi43 + psi44 + psi45 + psi49 + psi411 + psi415 + psi418)
rSC := (1 + lam52**2 + lam53**2 + lam55**2 + lam56**2 + lam512**2)/(1 + lam52**2 + lam53**2 + lam55**2 + lam56**2 + lam512**2 + psi51 + psi52 + psi53 + psi55 + psi56 + psi512)
rIN := (1**2 + lam65**2 + lam66**2 + lam67**2 + lam68**2 + lam611**2)/(1**2 + lam65**2 + lam66**2 + lam67**2 + lam68**2 + lam611**2 + psi62 + psi65 + psi66 + psi67 + psi68 + psi611)
rEN := (1**2 + lam74**2 + lam75**2 + lam77**2 + lam78**2 + lam714**2)/(1**2 + lam74**2 + lam75**2 + lam77**2 + lam78**2 + lam714**2 + psi72 + psi74 + psi75 + psi77 + psi78 + psi714)
"

fit3 <- lavaan::cfa(mod3, data=mydata, estimator = "MLM")
summary(fit3, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 62 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of free parameters                         61
                                                      
  Number of observations                           312
                                                      
Model Test User Model:
                                              Standard      Robust
  Test Statistic                               723.005     540.595
  Degrees of freedom                               264         264
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.337
       Satorra-Bentler correction                                 

Model Test Baseline Model:

  Test statistic                              4736.187    3156.822
  Degrees of freedom                               300         300
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.500

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.897       0.903
  Tucker-Lewis Index (TLI)                       0.882       0.890
                                                                  
  Robust Comparative Fit Index (CFI)                         0.914
  Robust Tucker-Lewis Index (TLI)                            0.902

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -8522.977   -8522.977
  Loglikelihood unrestricted model (H1)      -8161.475   -8161.475
                                                                  
  Akaike (AIC)                               17167.955   17167.955
  Bayesian (BIC)                             17396.278   17396.278
  Sample-size adjusted Bayesian (BIC)        17202.807   17202.807

Root Mean Square Error of Approximation:

  RMSEA                                          0.075       0.058
  90 Percent confidence interval - lower         0.068       0.052
  90 Percent confidence interval - upper         0.081       0.064
  P-value RMSEA <= 0.05                          0.000       0.016
                                                                  
  Robust RMSEA                                               0.067
  90 Percent confidence interval - lower                     0.059
  90 Percent confidence interval - upper                     0.075

Standardized Root Mean Square Residual:

  SRMR                                           0.063       0.063

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model          Structured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_3              1.000                               0.629    0.743
    Q4_4    (lm44)    1.037    0.051   20.319    0.000    0.652    0.770
    Q4_5    (lm45)    1.049    0.077   13.662    0.000    0.660    0.758
    Q4_9    (lm49)    1.076    0.093   11.507    0.000    0.676    0.701
    Q4_11   (l411)    1.163    0.083   14.020    0.000    0.732    0.770
    Q4_15   (l415)    1.004    0.071   14.060    0.000    0.631    0.716
    Q4_18   (l418)    1.015    0.069   14.748    0.000    0.639    0.809
  SC =~                                                                 
    Q5_1              1.000                               0.636    0.669
    Q5_2    (lm52)    1.069    0.097   11.060    0.000    0.679    0.673
    Q5_3    (lm53)    1.121    0.097   11.600    0.000    0.712    0.685
    Q5_5    (lm55)    1.069    0.111    9.654    0.000    0.679    0.642
    Q5_6    (lm56)    1.045    0.099   10.588    0.000    0.664    0.721
    Q5_12   (l512)    1.012    0.093   10.824    0.000    0.643    0.636
  IN =~                                                                 
    Q6_2              1.000                               0.597    0.657
    Q6_5    (lm65)    0.925    0.122    7.583    0.000    0.552    0.503
    Q6_6    (lm66)    1.060    0.095   11.169    0.000    0.633    0.806
    Q6_7    (lm67)    1.250    0.122   10.277    0.000    0.746    0.852
    Q6_8    (lm68)    1.094    0.090   12.147    0.000    0.653    0.784
    Q6_11   (l611)    1.110    0.126    8.787    0.000    0.663    0.680
  EN =~                                                                 
    Q7_2              1.000                               0.682    0.770
    Q7_4    (lm74)    0.933    0.070   13.359    0.000    0.637    0.676
    Q7_5    (lm75)    1.072    0.076   14.177    0.000    0.732    0.777
    Q7_7    (lm77)    0.982    0.101    9.749    0.000    0.670    0.629
    Q7_8    (lm78)    1.027    0.078   13.158    0.000    0.701    0.749
    Q7_14   (l714)    0.873    0.102    8.520    0.000    0.596    0.572

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.277    0.047    5.871    0.000    0.694    0.694
    IN                0.284    0.048    5.938    0.000    0.756    0.756
    EN                0.319    0.047    6.797    0.000    0.744    0.744
  SC ~~                                                                 
    IN                0.254    0.046    5.502    0.000    0.670    0.670
    EN                0.357    0.048    7.382    0.000    0.824    0.824
  IN ~~                                                                 
    EN                0.321    0.051    6.352    0.000    0.789    0.789
 .Q4_3 ~~                                                               
   .Q4_4              0.117    0.024    4.895    0.000    0.117    0.381
 .Q5_5 ~~                                                               
   .Q5_6              0.152    0.042    3.653    0.000    0.152    0.293
 .Q5_2 ~~                                                               
   .Q5_6             -0.063    0.035   -1.805    0.071   -0.063   -0.132
 .Q6_2 ~~                                                               
   .Q6_8              0.097    0.036    2.654    0.008    0.097    0.273
 .Q7_7 ~~                                                               
   .Q7_8              0.136    0.045    3.040    0.002    0.136    0.264

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.396    0.062    6.400    0.000    1.000    1.000
    SC                0.404    0.063    6.444    0.000    1.000    1.000
    IN                0.356    0.073    4.906    0.000    1.000    1.000
    EN                0.466    0.065    7.217    0.000    1.000    1.000
   .Q4_3    (ps43)    0.321    0.034    9.370    0.000    0.321    0.448
   .Q4_4    (ps44)    0.292    0.025   11.714    0.000    0.292    0.407
   .Q4_5    (ps45)    0.321    0.042    7.703    0.000    0.321    0.425
   .Q4_9    (ps49)    0.474    0.048    9.855    0.000    0.474    0.509
   .Q4_11   (p411)    0.368    0.038    9.722    0.000    0.368    0.408
   .Q4_15   (p415)    0.380    0.040    9.395    0.000    0.380    0.488
   .Q4_18   (p418)    0.215    0.026    8.149    0.000    0.215    0.345
   .Q5_1    (ps51)    0.499    0.045   11.027    0.000    0.499    0.553
   .Q5_2    (ps52)    0.559    0.059    9.527    0.000    0.559    0.548
   .Q5_3    (ps53)    0.574    0.059    9.727    0.000    0.574    0.531
   .Q5_5    (ps55)    0.658    0.068    9.621    0.000    0.658    0.588
   .Q5_6    (ps56)    0.408    0.048    8.515    0.000    0.408    0.481
   .Q5_12   (p512)    0.609    0.064    9.570    0.000    0.609    0.595
   .Q6_2    (ps62)    0.469    0.056    8.358    0.000    0.469    0.568
   .Q6_5    (ps65)    0.901    0.085   10.586    0.000    0.901    0.747
   .Q6_6    (ps66)    0.217    0.027    8.131    0.000    0.217    0.351
   .Q6_7    (ps67)    0.210    0.030    6.938    0.000    0.210    0.274
   .Q6_8    (ps68)    0.268    0.036    7.459    0.000    0.268    0.386
   .Q6_11   (p611)    0.512    0.051   10.125    0.000    0.512    0.538
   .Q7_2    (ps72)    0.320    0.039    8.308    0.000    0.320    0.408
   .Q7_4    (ps74)    0.481    0.048    9.934    0.000    0.481    0.542
   .Q7_5    (ps75)    0.351    0.044    7.909    0.000    0.351    0.396
   .Q7_7    (ps77)    0.687    0.061   11.183    0.000    0.687    0.605
   .Q7_8    (ps78)    0.385    0.048    8.068    0.000    0.385    0.440
   .Q7_14   (p714)    0.731    0.061   11.909    0.000    0.731    0.673

Defined Parameters:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    rEL               0.765    0.019   40.264    0.000    0.607    0.593
    rSC               0.668    0.031   21.722    0.000    0.498    0.497
    rIN               0.730    0.029   24.853    0.000    0.548    0.564
    rEN               0.662    0.027   24.105    0.000    0.523    0.522

# export
parameterEstimates(fit3,standardized = T, output = "pretty")


Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
  EL =~                                                                 
    Q4_3              1.000                               1.000    1.000
    Q4_4    (lm44)    1.037    0.051   20.319    0.000    0.937    1.137
    Q4_5    (lm45)    1.049    0.077   13.662    0.000    0.898    1.199
    Q4_9    (lm49)    1.076    0.093   11.507    0.000    0.892    1.259
    Q4_11   (l411)    1.163    0.083   14.020    0.000    1.001    1.326
    Q4_15   (l415)    1.004    0.071   14.060    0.000    0.864    1.144
    Q4_18   (l418)    1.015    0.069   14.748    0.000    0.880    1.150
  SC =~                                                                 
    Q5_1              1.000                               1.000    1.000
    Q5_2    (lm52)    1.069    0.097   11.060    0.000    0.880    1.258
    Q5_3    (lm53)    1.121    0.097   11.600    0.000    0.932    1.310
    Q5_5    (lm55)    1.069    0.111    9.654    0.000    0.852    1.286
    Q5_6    (lm56)    1.045    0.099   10.588    0.000    0.851    1.238
    Q5_12   (l512)    1.012    0.093   10.824    0.000    0.829    1.195
  IN =~                                                                 
    Q6_2              1.000                               1.000    1.000
    Q6_5    (lm65)    0.925    0.122    7.583    0.000    0.686    1.164
    Q6_6    (lm66)    1.060    0.095   11.169    0.000    0.874    1.246
    Q6_7    (lm67)    1.250    0.122   10.277    0.000    1.011    1.488
    Q6_8    (lm68)    1.094    0.090   12.147    0.000    0.918    1.271
    Q6_11   (l611)    1.110    0.126    8.787    0.000    0.863    1.358
  EN =~                                                                 
    Q7_2              1.000                               1.000    1.000
    Q7_4    (lm74)    0.933    0.070   13.359    0.000    0.796    1.070
    Q7_5    (lm75)    1.072    0.076   14.177    0.000    0.924    1.220
    Q7_7    (lm77)    0.982    0.101    9.749    0.000    0.785    1.180
    Q7_8    (lm78)    1.027    0.078   13.158    0.000    0.874    1.180
    Q7_14   (l714)    0.873    0.102    8.520    0.000    0.672    1.074
   Std.lv  Std.all
                  
    0.629    0.743
    0.652    0.770
    0.660    0.758
    0.676    0.701
    0.732    0.770
    0.631    0.716
    0.639    0.809
                  
    0.636    0.669
    0.679    0.673
    0.712    0.685
    0.679    0.642
    0.664    0.721
    0.643    0.636
                  
    0.597    0.657
    0.552    0.503
    0.633    0.806
    0.746    0.852
    0.653    0.784
    0.663    0.680
                  
    0.682    0.770
    0.637    0.676
    0.732    0.777
    0.670    0.629
    0.701    0.749
    0.596    0.572

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
  EL ~~                                                                 
    SC                0.277    0.047    5.871    0.000    0.185    0.370
    IN                0.284    0.048    5.938    0.000    0.190    0.378
    EN                0.319    0.047    6.797    0.000    0.227    0.412
  SC ~~                                                                 
    IN                0.254    0.046    5.502    0.000    0.164    0.345
    EN                0.357    0.048    7.382    0.000    0.262    0.452
  IN ~~                                                                 
    EN                0.321    0.051    6.352    0.000    0.222    0.421
 .Q4_3 ~~                                                               
   .Q4_4              0.117    0.024    4.895    0.000    0.070    0.163
 .Q5_5 ~~                                                               
   .Q5_6              0.152    0.042    3.653    0.000    0.070    0.234
 .Q5_2 ~~                                                               
   .Q5_6             -0.063    0.035   -1.805    0.071   -0.131    0.005
 .Q6_2 ~~                                                               
   .Q6_8              0.097    0.036    2.654    0.008    0.025    0.168
 .Q7_7 ~~                                                               
   .Q7_8              0.136    0.045    3.040    0.002    0.048    0.223
   Std.lv  Std.all
                  
    0.694    0.694
    0.756    0.756
    0.744    0.744
                  
    0.670    0.670
    0.824    0.824
                  
    0.789    0.789
                  
    0.117    0.381
                  
    0.152    0.293
                  
   -0.063   -0.132
                  
    0.097    0.273
                  
    0.136    0.264

Variances:
                   Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
    EL                0.396    0.062    6.400    0.000    0.274    0.517
    SC                0.404    0.063    6.444    0.000    0.281    0.527
    IN                0.356    0.073    4.906    0.000    0.214    0.499
    EN                0.466    0.065    7.217    0.000    0.339    0.592
   .Q4_3    (ps43)    0.321    0.034    9.370    0.000    0.254    0.388
   .Q4_4    (ps44)    0.292    0.025   11.714    0.000    0.243    0.341
   .Q4_5    (ps45)    0.321    0.042    7.703    0.000    0.239    0.403
   .Q4_9    (ps49)    0.474    0.048    9.855    0.000    0.379    0.568
   .Q4_11   (p411)    0.368    0.038    9.722    0.000    0.294    0.442
   .Q4_15   (p415)    0.380    0.040    9.395    0.000    0.300    0.459
   .Q4_18   (p418)    0.215    0.026    8.149    0.000    0.163    0.267
   .Q5_1    (ps51)    0.499    0.045   11.027    0.000    0.410    0.588
   .Q5_2    (ps52)    0.559    0.059    9.527    0.000    0.444    0.674
   .Q5_3    (ps53)    0.574    0.059    9.727    0.000    0.458    0.689
   .Q5_5    (ps55)    0.658    0.068    9.621    0.000    0.524    0.792
   .Q5_6    (ps56)    0.408    0.048    8.515    0.000    0.314    0.502
   .Q5_12   (p512)    0.609    0.064    9.570    0.000    0.484    0.734
   .Q6_2    (ps62)    0.469    0.056    8.358    0.000    0.359    0.578
   .Q6_5    (ps65)    0.901    0.085   10.586    0.000    0.734    1.068
   .Q6_6    (ps66)    0.217    0.027    8.131    0.000    0.164    0.269
   .Q6_7    (ps67)    0.210    0.030    6.938    0.000    0.150    0.269
   .Q6_8    (ps68)    0.268    0.036    7.459    0.000    0.197    0.338
   .Q6_11   (p611)    0.512    0.051   10.125    0.000    0.413    0.611
   .Q7_2    (ps72)    0.320    0.039    8.308    0.000    0.245    0.396
   .Q7_4    (ps74)    0.481    0.048    9.934    0.000    0.386    0.576
   .Q7_5    (ps75)    0.351    0.044    7.909    0.000    0.264    0.439
   .Q7_7    (ps77)    0.687    0.061   11.183    0.000    0.567    0.807
   .Q7_8    (ps78)    0.385    0.048    8.068    0.000    0.291    0.478
   .Q7_14   (p714)    0.731    0.061   11.909    0.000    0.611    0.851
   Std.lv  Std.all
    1.000    1.000
    1.000    1.000
    1.000    1.000
    1.000    1.000
    0.321    0.448
    0.292    0.407
    0.321    0.425
    0.474    0.509
    0.368    0.408
    0.380    0.488
    0.215    0.345
    0.499    0.553
    0.559    0.548
    0.574    0.531
    0.658    0.588
    0.408    0.481
    0.609    0.595
    0.469    0.568
    0.901    0.747
    0.217    0.351
    0.210    0.274
    0.268    0.386
    0.512    0.538
    0.320    0.408
    0.481    0.542
    0.351    0.396
    0.687    0.605
    0.385    0.440
    0.731    0.673

Defined Parameters:
                   Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
    rEL               0.765    0.019   40.264    0.000    0.728    0.802
    rSC               0.668    0.031   21.722    0.000    0.608    0.728
    rIN               0.730    0.029   24.853    0.000    0.673    0.788
    rEN               0.662    0.027   24.105    0.000    0.609    0.716
   Std.lv  Std.all
    0.607    0.593
    0.498    0.497
    0.548    0.564
    0.523    0.522

#a <- parameterEstimates(fit3,standardized = T, output = "text")
#write.csv(
#  a, "output/cfa_results.csv"
#)

Best fitting model

The following model was were used to obtain a “best” fitting model even if it is un-interpretable. Basic, the goal was to find a model that got the $\chi^2$ to be no-significant.

# final model (reported in manuscript)
mod4 <- "
EL =~ Q4_3 + Q4_4 + Q4_5 + Q4_9 + Q4_11 + Q4_15 + Q4_18
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_5 + Q5_6 + Q5_12
IN =~ Q6_2 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_14

EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN

Q4_3 ~~ Q4_4
Q5_5 + Q5_2 ~~ Q5_6
Q6_2 ~~ Q6_8
Q7_7 ~~ Q7_8
"
fit4 <- lavaan::cfa(mod4, data=mydata, estimator = "MLM")
summary(fit4, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 62 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of free parameters                         61
                                                      
  Number of observations                           312
                                                      
Model Test User Model:
                                              Standard      Robust
  Test Statistic                               723.005     540.595
  Degrees of freedom                               264         264
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.337
       Satorra-Bentler correction                                 

Model Test Baseline Model:

  Test statistic                              4736.187    3156.822
  Degrees of freedom                               300         300
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.500

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.897       0.903
  Tucker-Lewis Index (TLI)                       0.882       0.890
                                                                  
  Robust Comparative Fit Index (CFI)                         0.914
  Robust Tucker-Lewis Index (TLI)                            0.902

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -8522.977   -8522.977
  Loglikelihood unrestricted model (H1)      -8161.475   -8161.475
                                                                  
  Akaike (AIC)                               17167.955   17167.955
  Bayesian (BIC)                             17396.278   17396.278
  Sample-size adjusted Bayesian (BIC)        17202.807   17202.807

Root Mean Square Error of Approximation:

  RMSEA                                          0.075       0.058
  90 Percent confidence interval - lower         0.068       0.052
  90 Percent confidence interval - upper         0.081       0.064
  P-value RMSEA <= 0.05                          0.000       0.016
                                                                  
  Robust RMSEA                                               0.067
  90 Percent confidence interval - lower                     0.059
  90 Percent confidence interval - upper                     0.075

Standardized Root Mean Square Residual:

  SRMR                                           0.063       0.063

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model          Structured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_3              1.000                               0.629    0.743
    Q4_4              1.037    0.051   20.319    0.000    0.652    0.770
    Q4_5              1.049    0.077   13.662    0.000    0.660    0.758
    Q4_9              1.076    0.093   11.507    0.000    0.676    0.701
    Q4_11             1.163    0.083   14.020    0.000    0.732    0.770
    Q4_15             1.004    0.071   14.060    0.000    0.631    0.716
    Q4_18             1.015    0.069   14.748    0.000    0.639    0.809
  SC =~                                                                 
    Q5_1              1.000                               0.636    0.669
    Q5_2              1.069    0.097   11.060    0.000    0.679    0.673
    Q5_3              1.121    0.097   11.600    0.000    0.712    0.685
    Q5_5              1.069    0.111    9.654    0.000    0.679    0.642
    Q5_6              1.045    0.099   10.588    0.000    0.664    0.721
    Q5_12             1.012    0.093   10.824    0.000    0.643    0.636
  IN =~                                                                 
    Q6_2              1.000                               0.597    0.657
    Q6_5              0.925    0.122    7.583    0.000    0.552    0.503
    Q6_6              1.060    0.095   11.169    0.000    0.633    0.806
    Q6_7              1.250    0.122   10.277    0.000    0.746    0.852
    Q6_8              1.094    0.090   12.147    0.000    0.653    0.784
    Q6_11             1.110    0.126    8.787    0.000    0.663    0.680
  EN =~                                                                 
    Q7_2              1.000                               0.682    0.770
    Q7_4              0.933    0.070   13.359    0.000    0.637    0.676
    Q7_5              1.072    0.076   14.177    0.000    0.732    0.777
    Q7_7              0.982    0.101    9.749    0.000    0.670    0.629
    Q7_8              1.027    0.078   13.158    0.000    0.701    0.749
    Q7_14             0.873    0.102    8.520    0.000    0.596    0.572

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.277    0.047    5.871    0.000    0.694    0.694
    IN                0.284    0.048    5.938    0.000    0.756    0.756
    EN                0.319    0.047    6.797    0.000    0.744    0.744
  SC ~~                                                                 
    IN                0.254    0.046    5.502    0.000    0.670    0.670
    EN                0.357    0.048    7.382    0.000    0.824    0.824
  IN ~~                                                                 
    EN                0.321    0.051    6.352    0.000    0.789    0.789
 .Q4_3 ~~                                                               
   .Q4_4              0.117    0.024    4.895    0.000    0.117    0.381
 .Q5_5 ~~                                                               
   .Q5_6              0.152    0.042    3.653    0.000    0.152    0.293
 .Q5_2 ~~                                                               
   .Q5_6             -0.063    0.035   -1.805    0.071   -0.063   -0.132
 .Q6_2 ~~                                                               
   .Q6_8              0.097    0.036    2.654    0.008    0.097    0.273
 .Q7_7 ~~                                                               
   .Q7_8              0.136    0.045    3.040    0.002    0.136    0.264

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.396    0.062    6.400    0.000    1.000    1.000
    SC                0.404    0.063    6.444    0.000    1.000    1.000
    IN                0.356    0.073    4.906    0.000    1.000    1.000
    EN                0.466    0.065    7.217    0.000    1.000    1.000
   .Q4_3              0.321    0.034    9.370    0.000    0.321    0.448
   .Q4_4              0.292    0.025   11.714    0.000    0.292    0.407
   .Q4_5              0.321    0.042    7.703    0.000    0.321    0.425
   .Q4_9              0.474    0.048    9.855    0.000    0.474    0.509
   .Q4_11             0.368    0.038    9.722    0.000    0.368    0.408
   .Q4_15             0.380    0.040    9.395    0.000    0.380    0.488
   .Q4_18             0.215    0.026    8.149    0.000    0.215    0.345
   .Q5_1              0.499    0.045   11.027    0.000    0.499    0.553
   .Q5_2              0.559    0.059    9.527    0.000    0.559    0.548
   .Q5_3              0.574    0.059    9.727    0.000    0.574    0.531
   .Q5_5              0.658    0.068    9.621    0.000    0.658    0.588
   .Q5_6              0.408    0.048    8.515    0.000    0.408    0.481
   .Q5_12             0.609    0.064    9.570    0.000    0.609    0.595
   .Q6_2              0.469    0.056    8.358    0.000    0.469    0.568
   .Q6_5              0.901    0.085   10.586    0.000    0.901    0.747
   .Q6_6              0.217    0.027    8.131    0.000    0.217    0.351
   .Q6_7              0.210    0.030    6.938    0.000    0.210    0.274
   .Q6_8              0.268    0.036    7.459    0.000    0.268    0.386
   .Q6_11             0.512    0.051   10.125    0.000    0.512    0.538
   .Q7_2              0.320    0.039    8.308    0.000    0.320    0.408
   .Q7_4              0.481    0.048    9.934    0.000    0.481    0.542
   .Q7_5              0.351    0.044    7.909    0.000    0.351    0.396
   .Q7_7              0.687    0.061   11.183    0.000    0.687    0.605
   .Q7_8              0.385    0.048    8.068    0.000    0.385    0.440
   .Q7_14             0.731    0.061   11.909    0.000    0.731    0.673

# Residual Analysis
out <- residuals(fit4, type="cor.bollen")
kable(out[[2]], format="html", digit=3)%>%
  kable_styling(full_width = T)%>%
  scroll_box(width="100%", height="800px")

	Q4_3	Q4_4	Q4_5	Q4_9	Q4_11	Q4_15	Q4_18	Q5_1	Q5_2	Q5_3	Q5_5	Q5_6	Q5_12	Q6_2	Q6_5	Q6_6	Q6_7	Q6_8	Q6_11	Q7_2	Q7_4	Q7_5	Q7_7	Q7_8	Q7_14
Q4_3	0.000	0.000	-0.010	-0.074	-0.003	0.023	0.056	0.101	-0.103	-0.014	-0.071	-0.051	0.033	0.004	-0.009	0.003	-0.056	-0.015	0.127	-0.011	0.016	-0.095	-0.026	-0.018	0.024
Q4_4	0.000	0.000	0.018	-0.050	0.026	-0.035	0.017	0.006	-0.116	-0.041	-0.030	0.026	0.021	0.016	-0.022	-0.011	-0.066	-0.054	0.160	0.059	0.084	-0.009	0.014	0.067	0.019
Q4_5	-0.010	0.018	0.000	0.009	0.003	0.006	-0.003	0.042	-0.087	0.031	-0.097	0.020	0.019	0.047	-0.020	-0.040	-0.012	-0.055	0.078	0.015	-0.051	-0.061	-0.005	0.023	-0.019
Q4_9	-0.074	-0.050	0.009	0.000	0.021	0.036	0.006	0.099	-0.018	0.012	-0.025	-0.005	0.124	0.023	0.039	-0.070	0.000	-0.073	0.059	0.135	-0.009	0.002	-0.016	0.063	0.008
Q4_11	-0.003	0.026	0.003	0.021	0.000	-0.069	-0.046	0.128	-0.048	0.009	-0.063	-0.020	0.094	0.012	0.075	0.000	0.048	0.046	0.117	0.094	0.039	0.131	0.094	0.118	0.004
Q4_15	0.023	-0.035	0.006	0.036	-0.069	0.000	0.050	0.091	-0.011	0.056	-0.012	-0.019	0.148	0.024	0.039	-0.067	0.015	-0.041	0.092	-0.072	-0.084	-0.083	-0.073	-0.061	0.073
Q4_18	0.056	0.017	-0.003	0.006	-0.046	0.050	0.000	0.021	-0.062	-0.064	-0.103	-0.034	0.060	-0.008	0.023	-0.050	0.009	-0.023	0.045	-0.027	-0.017	-0.069	-0.138	-0.073	-0.065
Q5_1	0.101	0.006	0.042	0.099	0.128	0.091	0.021	0.000	0.070	0.015	-0.060	-0.080	-0.052	0.050	-0.014	0.021	0.014	0.041	0.142	0.013	-0.006	0.053	0.030	-0.014	0.022
Q5_2	-0.103	-0.116	-0.087	-0.018	-0.048	-0.011	-0.062	0.070	0.000	0.081	0.051	0.014	-0.069	0.010	-0.036	-0.021	-0.096	-0.037	0.104	0.008	-0.084	-0.099	-0.050	-0.162	0.080
Q5_3	-0.014	-0.041	0.031	0.012	0.009	0.056	-0.064	0.015	0.081	0.000	0.033	0.035	-0.079	0.023	0.052	-0.112	-0.077	-0.084	0.050	-0.051	-0.098	-0.046	-0.034	-0.085	0.021
Q5_5	-0.071	-0.030	-0.097	-0.025	-0.063	-0.012	-0.103	-0.060	0.051	0.033	0.000	-0.005	-0.008	-0.067	-0.018	-0.120	-0.113	-0.085	0.114	0.040	-0.019	-0.021	0.091	-0.029	0.218
Q5_6	-0.051	0.026	0.020	-0.005	-0.020	-0.019	-0.034	-0.080	0.014	0.035	-0.005	0.000	0.016	-0.014	0.025	-0.028	-0.056	-0.064	0.152	-0.011	0.027	-0.003	0.038	0.071	0.060
Q5_12	0.033	0.021	0.019	0.124	0.094	0.148	0.060	-0.052	-0.069	-0.079	-0.008	0.016	0.000	0.096	0.114	0.076	0.183	0.078	0.205	0.060	0.056	0.061	0.089	0.108	0.124
Q6_2	0.004	0.016	0.047	0.023	0.012	0.024	-0.008	0.050	0.010	0.023	-0.067	-0.014	0.096	0.000	-0.024	0.021	0.002	0.000	-0.011	-0.017	0.050	-0.039	-0.081	-0.092	-0.062
Q6_5	-0.009	-0.022	-0.020	0.039	0.075	0.039	0.023	-0.014	-0.036	0.052	-0.018	0.025	0.114	-0.024	0.000	-0.003	0.033	-0.043	0.011	-0.072	-0.042	-0.038	-0.017	0.011	-0.012
Q6_6	0.003	-0.011	-0.040	-0.070	0.000	-0.067	-0.050	0.021	-0.021	-0.112	-0.120	-0.028	0.076	0.021	-0.003	0.000	0.028	0.014	-0.028	0.039	-0.041	-0.005	-0.115	-0.056	-0.058
Q6_7	-0.056	-0.066	-0.012	0.000	0.048	0.015	0.009	0.014	-0.096	-0.077	-0.113	-0.056	0.183	0.002	0.033	0.028	0.000	0.029	-0.069	-0.008	-0.041	0.012	-0.106	-0.036	-0.056
Q6_8	-0.015	-0.054	-0.055	-0.073	0.046	-0.041	-0.023	0.041	-0.037	-0.084	-0.085	-0.064	0.078	0.000	-0.043	0.014	0.029	0.000	-0.048	0.036	0.069	0.052	-0.098	-0.028	-0.073
Q6_11	0.127	0.160	0.078	0.059	0.117	0.092	0.045	0.142	0.104	0.050	0.114	0.152	0.205	-0.011	0.011	-0.028	-0.069	-0.048	0.000	0.142	0.147	0.083	0.171	0.136	0.199
Q7_2	-0.011	0.059	0.015	0.135	0.094	-0.072	-0.027	0.013	0.008	-0.051	0.040	-0.011	0.060	-0.017	-0.072	0.039	-0.008	0.036	0.142	0.000	0.018	-0.024	-0.004	-0.002	-0.053
Q7_4	0.016	0.084	-0.051	-0.009	0.039	-0.084	-0.017	-0.006	-0.084	-0.098	-0.019	0.027	0.056	0.050	-0.042	-0.041	-0.041	0.069	0.147	0.018	0.000	0.037	0.006	-0.017	-0.058
Q7_5	-0.095	-0.009	-0.061	0.002	0.131	-0.083	-0.069	0.053	-0.099	-0.046	-0.021	-0.003	0.061	-0.039	-0.038	-0.005	0.012	0.052	0.083	-0.024	0.037	0.000	-0.006	0.035	-0.031
Q7_7	-0.026	0.014	-0.005	-0.016	0.094	-0.073	-0.138	0.030	-0.050	-0.034	0.091	0.038	0.089	-0.081	-0.017	-0.115	-0.106	-0.098	0.171	-0.004	0.006	-0.006	0.000	0.000	0.121
Q7_8	-0.018	0.067	0.023	0.063	0.118	-0.061	-0.073	-0.014	-0.162	-0.085	-0.029	0.071	0.108	-0.092	0.011	-0.056	-0.036	-0.028	0.136	-0.002	-0.017	0.035	0.000	0.000	0.021
Q7_14	0.024	0.019	-0.019	0.008	0.004	0.073	-0.065	0.022	0.080	0.021	0.218	0.060	0.124	-0.062	-0.012	-0.058	-0.056	-0.073	0.199	-0.053	-0.058	-0.031	0.121	0.021	0.000

ggcorrplot(out[[2]], type = "lower")

# modification indices
modindices(fit4, minimum.value = 10, sort = TRUE)

      lhs op   rhs   mi    epc sepc.lv sepc.all sepc.nox
140    EN =~ Q6_11 62.6  1.054   0.719    0.737    0.737
96     SC =~ Q6_11 46.8  0.766   0.487    0.499    0.499
115    IN =~ Q5_12 34.1  0.766   0.457    0.452    0.452
134    EN =~ Q5_12 31.6  1.014   0.692    0.684    0.684
77     EL =~ Q6_11 27.2  0.670   0.421    0.432    0.432
346  Q5_5 ~~ Q7_14 26.8  0.203   0.203    0.293    0.293
126    EN =~ Q4_11 23.9  0.505   0.345    0.363    0.363
363 Q5_12 ~~  Q6_7 18.5  0.106   0.106    0.297    0.297
402  Q6_7 ~~ Q6_11 18.1 -0.106  -0.106   -0.323   -0.323
246 Q4_11 ~~  Q7_5 17.8  0.101   0.101    0.280    0.280
421 Q6_11 ~~ Q7_14 17.1  0.154   0.154    0.251    0.251
130    EN =~  Q5_2 16.9 -0.773  -0.528   -0.522   -0.522
317  Q5_2 ~~  Q7_8 14.6 -0.110  -0.110   -0.237   -0.237
224  Q4_9 ~~  Q7_2 14.3  0.095   0.095    0.244    0.244
71     EL =~ Q5_12 14.3  0.484   0.305    0.301    0.301
102    SC =~ Q7_14 14.2  0.753   0.478    0.459    0.459
419 Q6_11 ~~  Q7_7 14.1  0.129   0.129    0.217    0.217
131    EN =~  Q5_3 13.7 -0.681  -0.465   -0.447   -0.447
128    EN =~ Q4_18 12.8 -0.295  -0.201   -0.255   -0.255
304  Q5_2 ~~  Q5_3 12.7  0.143   0.143    0.253    0.253
434  Q7_7 ~~ Q7_14 12.2  0.143   0.143    0.202    0.202
146  Q4_3 ~~  Q5_1 10.8  0.075   0.075    0.186    0.186
67     EL =~  Q5_2 10.6 -0.425  -0.267   -0.264   -0.264
230 Q4_11 ~~ Q4_15 10.4 -0.080  -0.080   -0.215   -0.215
268 Q4_15 ~~ Q7_14 10.4  0.104   0.104    0.197    0.197
66     EL =~  Q5_1 10.3  0.381   0.240    0.252    0.252

# Revised to be "best fitting"
mod5 <- "
EL =~ Q4_3 + Q4_4 + Q4_5 + Q4_9 + Q4_11 + Q4_15 + Q4_18 + Q5_1
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_5 + Q5_6 + Q5_12 + Q7_14
IN =~ Q6_2 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11 + Q5_12 + Q7_7
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_14 + Q6_11 + Q4_11 + Q4_4

EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN

Q4_3 ~~ Q7_5 + Q6_11
Q4_4 ~~ Q6_11
Q4_3 + Q5_1 ~~ Q4_4
Q4_9 ~~ Q4_3 + Q7_2

Q5_5 ~~ Q5_6 + Q7_14
Q5_2 ~~ Q5_6 + Q7_8
Q6_2 ~~ Q6_8
Q6_11 + Q4_11 ~~  Q7_5
Q7_7 ~~ Q7_8

"
fit5 <- lavaan::cfa(mod5, data=mydata, estimator = "MLM")
summary(fit5, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 89 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of free parameters                         78
                                                      
  Number of observations                           312
                                                      
Model Test User Model:
                                              Standard      Robust
  Test Statistic                               448.802     335.164
  Degrees of freedom                               247         247
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.339
       Satorra-Bentler correction                                 

Model Test Baseline Model:

  Test statistic                              4736.187    3156.822
  Degrees of freedom                               300         300
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.500

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.955       0.969
  Tucker-Lewis Index (TLI)                       0.945       0.963
                                                                  
  Robust Comparative Fit Index (CFI)                         0.972
  Robust Tucker-Lewis Index (TLI)                            0.967

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -8385.876   -8385.876
  Loglikelihood unrestricted model (H1)      -8161.475   -8161.475
                                                                  
  Akaike (AIC)                               16927.752   16927.752
  Bayesian (BIC)                             17219.706   17219.706
  Sample-size adjusted Bayesian (BIC)        16972.316   16972.316

Root Mean Square Error of Approximation:

  RMSEA                                          0.051       0.034
  90 Percent confidence interval - lower         0.044       0.025
  90 Percent confidence interval - upper         0.059       0.041
  P-value RMSEA <= 0.05                          0.390       1.000
                                                                  
  Robust RMSEA                                               0.039
  90 Percent confidence interval - lower                     0.028
  90 Percent confidence interval - upper                     0.049

Standardized Root Mean Square Residual:

  SRMR                                           0.039       0.039

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model          Structured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_3              1.000                               0.643    0.767
    Q4_4              0.809    0.089    9.119    0.000    0.520    0.614
    Q4_5              1.017    0.075   13.551    0.000    0.654    0.752
    Q4_9              1.057    0.090   11.752    0.000    0.680    0.708
    Q4_11             0.794    0.104    7.631    0.000    0.511    0.536
    Q4_15             1.003    0.071   14.100    0.000    0.645    0.731
    Q4_18             1.009    0.069   14.708    0.000    0.648    0.822
    Q5_1              0.442    0.099    4.471    0.000    0.284    0.300
  SC =~                                                                 
    Q5_1              1.000                               0.421    0.444
    Q5_2              1.718    0.278    6.179    0.000    0.723    0.716
    Q5_3              1.730    0.273    6.332    0.000    0.728    0.700
    Q5_5              1.689    0.284    5.957    0.000    0.711    0.671
    Q5_6              1.667    0.273    6.096    0.000    0.702    0.763
    Q5_12             0.880    0.208    4.235    0.000    0.370    0.366
    Q7_14             0.615    0.298    2.066    0.039    0.259    0.249
  IN =~                                                                 
    Q6_2              1.000                               0.592    0.652
    Q6_5              0.929    0.128    7.265    0.000    0.550    0.501
    Q6_6              1.072    0.102   10.540    0.000    0.635    0.808
    Q6_7              1.304    0.134    9.762    0.000    0.772    0.882
    Q6_8              1.110    0.097   11.446    0.000    0.657    0.789
    Q6_11             0.167    0.182    0.916    0.359    0.099    0.101
    Q5_12             0.672    0.146    4.599    0.000    0.398    0.393
    Q7_7             -0.552    0.199   -2.777    0.005   -0.327   -0.307
  EN =~                                                                 
    Q7_2              1.000                               0.663    0.754
    Q7_4              0.958    0.072   13.387    0.000    0.635    0.675
    Q7_5              1.096    0.077   14.268    0.000    0.727    0.772
    Q7_7              1.433    0.186    7.715    0.000    0.951    0.894
    Q7_8              1.048    0.082   12.840    0.000    0.695    0.748
    Q7_14             0.601    0.186    3.224    0.001    0.399    0.383
    Q6_11             1.005    0.163    6.175    0.000    0.667    0.684
    Q4_11             0.422    0.099    4.255    0.000    0.280    0.294
    Q4_4              0.239    0.082    2.921    0.003    0.159    0.187

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.156    0.034    4.627    0.000    0.577    0.577
    IN                0.268    0.048    5.607    0.000    0.704    0.704
    EN                0.302    0.047    6.489    0.000    0.709    0.709
  SC ~~                                                                 
    IN                0.130    0.033    3.979    0.000    0.520    0.520
    EN                0.211    0.041    5.199    0.000    0.756    0.756
  IN ~~                                                                 
    EN                0.302    0.050    6.078    0.000    0.768    0.768
 .Q4_3 ~~                                                               
   .Q7_5             -0.039    0.023   -1.686    0.092   -0.039   -0.122
   .Q6_11             0.052    0.025    2.055    0.040    0.052    0.153
 .Q4_4 ~~                                                               
   .Q6_11             0.045    0.025    1.767    0.077    0.045    0.129
 .Q4_3 ~~                                                               
   .Q4_4              0.111    0.023    4.722    0.000    0.111    0.372
 .Q4_4 ~~                                                               
   .Q5_1             -0.072    0.022   -3.200    0.001   -0.072   -0.184
 .Q4_3 ~~                                                               
   .Q4_9             -0.061    0.027   -2.224    0.026   -0.061   -0.167
 .Q4_9 ~~                                                               
   .Q7_2              0.101    0.028    3.584    0.000    0.101    0.257
 .Q5_5 ~~                                                               
   .Q5_6              0.101    0.043    2.342    0.019    0.101    0.217
   .Q7_14             0.177    0.044    3.979    0.000    0.177    0.268
 .Q5_2 ~~                                                               
   .Q5_6             -0.089    0.035   -2.504    0.012   -0.089   -0.212
   .Q7_8             -0.103    0.030   -3.373    0.001   -0.103   -0.237
 .Q6_2 ~~                                                               
   .Q6_8              0.098    0.038    2.585    0.010    0.098    0.277
 .Q6_11 ~~                                                              
   .Q7_5             -0.076    0.028   -2.674    0.007   -0.076   -0.202
 .Q4_11 ~~                                                              
   .Q7_5              0.057    0.026    2.172    0.030    0.057    0.157
 .Q7_7 ~~                                                               
   .Q7_8              0.095    0.044    2.165    0.030    0.095    0.200

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.413    0.063    6.546    0.000    1.000    1.000
    SC                0.177    0.054    3.308    0.001    1.000    1.000
    IN                0.351    0.074    4.713    0.000    1.000    1.000
    EN                0.440    0.062    7.131    0.000    1.000    1.000
   .Q4_3              0.290    0.032    8.948    0.000    0.290    0.412
   .Q4_4              0.306    0.025   12.312    0.000    0.306    0.426
   .Q4_5              0.328    0.045    7.289    0.000    0.328    0.434
   .Q4_9              0.460    0.049    9.400    0.000    0.460    0.499
   .Q4_11             0.365    0.036   10.211    0.000    0.365    0.403
   .Q4_15             0.362    0.039    9.320    0.000    0.362    0.465
   .Q4_18             0.202    0.026    7.851    0.000    0.202    0.325
   .Q5_1              0.503    0.043   11.630    0.000    0.503    0.559
   .Q5_2              0.495    0.063    7.922    0.000    0.495    0.487
   .Q5_3              0.551    0.057    9.675    0.000    0.551    0.510
   .Q5_5              0.617    0.068    9.021    0.000    0.617    0.550
   .Q5_6              0.353    0.053    6.679    0.000    0.353    0.418
   .Q5_12             0.574    0.059    9.737    0.000    0.574    0.561
   .Q7_14             0.704    0.056   12.463    0.000    0.704    0.648
   .Q6_2              0.474    0.058    8.247    0.000    0.474    0.575
   .Q6_5              0.903    0.086   10.447    0.000    0.903    0.749
   .Q6_6              0.215    0.028    7.754    0.000    0.215    0.348
   .Q6_7              0.170    0.028    6.062    0.000    0.170    0.222
   .Q6_8              0.263    0.037    7.017    0.000    0.263    0.378
   .Q6_11             0.395    0.045    8.692    0.000    0.395    0.416
   .Q7_7              0.599    0.070    8.559    0.000    0.599    0.529
   .Q7_2              0.335    0.037    9.035    0.000    0.335    0.432
   .Q7_4              0.483    0.049    9.925    0.000    0.483    0.545
   .Q7_5              0.357    0.043    8.302    0.000    0.357    0.403
   .Q7_8              0.380    0.048    7.897    0.000    0.380    0.440

# Residual Analysis
out <- residuals(fit5, type="cor.bollen")
kable(out[[2]], format="html", digit=3)%>%
  kable_styling(full_width = T)%>%
  scroll_box(width="100%", height="800px")

	Q4_3	Q4_4	Q4_5	Q4_9	Q4_11	Q4_15	Q4_18	Q5_1	Q5_2	Q5_3	Q5_5	Q5_6	Q5_12	Q7_14	Q6_2	Q6_5	Q6_6	Q6_7	Q6_8	Q6_11	Q7_7	Q7_2	Q7_4	Q7_5	Q7_8
Q4_3	0.000	0.007	-0.023	-0.020	-0.002	-0.006	0.027	0.019	-0.073	0.029	-0.038	-0.017	-0.014	0.022	0.021	0.003	0.019	-0.054	0.000	0.019	0.003	0.005	0.024	-0.035	-0.010
Q4_4	0.007	0.000	0.040	-0.038	0.035	-0.029	0.027	0.009	-0.112	-0.022	-0.020	0.033	-0.048	-0.014	0.023	-0.018	-0.007	-0.078	-0.052	0.018	-0.005	0.032	0.052	-0.045	0.031
Q4_5	-0.023	0.040	0.000	0.008	0.026	-0.002	-0.007	-0.025	-0.044	0.087	-0.050	0.068	-0.014	-0.008	0.079	0.003	-0.005	0.010	-0.024	0.050	0.037	0.048	-0.029	-0.034	0.047
Q4_9	-0.020	-0.038	0.008	0.000	0.033	0.020	-0.009	0.030	0.017	0.059	0.013	0.033	0.088	0.013	0.046	0.055	-0.046	0.012	-0.050	0.026	0.017	0.040	0.005	0.020	0.078
Q4_11	-0.002	0.035	0.026	0.033	0.000	-0.063	-0.035	0.026	-0.070	0.003	-0.077	-0.041	0.002	-0.059	0.001	0.065	-0.019	0.012	0.026	-0.009	0.037	0.027	-0.029	-0.007	0.042
Q4_15	-0.006	-0.029	-0.002	0.020	-0.063	0.000	0.028	0.016	0.021	0.101	0.024	0.017	0.106	0.075	0.044	0.053	-0.047	0.022	-0.023	0.054	-0.043	-0.053	-0.073	-0.069	-0.050
Q4_18	0.027	0.027	-0.007	-0.009	-0.035	0.028	0.000	-0.061	-0.024	-0.012	-0.061	0.009	0.015	-0.061	0.017	0.041	-0.024	0.020	0.001	0.004	-0.102	-0.002	-0.003	-0.051	-0.058
Q5_1	0.019	0.009	-0.025	0.030	0.026	0.016	-0.061	0.000	0.078	0.041	-0.045	-0.069	-0.026	-0.026	0.056	-0.010	0.025	0.005	0.043	0.027	0.022	0.024	-0.004	0.057	-0.012
Q5_2	-0.073	-0.112	-0.044	0.017	-0.070	0.021	-0.024	0.078	0.000	0.041	0.002	-0.020	-0.050	0.011	0.063	0.004	0.041	-0.041	0.022	0.002	-0.071	0.027	-0.075	-0.087	-0.043
Q5_3	0.029	-0.022	0.087	0.059	0.003	0.101	-0.012	0.041	0.041	0.000	0.003	-0.005	-0.042	-0.033	0.087	0.100	-0.036	-0.007	-0.011	-0.037	-0.040	-0.015	-0.073	-0.017	-0.059
Q5_5	-0.038	-0.020	-0.050	0.013	-0.077	0.024	-0.061	-0.045	0.002	0.003	0.000	-0.003	0.017	-0.001	-0.012	0.023	-0.055	-0.055	-0.023	0.024	0.077	0.064	-0.004	-0.002	-0.013
Q5_6	-0.017	0.033	0.068	0.033	-0.041	0.017	0.009	-0.069	-0.020	-0.005	-0.003	0.000	0.039	-0.011	0.045	0.069	0.041	0.005	0.002	0.046	0.017	0.011	0.040	0.013	0.083
Q5_12	-0.014	-0.048	-0.014	0.088	0.002	0.106	0.015	-0.026	-0.050	-0.042	0.017	0.039	0.000	0.060	-0.005	0.036	-0.053	0.031	-0.048	0.039	0.080	0.027	0.020	0.020	0.067
Q7_14	0.022	-0.014	-0.008	0.013	-0.059	0.075	-0.061	-0.026	0.011	-0.033	-0.001	-0.011	0.060	0.000	-0.042	0.003	-0.036	-0.045	-0.053	0.073	0.101	-0.042	-0.056	-0.027	0.022
Q6_2	0.021	0.023	0.079	0.046	0.001	0.044	0.017	0.056	0.063	0.087	-0.012	0.045	-0.005	-0.042	0.000	-0.020	0.024	-0.013	0.000	0.027	-0.002	0.005	0.063	-0.023	-0.078
Q6_5	0.003	-0.018	0.003	0.055	0.065	0.053	0.041	-0.010	0.004	0.100	0.023	0.069	0.036	0.003	-0.020	0.000	-0.002	0.019	-0.044	0.039	0.042	-0.056	-0.033	-0.027	0.020
Q6_6	0.019	-0.007	-0.005	-0.046	-0.019	-0.047	-0.024	0.025	0.041	-0.036	-0.055	0.041	-0.053	-0.036	0.024	-0.002	0.000	0.002	0.008	0.014	-0.022	0.061	-0.030	0.009	-0.044
Q6_7	-0.054	-0.078	0.010	0.012	0.012	0.022	0.020	0.005	-0.041	-0.007	-0.055	0.005	0.031	-0.045	-0.013	0.019	0.002	0.000	0.001	-0.042	-0.018	-0.001	-0.043	0.012	-0.039
Q6_8	0.000	-0.052	-0.024	-0.050	0.026	-0.023	0.001	0.043	0.022	-0.011	-0.023	0.002	-0.048	-0.053	0.000	-0.044	0.008	0.001	0.000	-0.009	-0.008	0.055	0.079	0.065	-0.018
Q6_11	0.019	0.018	0.050	0.026	-0.009	0.054	0.004	0.027	0.002	-0.037	0.024	0.046	0.039	0.073	0.027	0.039	0.014	-0.042	-0.009	0.000	0.020	-0.019	-0.004	-0.006	-0.033
Q7_7	0.003	-0.005	0.037	0.017	0.037	-0.043	-0.102	0.022	-0.071	-0.040	0.077	0.017	0.080	0.101	-0.002	0.042	-0.022	-0.018	-0.008	0.020	0.000	-0.016	-0.013	-0.026	0.018
Q7_2	0.005	0.032	0.048	0.040	0.027	-0.053	-0.002	0.024	0.027	-0.015	0.064	0.011	0.027	-0.042	0.005	-0.056	0.061	-0.001	0.055	-0.019	-0.016	0.000	0.030	-0.009	0.010
Q7_4	0.024	0.052	-0.029	0.005	-0.029	-0.073	-0.003	-0.004	-0.075	-0.073	-0.004	0.040	0.020	-0.056	0.063	-0.033	-0.030	-0.043	0.079	-0.004	-0.013	0.030	0.000	0.041	-0.016
Q7_5	-0.035	-0.045	-0.034	0.020	-0.007	-0.069	-0.051	0.057	-0.087	-0.017	-0.002	0.013	0.020	-0.027	-0.023	-0.027	0.009	0.012	0.065	-0.006	-0.026	-0.009	0.041	0.000	0.039
Q7_8	-0.010	0.031	0.047	0.078	0.042	-0.050	-0.058	-0.012	-0.043	-0.059	-0.013	0.083	0.067	0.022	-0.078	0.020	-0.044	-0.039	-0.018	-0.033	0.018	0.010	-0.016	0.039	0.000

ggcorrplot(out[[2]], type = "lower")

# modification indices
modindices(fit5, minimum.value = 5, sort = TRUE)

      lhs op   rhs   mi    epc sepc.lv sepc.all sepc.nox
360  Q5_6 ~~  Q7_8 9.48  0.071   0.071    0.193    0.193
259 Q4_15 ~~ Q5_12 8.00  0.079   0.079    0.174    0.174
398  Q6_5 ~~  Q7_2 7.47 -0.089  -0.089   -0.162   -0.162
365 Q5_12 ~~  Q6_7 7.15  0.064   0.064    0.206    0.206
406  Q6_6 ~~  Q7_2 7.02  0.046   0.046    0.172    0.172
378 Q7_14 ~~ Q6_11 6.98  0.084   0.084    0.159    0.159
285 Q4_18 ~~  Q7_7 6.84 -0.059  -0.059   -0.169   -0.169
290  Q5_1 ~~  Q5_2 6.79  0.092   0.092    0.184    0.184
260 Q4_15 ~~ Q7_14 6.75  0.076   0.076    0.152    0.152
344  Q5_5 ~~  Q7_2 6.61  0.066   0.066    0.144    0.144
364 Q5_12 ~~  Q6_6 6.57 -0.060  -0.060   -0.171   -0.171
380 Q7_14 ~~  Q7_2 6.53 -0.072  -0.072   -0.149   -0.149
172  Q4_4 ~~  Q4_9 6.50 -0.062  -0.062   -0.164   -0.164
268 Q4_15 ~~  Q7_2 6.30 -0.055  -0.055   -0.157   -0.157
313  Q5_2 ~~  Q6_6 6.17  0.056   0.056    0.170    0.170
293  Q5_1 ~~  Q5_6 6.11 -0.073  -0.073   -0.173   -0.173
234 Q4_11 ~~ Q4_15 5.75 -0.056  -0.056   -0.153   -0.153
420  Q6_8 ~~  Q7_4 5.46  0.050   0.050    0.140    0.140
228  Q4_9 ~~  Q6_8 5.42 -0.048  -0.048   -0.138   -0.138
145    EN =~ Q5_12 5.41  0.451   0.299    0.296    0.296
325  Q5_3 ~~  Q6_2 5.33  0.070   0.070    0.137    0.137
266 Q4_15 ~~ Q6_11 5.31  0.056   0.056    0.148    0.148
392  Q6_2 ~~  Q7_8 5.02 -0.053  -0.053   -0.124   -0.124

DWLS

fit1 <- lavaan::cfa(mod1, data=mydata, ordered=T)

Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
    The variance-covariance matrix of the estimated parameters (vcov)
    does not appear to be positive definite! The smallest eigenvalue
    (= -4.812604e-18) is smaller than zero. This may be a symptom that
    the model is not identified.

summary(fit1, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 69 iterations

  Estimator                                       DWLS
  Optimization method                           NLMINB
  Number of free parameters                        196
                                                      
  Number of observations                           312
                                                      
Model Test User Model:
                                              Standard      Robust
  Test Statistic                              2418.115    2135.032
  Degrees of freedom                               659         659
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.409
  Shift parameter                                          418.362
       simple second-order correction                             

Model Test Baseline Model:

  Test statistic                            105417.247   16551.218
  Degrees of freedom                               703         703
  P-value                                        0.000       0.000
  Scaling correction factor                                  6.607

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.983       0.907
  Tucker-Lewis Index (TLI)                       0.982       0.901
                                                                  
  Robust Comparative Fit Index (CFI)                            NA
  Robust Tucker-Lewis Index (TLI)                               NA

Root Mean Square Error of Approximation:

  RMSEA                                          0.093       0.085
  90 Percent confidence interval - lower         0.089       0.081
  90 Percent confidence interval - upper         0.097       0.089
  P-value RMSEA <= 0.05                          0.000       0.000
                                                                  
  Robust RMSEA                                                  NA
  90 Percent confidence interval - lower                        NA
  90 Percent confidence interval - upper                        NA

Standardized Root Mean Square Residual:

  SRMR                                           0.074       0.074

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model        Unstructured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_1              1.000                               0.818    0.818
    Q4_2              0.961    0.030   32.295    0.000    0.786    0.786
    Q4_3              1.040    0.029   36.428    0.000    0.850    0.850
    Q4_4              1.043    0.028   37.591    0.000    0.853    0.853
    Q4_5              0.981    0.033   29.620    0.000    0.802    0.802
    Q4_8              0.954    0.035   27.564    0.000    0.780    0.780
    Q4_9              0.920    0.037   25.167    0.000    0.752    0.752
    Q4_10             1.009    0.031   32.551    0.000    0.825    0.825
    Q4_11             1.017    0.033   31.152    0.000    0.832    0.832
    Q4_15             0.960    0.030   31.834    0.000    0.785    0.785
    Q4_16             0.914    0.031   29.027    0.000    0.747    0.747
    Q4_17             0.813    0.039   20.918    0.000    0.665    0.665
    Q4_18             1.046    0.029   36.038    0.000    0.855    0.855
  SC =~                                                                 
    Q5_1              1.000                               0.778    0.778
    Q5_2              0.866    0.046   18.736    0.000    0.674    0.674
    Q5_3              0.927    0.052   17.786    0.000    0.721    0.721
    Q5_4              1.057    0.046   22.833    0.000    0.822    0.822
    Q5_5              1.085    0.045   24.149    0.000    0.844    0.844
    Q5_6              1.033    0.049   21.191    0.000    0.804    0.804
    Q5_8              1.005    0.051   19.593    0.000    0.782    0.782
    Q5_12             1.018    0.051   19.822    0.000    0.792    0.792
  IN =~                                                                 
    Q6_1              1.000                               0.757    0.757
    Q6_2              1.110    0.045   24.613    0.000    0.840    0.840
    Q6_3              1.060    0.049   21.673    0.000    0.803    0.803
    Q6_4              1.066    0.046   23.026    0.000    0.806    0.806
    Q6_5              0.731    0.056   12.943    0.000    0.553    0.553
    Q6_6              1.126    0.052   21.717    0.000    0.852    0.852
    Q6_7              1.192    0.058   20.543    0.000    0.902    0.902
    Q6_8              1.121    0.052   21.584    0.000    0.848    0.848
    Q6_11             1.129    0.062   18.324    0.000    0.854    0.854
  EN =~                                                                 
    Q7_2              1.000                               0.826    0.826
    Q7_4              0.846    0.038   22.146    0.000    0.699    0.699
    Q7_5              0.954    0.038   24.957    0.000    0.788    0.788
    Q7_7              0.845    0.044   19.242    0.000    0.698    0.698
    Q7_8              0.966    0.040   24.270    0.000    0.798    0.798
    Q7_12             0.849    0.044   19.253    0.000    0.701    0.701
    Q7_13             0.489    0.055    8.951    0.000    0.404    0.404
    Q7_14             0.812    0.046   17.715    0.000    0.671    0.671

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.382    0.031   12.362    0.000    0.600    0.600
    IN                0.441    0.033   13.331    0.000    0.713    0.713
    EN                0.484    0.028   17.118    0.000    0.716    0.716
  SC ~~                                                                 
    IN                0.353    0.033   10.626    0.000    0.600    0.600
    EN                0.484    0.032   14.992    0.000    0.753    0.753
  IN ~~                                                                 
    EN                0.450    0.034   13.066    0.000    0.720    0.720

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q4_1              0.000                               0.000    0.000
   .Q4_2              0.000                               0.000    0.000
   .Q4_3              0.000                               0.000    0.000
   .Q4_4              0.000                               0.000    0.000
   .Q4_5              0.000                               0.000    0.000
   .Q4_8              0.000                               0.000    0.000
   .Q4_9              0.000                               0.000    0.000
   .Q4_10             0.000                               0.000    0.000
   .Q4_11             0.000                               0.000    0.000
   .Q4_15             0.000                               0.000    0.000
   .Q4_16             0.000                               0.000    0.000
   .Q4_17             0.000                               0.000    0.000
   .Q4_18             0.000                               0.000    0.000
   .Q5_1              0.000                               0.000    0.000
   .Q5_2              0.000                               0.000    0.000
   .Q5_3              0.000                               0.000    0.000
   .Q5_4              0.000                               0.000    0.000
   .Q5_5              0.000                               0.000    0.000
   .Q5_6              0.000                               0.000    0.000
   .Q5_8              0.000                               0.000    0.000
   .Q5_12             0.000                               0.000    0.000
   .Q6_1              0.000                               0.000    0.000
   .Q6_2              0.000                               0.000    0.000
   .Q6_3              0.000                               0.000    0.000
   .Q6_4              0.000                               0.000    0.000
   .Q6_5              0.000                               0.000    0.000
   .Q6_6              0.000                               0.000    0.000
   .Q6_7              0.000                               0.000    0.000
   .Q6_8              0.000                               0.000    0.000
   .Q6_11             0.000                               0.000    0.000
   .Q7_2              0.000                               0.000    0.000
   .Q7_4              0.000                               0.000    0.000
   .Q7_5              0.000                               0.000    0.000
   .Q7_7              0.000                               0.000    0.000
   .Q7_8              0.000                               0.000    0.000
   .Q7_12             0.000                               0.000    0.000
   .Q7_13             0.000                               0.000    0.000
   .Q7_14             0.000                               0.000    0.000
    EL                0.000                               0.000    0.000
    SC                0.000                               0.000    0.000
    IN                0.000                               0.000    0.000
    EN                0.000                               0.000    0.000

Thresholds:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    Q4_1|t1          -1.062    0.088  -12.101    0.000   -1.062   -1.062
    Q4_1|t2           0.153    0.071    2.147    0.032    0.153    0.153
    Q4_1|t3           1.449    0.106   13.658    0.000    1.449    1.449
    Q4_1|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q4_2|t1          -0.993    0.085  -11.632    0.000   -0.993   -0.993
    Q4_2|t2           0.521    0.075    6.972    0.000    0.521    0.521
    Q4_2|t3           1.732    0.127   13.613    0.000    1.732    1.732
    Q4_2|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q4_3|t1          -1.198    0.093  -12.870    0.000   -1.198   -1.198
    Q4_3|t2           0.040    0.071    0.565    0.572    0.040    0.040
    Q4_3|t3           1.449    0.106   13.658    0.000    1.449    1.449
    Q4_3|t4           2.006    0.157   12.746    0.000    2.006    2.006
    Q4_4|t1          -1.150    0.091  -12.627    0.000   -1.150   -1.150
    Q4_4|t2          -0.153    0.071   -2.147    0.032   -0.153   -0.153
    Q4_4|t3           1.426    0.105   13.620    0.000    1.426    1.426
    Q4_4|t4           2.144    0.178   12.067    0.000    2.144    2.144
    Q4_5|t1          -0.930    0.083  -11.142    0.000   -0.930   -0.930
    Q4_5|t2           0.448    0.074    6.082    0.000    0.448    0.448
    Q4_5|t3           1.383    0.102   13.529    0.000    1.383    1.383
    Q4_5|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q4_8|t1          -0.812    0.080  -10.117    0.000   -0.812   -0.812
    Q4_8|t2           0.457    0.074    6.194    0.000    0.457    0.457
    Q4_8|t3           1.547    0.113   13.751    0.000    1.547    1.547
    Q4_8|t4           2.341    0.215   10.896    0.000    2.341    2.341
    Q4_9|t1          -0.955    0.084  -11.340    0.000   -0.955   -0.955
    Q4_9|t2           0.235    0.072    3.275    0.001    0.235    0.235
    Q4_9|t3           1.090    0.089   12.282    0.000    1.090    1.090
    Q4_9|t4           2.070    0.166   12.448    0.000    2.070    2.070
    Q4_10|t1         -1.198    0.093  -12.870    0.000   -1.198   -1.198
    Q4_10|t2         -0.227    0.072   -3.163    0.002   -0.227   -0.227
    Q4_10|t3          1.603    0.117   13.751    0.000    1.603    1.603
    Q4_10|t4          2.232    0.193   11.570    0.000    2.232    2.232
    Q4_11|t1         -1.007    0.086  -11.727    0.000   -1.007   -1.007
    Q4_11|t2          0.032    0.071    0.452    0.651    0.032    0.032
    Q4_11|t3          1.150    0.091   12.627    0.000    1.150    1.150
    Q4_11|t4          2.006    0.157   12.746    0.000    2.006    2.006
    Q4_15|t1         -0.905    0.083  -10.941    0.000   -0.905   -0.905
    Q4_15|t2          0.302    0.072    4.176    0.000    0.302    0.302
    Q4_15|t3          1.342    0.100   13.421    0.000    1.342    1.342
    Q4_15|t4          2.489    0.251    9.915    0.000    2.489    2.489
    Q4_16|t1         -0.823    0.081  -10.222    0.000   -0.823   -0.823
    Q4_16|t2          0.170    0.071    2.373    0.018    0.170    0.170
    Q4_16|t3          1.304    0.098   13.300    0.000    1.304    1.304
    Q4_16|t4          2.341    0.215   10.896    0.000    2.341    2.341
    Q4_17|t1         -0.539    0.075   -7.194    0.000   -0.539   -0.539
    Q4_17|t2          0.521    0.075    6.972    0.000    0.521    0.521
    Q4_17|t3          1.362    0.101   13.477    0.000    1.362    1.362
    Q4_17|t4          2.726    0.330    8.259    0.000    2.726    2.726
    Q4_18|t1         -1.105    0.089  -12.370    0.000   -1.105   -1.105
    Q4_18|t2          0.413    0.073    5.635    0.000    0.413    0.413
    Q4_18|t3          1.521    0.111   13.738    0.000    1.521    1.521
    Q4_18|t4          2.489    0.251    9.915    0.000    2.489    2.489
    Q5_1|t1          -1.076    0.088  -12.192    0.000   -1.076   -1.076
    Q5_1|t2           0.008    0.071    0.113    0.910    0.008    0.008
    Q5_1|t3           1.020    0.086   11.822    0.000    1.020    1.020
    Q5_1|t4           2.144    0.178   12.067    0.000    2.144    2.144
    Q5_2|t1          -1.323    0.099  -13.362    0.000   -1.323   -1.323
    Q5_2|t2          -0.521    0.075   -6.972    0.000   -0.521   -0.521
    Q5_2|t3           0.502    0.074    6.750    0.000    0.502    0.502
    Q5_2|t4           1.697    0.124   13.669    0.000    1.697    1.697
    Q5_3|t1          -1.020    0.086  -11.822    0.000   -1.020   -1.020
    Q5_3|t2          -0.048    0.071   -0.678    0.498   -0.048   -0.048
    Q5_3|t3           0.881    0.082   10.738    0.000    0.881    0.881
    Q5_3|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q5_4|t1          -1.404    0.103  -13.577    0.000   -1.404   -1.404
    Q5_4|t2          -0.835    0.081  -10.326    0.000   -0.835   -0.835
    Q5_4|t3          -0.310    0.072   -4.289    0.000   -0.310   -0.310
    Q5_4|t4           1.182    0.092   12.791    0.000    1.182    1.182
    Q5_5|t1          -1.521    0.111  -13.738    0.000   -1.521   -1.521
    Q5_5|t2          -0.823    0.081  -10.222    0.000   -0.823   -0.823
    Q5_5|t3          -0.302    0.072   -4.176    0.000   -0.302   -0.302
    Q5_5|t4           1.267    0.096   13.166    0.000    1.267    1.267
    Q5_6|t1          -1.449    0.106  -13.658    0.000   -1.449   -1.449
    Q5_6|t2          -0.457    0.074   -6.194    0.000   -0.457   -0.457
    Q5_6|t3           0.812    0.080   10.117    0.000    0.812    0.812
    Q5_6|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q5_8|t1          -1.120    0.090  -12.457    0.000   -1.120   -1.120
    Q5_8|t2          -0.378    0.073   -5.187    0.000   -0.378   -0.378
    Q5_8|t3           0.586    0.076    7.746    0.000    0.586    0.586
    Q5_8|t4           1.664    0.121   13.709    0.000    1.664    1.664
    Q5_12|t1         -1.120    0.090  -12.457    0.000   -1.120   -1.120
    Q5_12|t2         -0.466    0.074   -6.305    0.000   -0.466   -0.466
    Q5_12|t3          0.801    0.080   10.012    0.000    0.801    0.801
    Q5_12|t4          1.697    0.124   13.669    0.000    1.697    1.697
    Q6_1|t1           0.000    0.071    0.000    1.000    0.000    0.000
    Q6_1|t2           1.198    0.093   12.870    0.000    1.198    1.198
    Q6_1|t3           1.633    0.119   13.736    0.000    1.633    1.633
    Q6_1|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q6_2|t1          -0.605    0.076   -7.965    0.000   -0.605   -0.605
    Q6_2|t2           0.790    0.080    9.907    0.000    0.790    0.790
    Q6_2|t3           1.362    0.101   13.477    0.000    1.362    1.362
    Q6_2|t4           2.144    0.178   12.067    0.000    2.144    2.144
    Q6_3|t1          -0.484    0.074   -6.528    0.000   -0.484   -0.484
    Q6_3|t2           0.801    0.080   10.012    0.000    0.801    0.801
    Q6_3|t3           1.426    0.105   13.620    0.000    1.426    1.426
    Q6_3|t4           2.070    0.166   12.448    0.000    2.070    2.070
    Q6_4|t1          -0.625    0.076   -8.185    0.000   -0.625   -0.625
    Q6_4|t2           0.596    0.076    7.856    0.000    0.596    0.596
    Q6_4|t3           1.426    0.105   13.620    0.000    1.426    1.426
    Q6_4|t4           2.006    0.157   12.746    0.000    2.006    2.006
    Q6_5|t1          -0.779    0.079   -9.801    0.000   -0.779   -0.779
    Q6_5|t2           0.293    0.072    4.064    0.000    0.293    0.293
    Q6_5|t3           0.905    0.083   10.941    0.000    0.905    0.905
    Q6_5|t4           1.732    0.127   13.613    0.000    1.732    1.732
    Q6_6|t1          -0.353    0.073   -4.851    0.000   -0.353   -0.353
    Q6_6|t2           1.120    0.090   12.457    0.000    1.120    1.120
    Q6_6|t3           1.732    0.127   13.613    0.000    1.732    1.732
    Q6_6|t4           2.489    0.251    9.915    0.000    2.489    2.489
    Q6_7|t1          -0.674    0.077   -8.729    0.000   -0.674   -0.674
    Q6_7|t2           0.558    0.075    7.415    0.000    0.558    0.558
    Q6_7|t3           1.521    0.111   13.738    0.000    1.521    1.521
    Q6_7|t4           2.341    0.215   10.896    0.000    2.341    2.341
    Q6_8|t1          -0.846    0.081  -10.430    0.000   -0.846   -0.846
    Q6_8|t2           0.577    0.076    7.636    0.000    0.577    0.577
    Q6_8|t3           1.574    0.114   13.756    0.000    1.574    1.574
    Q6_8|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q6_11|t1         -1.047    0.087  -12.009    0.000   -1.047   -1.047
    Q6_11|t2         -0.370    0.073   -5.075    0.000   -0.370   -0.370
    Q6_11|t3          0.980    0.085   11.535    0.000    0.980    0.980
    Q6_11|t4          1.898    0.144   13.171    0.000    1.898    1.898
    Q7_2|t1          -1.076    0.088  -12.192    0.000   -1.076   -1.076
    Q7_2|t2          -0.422    0.073   -5.747    0.000   -0.422   -0.422
    Q7_2|t3           1.215    0.094   12.947    0.000    1.215    1.215
    Q7_2|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q7_4|t1          -1.267    0.096  -13.166    0.000   -1.267   -1.267
    Q7_4|t2          -0.422    0.073   -5.747    0.000   -0.422   -0.422
    Q7_4|t3           0.869    0.082   10.636    0.000    0.869    0.869
    Q7_4|t4           1.851    0.139   13.323    0.000    1.851    1.851
    Q7_5|t1          -1.182    0.092  -12.791    0.000   -1.182   -1.182
    Q7_5|t2          -0.530    0.075   -7.083    0.000   -0.530   -0.530
    Q7_5|t3           0.980    0.085   11.535    0.000    0.980    0.980
    Q7_5|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q7_7|t1          -1.547    0.113  -13.751    0.000   -1.547   -1.547
    Q7_7|t2          -0.917    0.083  -11.041    0.000   -0.917   -0.917
    Q7_7|t3          -0.422    0.073   -5.747    0.000   -0.422   -0.422
    Q7_7|t4           1.062    0.088   12.101    0.000    1.062    1.062
    Q7_8|t1          -1.285    0.097  -13.235    0.000   -1.285   -1.285
    Q7_8|t2          -0.493    0.074   -6.639    0.000   -0.493   -0.493
    Q7_8|t3           0.893    0.082   10.840    0.000    0.893    0.893
    Q7_8|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q7_12|t1         -1.449    0.106  -13.658    0.000   -1.449   -1.449
    Q7_12|t2         -0.930    0.083  -11.142    0.000   -0.930   -0.930
    Q7_12|t3         -0.016    0.071   -0.226    0.821   -0.016   -0.016
    Q7_12|t4          1.150    0.091   12.627    0.000    1.150    1.150
    Q7_13|t1         -1.574    0.114  -13.756    0.000   -1.574   -1.574
    Q7_13|t2         -0.893    0.082  -10.840    0.000   -0.893   -0.893
    Q7_13|t3         -0.137    0.071   -1.921    0.055   -0.137   -0.137
    Q7_13|t4          0.905    0.083   10.941    0.000    0.905    0.905
    Q7_14|t1         -1.633    0.119  -13.736    0.000   -1.633   -1.633
    Q7_14|t2         -1.007    0.086  -11.727    0.000   -1.007   -1.007
    Q7_14|t3         -0.277    0.072   -3.839    0.000   -0.277   -0.277
    Q7_14|t4          0.993    0.085   11.632    0.000    0.993    0.993

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.669    0.034   19.841    0.000    1.000    1.000
    SC                0.605    0.047   12.996    0.000    1.000    1.000
    IN                0.573    0.051   11.214    0.000    1.000    1.000
    EN                0.682    0.043   15.904    0.000    1.000    1.000
   .Q4_1              0.331                               0.331    0.331
   .Q4_2              0.382                               0.382    0.382
   .Q4_3              0.277                               0.277    0.277
   .Q4_4              0.272                               0.272    0.272
   .Q4_5              0.357                               0.357    0.357
   .Q4_8              0.391                               0.391    0.391
   .Q4_9              0.434                               0.434    0.434
   .Q4_10             0.319                               0.319    0.319
   .Q4_11             0.308                               0.308    0.308
   .Q4_15             0.384                               0.384    0.384
   .Q4_16             0.442                               0.442    0.442
   .Q4_17             0.558                               0.558    0.558
   .Q4_18             0.269                               0.269    0.269
   .Q5_1              0.395                               0.395    0.395
   .Q5_2              0.546                               0.546    0.546
   .Q5_3              0.480                               0.480    0.480
   .Q5_4              0.324                               0.324    0.324
   .Q5_5              0.287                               0.287    0.287
   .Q5_6              0.354                               0.354    0.354
   .Q5_8              0.389                               0.389    0.389
   .Q5_12             0.372                               0.372    0.372
   .Q6_1              0.427                               0.427    0.427
   .Q6_2              0.295                               0.295    0.295
   .Q6_3              0.356                               0.356    0.356
   .Q6_4              0.350                               0.350    0.350
   .Q6_5              0.694                               0.694    0.694
   .Q6_6              0.273                               0.273    0.273
   .Q6_7              0.187                               0.187    0.187
   .Q6_8              0.280                               0.280    0.280
   .Q6_11             0.270                               0.270    0.270
   .Q7_2              0.318                               0.318    0.318
   .Q7_4              0.512                               0.512    0.512
   .Q7_5              0.379                               0.379    0.379
   .Q7_7              0.513                               0.513    0.513
   .Q7_8              0.364                               0.364    0.364
   .Q7_12             0.508                               0.508    0.508
   .Q7_13             0.837                               0.837    0.837
   .Q7_14             0.550                               0.550    0.550

Scales y*:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    Q4_1              1.000                               1.000    1.000
    Q4_2              1.000                               1.000    1.000
    Q4_3              1.000                               1.000    1.000
    Q4_4              1.000                               1.000    1.000
    Q4_5              1.000                               1.000    1.000
    Q4_8              1.000                               1.000    1.000
    Q4_9              1.000                               1.000    1.000
    Q4_10             1.000                               1.000    1.000
    Q4_11             1.000                               1.000    1.000
    Q4_15             1.000                               1.000    1.000
    Q4_16             1.000                               1.000    1.000
    Q4_17             1.000                               1.000    1.000
    Q4_18             1.000                               1.000    1.000
    Q5_1              1.000                               1.000    1.000
    Q5_2              1.000                               1.000    1.000
    Q5_3              1.000                               1.000    1.000
    Q5_4              1.000                               1.000    1.000
    Q5_5              1.000                               1.000    1.000
    Q5_6              1.000                               1.000    1.000
    Q5_8              1.000                               1.000    1.000
    Q5_12             1.000                               1.000    1.000
    Q6_1              1.000                               1.000    1.000
    Q6_2              1.000                               1.000    1.000
    Q6_3              1.000                               1.000    1.000
    Q6_4              1.000                               1.000    1.000
    Q6_5              1.000                               1.000    1.000
    Q6_6              1.000                               1.000    1.000
    Q6_7              1.000                               1.000    1.000
    Q6_8              1.000                               1.000    1.000
    Q6_11             1.000                               1.000    1.000
    Q7_2              1.000                               1.000    1.000
    Q7_4              1.000                               1.000    1.000
    Q7_5              1.000                               1.000    1.000
    Q7_7              1.000                               1.000    1.000
    Q7_8              1.000                               1.000    1.000
    Q7_12             1.000                               1.000    1.000
    Q7_13             1.000                               1.000    1.000
    Q7_14             1.000                               1.000    1.000

Modified Model

mod2 <- "
EL =~ Q4_3 + Q4_4 + Q4_5 + Q4_9 + Q4_11 + Q4_15 + Q4_18
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_5 + Q5_6 + Q5_12
IN =~ Q6_2 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_14

EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN
"

fit1 <- lavaan::cfa(mod2, data=mydata, ordered=T)

Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
    The variance-covariance matrix of the estimated parameters (vcov)
    does not appear to be positive definite! The smallest eigenvalue
    (= -6.473428e-18) is smaller than zero. This may be a symptom that
    the model is not identified.

summary(fit1, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 56 iterations

  Estimator                                       DWLS
  Optimization method                           NLMINB
  Number of free parameters                        131
                                                      
  Number of observations                           312
                                                      
Model Test User Model:
                                              Standard      Robust
  Test Statistic                               721.379     903.780
  Degrees of freedom                               269         269
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  0.930
  Shift parameter                                          127.979
       simple second-order correction                             

Model Test Baseline Model:

  Test statistic                             45265.499   10049.784
  Degrees of freedom                               300         300
  P-value                                        0.000       0.000
  Scaling correction factor                                  4.612

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.990       0.935
  Tucker-Lewis Index (TLI)                       0.989       0.927
                                                                  
  Robust Comparative Fit Index (CFI)                            NA
  Robust Tucker-Lewis Index (TLI)                               NA

Root Mean Square Error of Approximation:

  RMSEA                                          0.074       0.087
  90 Percent confidence interval - lower         0.067       0.081
  90 Percent confidence interval - upper         0.080       0.093
  P-value RMSEA <= 0.05                          0.000       0.000
                                                                  
  Robust RMSEA                                                  NA
  90 Percent confidence interval - lower                        NA
  90 Percent confidence interval - upper                        NA

Standardized Root Mean Square Residual:

  SRMR                                           0.061       0.061

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model        Unstructured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_3              1.000                               0.825    0.825
    Q4_4              1.043    0.030   35.333    0.000    0.860    0.860
    Q4_5              0.963    0.033   29.279    0.000    0.794    0.794
    Q4_9              0.921    0.039   23.459    0.000    0.759    0.759
    Q4_11             1.034    0.035   29.534    0.000    0.853    0.853
    Q4_15             0.925    0.033   27.602    0.000    0.762    0.762
    Q4_18             1.019    0.031   32.791    0.000    0.840    0.840
  SC =~                                                                 
    Q5_1              1.000                               0.756    0.756
    Q5_2              0.866    0.049   17.506    0.000    0.655    0.655
    Q5_3              0.919    0.054   16.868    0.000    0.695    0.695
    Q5_5              0.911    0.050   18.410    0.000    0.689    0.689
    Q5_6              1.008    0.054   18.844    0.000    0.763    0.763
    Q5_12             1.016    0.055   18.456    0.000    0.769    0.769
  IN =~                                                                 
    Q6_2              1.000                               0.737    0.737
    Q6_5              0.741    0.057   12.890    0.000    0.546    0.546
    Q6_6              1.147    0.048   23.856    0.000    0.845    0.845
    Q6_7              1.207    0.051   23.562    0.000    0.889    0.889
    Q6_8              1.128    0.046   24.499    0.000    0.831    0.831
    Q6_11             1.145    0.051   22.488    0.000    0.843    0.843
  EN =~                                                                 
    Q7_2              1.000                               0.821    0.821
    Q7_4              0.853    0.037   23.330    0.000    0.700    0.700
    Q7_5              0.963    0.037   26.233    0.000    0.791    0.791
    Q7_7              0.822    0.045   18.379    0.000    0.675    0.675
    Q7_8              0.956    0.039   24.696    0.000    0.785    0.785
    Q7_14             0.759    0.047   16.309    0.000    0.623    0.623

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.428    0.033   12.881    0.000    0.686    0.686
    IN                0.460    0.033   13.834    0.000    0.757    0.757
    EN                0.511    0.030   16.817    0.000    0.755    0.755
  SC ~~                                                                 
    IN                0.394    0.034   11.763    0.000    0.708    0.708
    EN                0.515    0.033   15.681    0.000    0.828    0.828
  IN ~~                                                                 
    EN                0.489    0.032   15.303    0.000    0.808    0.808

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q4_3              0.000                               0.000    0.000
   .Q4_4              0.000                               0.000    0.000
   .Q4_5              0.000                               0.000    0.000
   .Q4_9              0.000                               0.000    0.000
   .Q4_11             0.000                               0.000    0.000
   .Q4_15             0.000                               0.000    0.000
   .Q4_18             0.000                               0.000    0.000
   .Q5_1              0.000                               0.000    0.000
   .Q5_2              0.000                               0.000    0.000
   .Q5_3              0.000                               0.000    0.000
   .Q5_5              0.000                               0.000    0.000
   .Q5_6              0.000                               0.000    0.000
   .Q5_12             0.000                               0.000    0.000
   .Q6_2              0.000                               0.000    0.000
   .Q6_5              0.000                               0.000    0.000
   .Q6_6              0.000                               0.000    0.000
   .Q6_7              0.000                               0.000    0.000
   .Q6_8              0.000                               0.000    0.000
   .Q6_11             0.000                               0.000    0.000
   .Q7_2              0.000                               0.000    0.000
   .Q7_4              0.000                               0.000    0.000
   .Q7_5              0.000                               0.000    0.000
   .Q7_7              0.000                               0.000    0.000
   .Q7_8              0.000                               0.000    0.000
   .Q7_14             0.000                               0.000    0.000
    EL                0.000                               0.000    0.000
    SC                0.000                               0.000    0.000
    IN                0.000                               0.000    0.000
    EN                0.000                               0.000    0.000

Thresholds:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    Q4_3|t1          -1.198    0.093  -12.870    0.000   -1.198   -1.198
    Q4_3|t2           0.040    0.071    0.565    0.572    0.040    0.040
    Q4_3|t3           1.449    0.106   13.658    0.000    1.449    1.449
    Q4_3|t4           2.006    0.157   12.746    0.000    2.006    2.006
    Q4_4|t1          -1.150    0.091  -12.627    0.000   -1.150   -1.150
    Q4_4|t2          -0.153    0.071   -2.147    0.032   -0.153   -0.153
    Q4_4|t3           1.426    0.105   13.620    0.000    1.426    1.426
    Q4_4|t4           2.144    0.178   12.067    0.000    2.144    2.144
    Q4_5|t1          -0.930    0.083  -11.142    0.000   -0.930   -0.930
    Q4_5|t2           0.448    0.074    6.082    0.000    0.448    0.448
    Q4_5|t3           1.383    0.102   13.529    0.000    1.383    1.383
    Q4_5|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q4_9|t1          -0.955    0.084  -11.340    0.000   -0.955   -0.955
    Q4_9|t2           0.235    0.072    3.275    0.001    0.235    0.235
    Q4_9|t3           1.090    0.089   12.282    0.000    1.090    1.090
    Q4_9|t4           2.070    0.166   12.448    0.000    2.070    2.070
    Q4_11|t1         -1.007    0.086  -11.727    0.000   -1.007   -1.007
    Q4_11|t2          0.032    0.071    0.452    0.651    0.032    0.032
    Q4_11|t3          1.150    0.091   12.627    0.000    1.150    1.150
    Q4_11|t4          2.006    0.157   12.746    0.000    2.006    2.006
    Q4_15|t1         -0.905    0.083  -10.941    0.000   -0.905   -0.905
    Q4_15|t2          0.302    0.072    4.176    0.000    0.302    0.302
    Q4_15|t3          1.342    0.100   13.421    0.000    1.342    1.342
    Q4_15|t4          2.489    0.251    9.915    0.000    2.489    2.489
    Q4_18|t1         -1.105    0.089  -12.370    0.000   -1.105   -1.105
    Q4_18|t2          0.413    0.073    5.635    0.000    0.413    0.413
    Q4_18|t3          1.521    0.111   13.738    0.000    1.521    1.521
    Q4_18|t4          2.489    0.251    9.915    0.000    2.489    2.489
    Q5_1|t1          -1.076    0.088  -12.192    0.000   -1.076   -1.076
    Q5_1|t2           0.008    0.071    0.113    0.910    0.008    0.008
    Q5_1|t3           1.020    0.086   11.822    0.000    1.020    1.020
    Q5_1|t4           2.144    0.178   12.067    0.000    2.144    2.144
    Q5_2|t1          -1.323    0.099  -13.362    0.000   -1.323   -1.323
    Q5_2|t2          -0.521    0.075   -6.972    0.000   -0.521   -0.521
    Q5_2|t3           0.502    0.074    6.750    0.000    0.502    0.502
    Q5_2|t4           1.697    0.124   13.669    0.000    1.697    1.697
    Q5_3|t1          -1.020    0.086  -11.822    0.000   -1.020   -1.020
    Q5_3|t2          -0.048    0.071   -0.678    0.498   -0.048   -0.048
    Q5_3|t3           0.881    0.082   10.738    0.000    0.881    0.881
    Q5_3|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q5_5|t1          -1.521    0.111  -13.738    0.000   -1.521   -1.521
    Q5_5|t2          -0.823    0.081  -10.222    0.000   -0.823   -0.823
    Q5_5|t3          -0.302    0.072   -4.176    0.000   -0.302   -0.302
    Q5_5|t4           1.267    0.096   13.166    0.000    1.267    1.267
    Q5_6|t1          -1.449    0.106  -13.658    0.000   -1.449   -1.449
    Q5_6|t2          -0.457    0.074   -6.194    0.000   -0.457   -0.457
    Q5_6|t3           0.812    0.080   10.117    0.000    0.812    0.812
    Q5_6|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q5_12|t1         -1.120    0.090  -12.457    0.000   -1.120   -1.120
    Q5_12|t2         -0.466    0.074   -6.305    0.000   -0.466   -0.466
    Q5_12|t3          0.801    0.080   10.012    0.000    0.801    0.801
    Q5_12|t4          1.697    0.124   13.669    0.000    1.697    1.697
    Q6_2|t1          -0.605    0.076   -7.965    0.000   -0.605   -0.605
    Q6_2|t2           0.790    0.080    9.907    0.000    0.790    0.790
    Q6_2|t3           1.362    0.101   13.477    0.000    1.362    1.362
    Q6_2|t4           2.144    0.178   12.067    0.000    2.144    2.144
    Q6_5|t1          -0.779    0.079   -9.801    0.000   -0.779   -0.779
    Q6_5|t2           0.293    0.072    4.064    0.000    0.293    0.293
    Q6_5|t3           0.905    0.083   10.941    0.000    0.905    0.905
    Q6_5|t4           1.732    0.127   13.613    0.000    1.732    1.732
    Q6_6|t1          -0.353    0.073   -4.851    0.000   -0.353   -0.353
    Q6_6|t2           1.120    0.090   12.457    0.000    1.120    1.120
    Q6_6|t3           1.732    0.127   13.613    0.000    1.732    1.732
    Q6_6|t4           2.489    0.251    9.915    0.000    2.489    2.489
    Q6_7|t1          -0.674    0.077   -8.729    0.000   -0.674   -0.674
    Q6_7|t2           0.558    0.075    7.415    0.000    0.558    0.558
    Q6_7|t3           1.521    0.111   13.738    0.000    1.521    1.521
    Q6_7|t4           2.341    0.215   10.896    0.000    2.341    2.341
    Q6_8|t1          -0.846    0.081  -10.430    0.000   -0.846   -0.846
    Q6_8|t2           0.577    0.076    7.636    0.000    0.577    0.577
    Q6_8|t3           1.574    0.114   13.756    0.000    1.574    1.574
    Q6_8|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q6_11|t1         -1.047    0.087  -12.009    0.000   -1.047   -1.047
    Q6_11|t2         -0.370    0.073   -5.075    0.000   -0.370   -0.370
    Q6_11|t3          0.980    0.085   11.535    0.000    0.980    0.980
    Q6_11|t4          1.898    0.144   13.171    0.000    1.898    1.898
    Q7_2|t1          -1.076    0.088  -12.192    0.000   -1.076   -1.076
    Q7_2|t2          -0.422    0.073   -5.747    0.000   -0.422   -0.422
    Q7_2|t3           1.215    0.094   12.947    0.000    1.215    1.215
    Q7_2|t4           2.232    0.193   11.570    0.000    2.232    2.232
    Q7_4|t1          -1.267    0.096  -13.166    0.000   -1.267   -1.267
    Q7_4|t2          -0.422    0.073   -5.747    0.000   -0.422   -0.422
    Q7_4|t3           0.869    0.082   10.636    0.000    0.869    0.869
    Q7_4|t4           1.851    0.139   13.323    0.000    1.851    1.851
    Q7_5|t1          -1.182    0.092  -12.791    0.000   -1.182   -1.182
    Q7_5|t2          -0.530    0.075   -7.083    0.000   -0.530   -0.530
    Q7_5|t3           0.980    0.085   11.535    0.000    0.980    0.980
    Q7_5|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q7_7|t1          -1.547    0.113  -13.751    0.000   -1.547   -1.547
    Q7_7|t2          -0.917    0.083  -11.041    0.000   -0.917   -0.917
    Q7_7|t3          -0.422    0.073   -5.747    0.000   -0.422   -0.422
    Q7_7|t4           1.062    0.088   12.101    0.000    1.062    1.062
    Q7_8|t1          -1.285    0.097  -13.235    0.000   -1.285   -1.285
    Q7_8|t2          -0.493    0.074   -6.639    0.000   -0.493   -0.493
    Q7_8|t3           0.893    0.082   10.840    0.000    0.893    0.893
    Q7_8|t4           1.769    0.131   13.539    0.000    1.769    1.769
    Q7_14|t1         -1.633    0.119  -13.736    0.000   -1.633   -1.633
    Q7_14|t2         -1.007    0.086  -11.727    0.000   -1.007   -1.007
    Q7_14|t3         -0.277    0.072   -3.839    0.000   -0.277   -0.277
    Q7_14|t4          0.993    0.085   11.632    0.000    0.993    0.993

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.680    0.036   19.131    0.000    1.000    1.000
    SC                0.572    0.045   12.588    0.000    1.000    1.000
    IN                0.543    0.043   12.614    0.000    1.000    1.000
    EN                0.675    0.041   16.526    0.000    1.000    1.000
   .Q4_3              0.320                               0.320    0.320
   .Q4_4              0.261                               0.261    0.261
   .Q4_5              0.369                               0.369    0.369
   .Q4_9              0.423                               0.423    0.423
   .Q4_11             0.272                               0.272    0.272
   .Q4_15             0.419                               0.419    0.419
   .Q4_18             0.294                               0.294    0.294
   .Q5_1              0.428                               0.428    0.428
   .Q5_2              0.571                               0.571    0.571
   .Q5_3              0.517                               0.517    0.517
   .Q5_5              0.525                               0.525    0.525
   .Q5_6              0.418                               0.418    0.418
   .Q5_12             0.409                               0.409    0.409
   .Q6_2              0.457                               0.457    0.457
   .Q6_5              0.702                               0.702    0.702
   .Q6_6              0.286                               0.286    0.286
   .Q6_7              0.210                               0.210    0.210
   .Q6_8              0.309                               0.309    0.309
   .Q6_11             0.289                               0.289    0.289
   .Q7_2              0.325                               0.325    0.325
   .Q7_4              0.509                               0.509    0.509
   .Q7_5              0.375                               0.375    0.375
   .Q7_7              0.544                               0.544    0.544
   .Q7_8              0.383                               0.383    0.383
   .Q7_14             0.612                               0.612    0.612

Scales y*:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    Q4_3              1.000                               1.000    1.000
    Q4_4              1.000                               1.000    1.000
    Q4_5              1.000                               1.000    1.000
    Q4_9              1.000                               1.000    1.000
    Q4_11             1.000                               1.000    1.000
    Q4_15             1.000                               1.000    1.000
    Q4_18             1.000                               1.000    1.000
    Q5_1              1.000                               1.000    1.000
    Q5_2              1.000                               1.000    1.000
    Q5_3              1.000                               1.000    1.000
    Q5_5              1.000                               1.000    1.000
    Q5_6              1.000                               1.000    1.000
    Q5_12             1.000                               1.000    1.000
    Q6_2              1.000                               1.000    1.000
    Q6_5              1.000                               1.000    1.000
    Q6_6              1.000                               1.000    1.000
    Q6_7              1.000                               1.000    1.000
    Q6_8              1.000                               1.000    1.000
    Q6_11             1.000                               1.000    1.000
    Q7_2              1.000                               1.000    1.000
    Q7_4              1.000                               1.000    1.000
    Q7_5              1.000                               1.000    1.000
    Q7_7              1.000                               1.000    1.000
    Q7_8              1.000                               1.000    1.000
    Q7_14             1.000                               1.000    1.000

Final Model with Reliability Estimates

mod3 <- "
EL =~ 1*Q4_3 + lam44*Q4_4 + lam45*Q4_5 + lam49*Q4_9 + lam411*Q4_11 + lam415*Q4_15 + lam418*Q4_18
SC =~ 1*Q5_1 + lam52*Q5_2 + lam53*Q5_3 + lam55*Q5_5 + lam56*Q5_6 + lam512*Q5_12
IN =~ 1*Q6_2 + lam65*Q6_5 + lam66*Q6_6 + lam67*Q6_7 + lam68*Q6_8 + lam611*Q6_11
EN =~ 1*Q7_2 + lam74*Q7_4 + lam75*Q7_5 + lam77*Q7_7 + lam78*Q7_8 + lam714*Q7_14

# Factor covarainces
EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN

# Item residual variances
Q4_3 ~~ psi43*Q4_3
Q4_4 ~~ psi44*Q4_4
Q4_5 ~~ psi45*Q4_5
Q4_9 ~~ psi49*Q4_9
Q4_11 ~~ psi411*Q4_11
Q4_15 ~~ psi415*Q4_15
Q4_18 ~~ psi418*Q4_18
Q5_1 ~~ psi51*Q5_1
Q5_2 ~~ psi52*Q5_2
Q5_3 ~~ psi53*Q5_3
Q5_5 ~~ psi55*Q5_5
Q5_6 ~~ psi56*Q5_6
Q5_12 ~~ psi512*Q5_12
Q6_2 ~~ psi62*Q6_2
Q6_5 ~~ psi65*Q6_5
Q6_6 ~~ psi66*Q6_6
Q6_7 ~~ psi67*Q6_7
Q6_8 ~~ psi68*Q6_8
Q6_11 ~~ psi611*Q6_11
Q7_2 ~~ psi72*Q7_2
Q7_4 ~~ psi74*Q7_4
Q7_5 ~~ psi75*Q7_5
Q7_7 ~~ psi77*Q7_7
Q7_8 ~~ psi78*Q7_8
Q7_14 ~~ psi714*Q7_14

Q4_3 ~~ Q4_4
Q5_5 + Q5_2 ~~ Q5_6
Q6_2 ~~ Q6_8
Q7_7 ~~ Q7_8

# Factor Reliabilities
rEL := (1**2 + lam44**2 + lam45**2 + lam49**2 + lam411**2 + lam415**2 + lam418**2)/(1**2 + lam44**2 + lam45**2 + lam49**2 + lam411**2 + lam415**2 + lam418**2 + psi43 + psi44 + psi45 + psi49 + psi411 + psi415 + psi418)
rSC := (1 + lam52**2 + lam53**2 + lam55**2 + lam56**2 + lam512**2)/(1 + lam52**2 + lam53**2 + lam55**2 + lam56**2 + lam512**2 + psi51 + psi52 + psi53 + psi55 + psi56 + psi512)
rIN := (1**2 + lam65**2 + lam66**2 + lam67**2 + lam68**2 + lam611**2)/(1**2 + lam65**2 + lam66**2 + lam67**2 + lam68**2 + lam611**2 + psi62 + psi65 + psi66 + psi67 + psi68 + psi611)
rEN := (1**2 + lam74**2 + lam75**2 + lam77**2 + lam78**2 + lam714**2)/(1**2 + lam74**2 + lam75**2 + lam77**2 + lam78**2 + lam714**2 + psi72 + psi74 + psi75 + psi77 + psi78 + psi714)
"

fit3 <- lavaan::cfa(mod3, data=mydata, ordered = T)

Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
    Could not compute standard errors! The information matrix could
    not be inverted. This may be a symptom that the model is not
    identified.

Warning in lav_test_satorra_bentler(lavobject = NULL, lavsamplestats = lavsamplestats, : lavaan WARNING: could not invert information matrix needed for robust test statistic

summary(fit3, standardized=T, fit.measures=T)

lavaan 0.6-7 ended normally after 55 iterations

  Estimator                                       DWLS
  Optimization method                           NLMINB
  Number of free parameters                        161
                                                      
  Number of observations                           312
                                                      
Model Test User Model:

Warning in cbind(c1, c2, c3, deparse.level = 0): number of rows of result is not
a multiple of vector length (arg 3)

                                              Standard      Robust
  Test Statistic                               608.692     608.692
  Degrees of freedom                               239         239
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                     NA
  Shift parameter                                                 
                                                            Robust

Model Test Baseline Model:

  Test statistic                             45265.499   10049.784
  Degrees of freedom                               300         300
  P-value                                        0.000       0.000
  Scaling correction factor                                  4.612

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.992       0.962
  Tucker-Lewis Index (TLI)                       0.990       0.952
                                                                  
  Robust Comparative Fit Index (CFI)                            NA
  Robust Tucker-Lewis Index (TLI)                               NA

Root Mean Square Error of Approximation:

  RMSEA                                          0.071       0.071
  90 Percent confidence interval - lower         0.064       0.064
  90 Percent confidence interval - upper         0.077       0.077
  P-value RMSEA <= 0.05                          0.000       0.000
                                                                  
  Robust RMSEA                                                  NA
  90 Percent confidence interval - lower                        NA
  90 Percent confidence interval - upper                        NA

Standardized Root Mean Square Residual:

  SRMR                                           0.058       0.058

Parameter Estimates:

  Standard errors                           Robust.sem
  Information                                 Expected
  Information saturated (h1) model        Unstructured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL =~                                                                 
    Q4_3              1.000                               0.779    0.779
    Q4_4    (lm44)    1.050       NA                      0.819    0.819
    Q4_5    (lm45)    1.022       NA                      0.796    0.796
    Q4_9    (lm49)    0.975       NA                      0.760    0.760
    Q4_11   (l411)    1.098       NA                      0.856    0.856
    Q4_15   (l415)    0.980       NA                      0.763    0.763
    Q4_18   (l418)    1.085       NA                      0.845    0.845
  SC =~                                                                 
    Q5_1              1.000                               0.752    0.752
    Q5_2    (lm52)    0.867       NA                      0.652    0.652
    Q5_3    (lm53)    0.919       NA                      0.691    0.691
    Q5_5    (lm55)    0.867       NA                      0.652    0.652
    Q5_6    (lm56)    0.973       NA                      0.732    0.732
    Q5_12   (l512)    1.017       NA                      0.764    0.764
  IN =~                                                                 
    Q6_2              1.000                               0.704    0.704
    Q6_5    (lm65)    0.773       NA                      0.545    0.545
    Q6_6    (lm66)    1.200       NA                      0.845    0.845
    Q6_7    (lm67)    1.264       NA                      0.890    0.890
    Q6_8    (lm68)    1.148       NA                      0.809    0.809
    Q6_11   (l611)    1.195       NA                      0.842    0.842
  EN =~                                                                 
    Q7_2              1.000                               0.818    0.818
    Q7_4    (lm74)    0.853       NA                      0.698    0.698
    Q7_5    (lm75)    0.963       NA                      0.788    0.788
    Q7_7    (lm77)    0.791       NA                      0.647    0.647
    Q7_8    (lm78)    0.933       NA                      0.764    0.764
    Q7_14   (l714)    0.759       NA                      0.621    0.621

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EL ~~                                                                 
    SC                0.416       NA                      0.710    0.710
    IN                0.426       NA                      0.775    0.775
    EN                0.494       NA                      0.774    0.774
  SC ~~                                                                 
    IN                0.387       NA                      0.730    0.730
    EN                0.526       NA                      0.856    0.856
  IN ~~                                                                 
    EN                0.477       NA                      0.828    0.828
 .Q4_3 ~~                                                               
   .Q4_4              0.164       NA                      0.164    0.455
 .Q5_5 ~~                                                               
   .Q5_6              0.185       NA                      0.185    0.357
 .Q5_2 ~~                                                               
   .Q5_6             -0.005       NA                     -0.005   -0.010
 .Q6_2 ~~                                                               
   .Q6_8              0.153       NA                      0.153    0.367
 .Q7_7 ~~                                                               
   .Q7_8              0.161       NA                      0.161    0.327

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q4_3              0.000                               0.000    0.000
   .Q4_4              0.000                               0.000    0.000
   .Q4_5              0.000                               0.000    0.000
   .Q4_9              0.000                               0.000    0.000
   .Q4_11             0.000                               0.000    0.000
   .Q4_15             0.000                               0.000    0.000
   .Q4_18             0.000                               0.000    0.000
   .Q5_1              0.000                               0.000    0.000
   .Q5_2              0.000                               0.000    0.000
   .Q5_3              0.000                               0.000    0.000
   .Q5_5              0.000                               0.000    0.000
   .Q5_6              0.000                               0.000    0.000
   .Q5_12             0.000                               0.000    0.000
   .Q6_2              0.000                               0.000    0.000
   .Q6_5              0.000                               0.000    0.000
   .Q6_6              0.000                               0.000    0.000
   .Q6_7              0.000                               0.000    0.000
   .Q6_8              0.000                               0.000    0.000
   .Q6_11             0.000                               0.000    0.000
   .Q7_2              0.000                               0.000    0.000
   .Q7_4              0.000                               0.000    0.000
   .Q7_5              0.000                               0.000    0.000
   .Q7_7              0.000                               0.000    0.000
   .Q7_8              0.000                               0.000    0.000
   .Q7_14             0.000                               0.000    0.000
    EL                0.000                               0.000    0.000
    SC                0.000                               0.000    0.000
    IN                0.000                               0.000    0.000
    EN                0.000                               0.000    0.000

Thresholds:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    Q4_3|t1          -1.198       NA                     -1.198   -1.198
    Q4_3|t2           0.040       NA                      0.040    0.040
    Q4_3|t3           1.449       NA                      1.449    1.449
    Q4_3|t4           2.006       NA                      2.006    2.006
    Q4_4|t1          -1.150       NA                     -1.150   -1.150
    Q4_4|t2          -0.153       NA                     -0.153   -0.153
    Q4_4|t3           1.426       NA                      1.426    1.426
    Q4_4|t4           2.144       NA                      2.144    2.144
    Q4_5|t1          -0.930       NA                     -0.930   -0.930
    Q4_5|t2           0.448       NA                      0.448    0.448
    Q4_5|t3           1.383       NA                      1.383    1.383
    Q4_5|t4           2.232       NA                      2.232    2.232
    Q4_9|t1          -0.955       NA                     -0.955   -0.955
    Q4_9|t2           0.235       NA                      0.235    0.235
    Q4_9|t3           1.090       NA                      1.090    1.090
    Q4_9|t4           2.070       NA                      2.070    2.070
    Q4_11|t1         -1.007       NA                     -1.007   -1.007
    Q4_11|t2          0.032       NA                      0.032    0.032
    Q4_11|t3          1.150       NA                      1.150    1.150
    Q4_11|t4          2.006       NA                      2.006    2.006
    Q4_15|t1         -0.905       NA                     -0.905   -0.905
    Q4_15|t2          0.302       NA                      0.302    0.302
    Q4_15|t3          1.342       NA                      1.342    1.342
    Q4_15|t4          2.489       NA                      2.489    2.489
    Q4_18|t1         -1.105       NA                     -1.105   -1.105
    Q4_18|t2          0.413       NA                      0.413    0.413
    Q4_18|t3          1.521       NA                      1.521    1.521
    Q4_18|t4          2.489       NA                      2.489    2.489
    Q5_1|t1          -1.076       NA                     -1.076   -1.076
    Q5_1|t2           0.008       NA                      0.008    0.008
    Q5_1|t3           1.020       NA                      1.020    1.020
    Q5_1|t4           2.144       NA                      2.144    2.144
    Q5_2|t1          -1.323       NA                     -1.323   -1.323
    Q5_2|t2          -0.521       NA                     -0.521   -0.521
    Q5_2|t3           0.502       NA                      0.502    0.502
    Q5_2|t4           1.697       NA                      1.697    1.697
    Q5_3|t1          -1.020       NA                     -1.020   -1.020
    Q5_3|t2          -0.048       NA                     -0.048   -0.048
    Q5_3|t3           0.881       NA                      0.881    0.881
    Q5_3|t4           1.769       NA                      1.769    1.769
    Q5_5|t1          -1.521       NA                     -1.521   -1.521
    Q5_5|t2          -0.823       NA                     -0.823   -0.823
    Q5_5|t3          -0.302       NA                     -0.302   -0.302
    Q5_5|t4           1.267       NA                      1.267    1.267
    Q5_6|t1          -1.449       NA                     -1.449   -1.449
    Q5_6|t2          -0.457       NA                     -0.457   -0.457
    Q5_6|t3           0.812       NA                      0.812    0.812
    Q5_6|t4           1.769       NA                      1.769    1.769
    Q5_12|t1         -1.120       NA                     -1.120   -1.120
    Q5_12|t2         -0.466       NA                     -0.466   -0.466
    Q5_12|t3          0.801       NA                      0.801    0.801
    Q5_12|t4          1.697       NA                      1.697    1.697
    Q6_2|t1          -0.605       NA                     -0.605   -0.605
    Q6_2|t2           0.790       NA                      0.790    0.790
    Q6_2|t3           1.362       NA                      1.362    1.362
    Q6_2|t4           2.144       NA                      2.144    2.144
    Q6_5|t1          -0.779       NA                     -0.779   -0.779
    Q6_5|t2           0.293       NA                      0.293    0.293
    Q6_5|t3           0.905       NA                      0.905    0.905
    Q6_5|t4           1.732       NA                      1.732    1.732
    Q6_6|t1          -0.353       NA                     -0.353   -0.353
    Q6_6|t2           1.120       NA                      1.120    1.120
    Q6_6|t3           1.732       NA                      1.732    1.732
    Q6_6|t4           2.489       NA                      2.489    2.489
    Q6_7|t1          -0.674       NA                     -0.674   -0.674
    Q6_7|t2           0.558       NA                      0.558    0.558
    Q6_7|t3           1.521       NA                      1.521    1.521
    Q6_7|t4           2.341       NA                      2.341    2.341
    Q6_8|t1          -0.846       NA                     -0.846   -0.846
    Q6_8|t2           0.577       NA                      0.577    0.577
    Q6_8|t3           1.574       NA                      1.574    1.574
    Q6_8|t4           2.232       NA                      2.232    2.232
    Q6_11|t1         -1.047       NA                     -1.047   -1.047
    Q6_11|t2         -0.370       NA                     -0.370   -0.370
    Q6_11|t3          0.980       NA                      0.980    0.980
    Q6_11|t4          1.898       NA                      1.898    1.898
    Q7_2|t1          -1.076       NA                     -1.076   -1.076
    Q7_2|t2          -0.422       NA                     -0.422   -0.422
    Q7_2|t3           1.215       NA                      1.215    1.215
    Q7_2|t4           2.232       NA                      2.232    2.232
    Q7_4|t1          -1.267       NA                     -1.267   -1.267
    Q7_4|t2          -0.422       NA                     -0.422   -0.422
    Q7_4|t3           0.869       NA                      0.869    0.869
    Q7_4|t4           1.851       NA                      1.851    1.851
    Q7_5|t1          -1.182       NA                     -1.182   -1.182
    Q7_5|t2          -0.530       NA                     -0.530   -0.530
    Q7_5|t3           0.980       NA                      0.980    0.980
    Q7_5|t4           1.769       NA                      1.769    1.769
    Q7_7|t1          -1.547       NA                     -1.547   -1.547
    Q7_7|t2          -0.917       NA                     -0.917   -0.917
    Q7_7|t3          -0.422       NA                     -0.422   -0.422
    Q7_7|t4           1.062       NA                      1.062    1.062
    Q7_8|t1          -1.285       NA                     -1.285   -1.285
    Q7_8|t2          -0.493       NA                     -0.493   -0.493
    Q7_8|t3           0.893       NA                      0.893    0.893
    Q7_8|t4           1.769       NA                      1.769    1.769
    Q7_14|t1         -1.633       NA                     -1.633   -1.633
    Q7_14|t2         -1.007       NA                     -1.007   -1.007
    Q7_14|t3         -0.277       NA                     -0.277   -0.277
    Q7_14|t4          0.993       NA                      0.993    0.993

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    EL                0.607       NA                      1.000    1.000
    SC                0.565       NA                      1.000    1.000
    IN                0.496       NA                      1.000    1.000
    EN                0.670       NA                      1.000    1.000
   .Q4_3    (ps43)    0.393       NA                      0.393    0.393
   .Q4_4    (ps44)    0.330       NA                      0.330    0.330
   .Q4_5    (ps45)    0.366       NA                      0.366    0.366
   .Q4_9    (ps49)    0.422       NA                      0.422    0.422
   .Q4_11   (p411)    0.267       NA                      0.267    0.267
   .Q4_15   (p415)    0.417       NA                      0.417    0.417
   .Q4_18   (p418)    0.286       NA                      0.286    0.286
   .Q5_1    (ps51)    0.435       NA                      0.435    0.435
   .Q5_2    (ps52)    0.575       NA                      0.575    0.575
   .Q5_3    (ps53)    0.523       NA                      0.523    0.523
   .Q5_5    (ps55)    0.575       NA                      0.575    0.575
   .Q5_6    (ps56)    0.465       NA                      0.465    0.465
   .Q5_12   (p512)    0.416       NA                      0.416    0.416
   .Q6_2    (ps62)    0.504       NA                      0.504    0.504
   .Q6_5    (ps65)    0.703       NA                      0.703    0.703
   .Q6_6    (ps66)    0.285       NA                      0.285    0.285
   .Q6_7    (ps67)    0.208       NA                      0.208    0.208
   .Q6_8    (ps68)    0.346       NA                      0.346    0.346
   .Q6_11   (p611)    0.292       NA                      0.292    0.292
   .Q7_2    (ps72)    0.330       NA                      0.330    0.330
   .Q7_4    (ps74)    0.513       NA                      0.513    0.513
   .Q7_5    (ps75)    0.379       NA                      0.379    0.379
   .Q7_7    (ps77)    0.581       NA                      0.581    0.581
   .Q7_8    (ps78)    0.417       NA                      0.417    0.417
   .Q7_14   (p714)    0.614       NA                      0.614    0.614

Scales y*:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    Q4_3              1.000                               1.000    1.000
    Q4_4              1.000                               1.000    1.000
    Q4_5              1.000                               1.000    1.000
    Q4_9              1.000                               1.000    1.000
    Q4_11             1.000                               1.000    1.000
    Q4_15             1.000                               1.000    1.000
    Q4_18             1.000                               1.000    1.000
    Q5_1              1.000                               1.000    1.000
    Q5_2              1.000                               1.000    1.000
    Q5_3              1.000                               1.000    1.000
    Q5_5              1.000                               1.000    1.000
    Q5_6              1.000                               1.000    1.000
    Q5_12             1.000                               1.000    1.000
    Q6_2              1.000                               1.000    1.000
    Q6_5              1.000                               1.000    1.000
    Q6_6              1.000                               1.000    1.000
    Q6_7              1.000                               1.000    1.000
    Q6_8              1.000                               1.000    1.000
    Q6_11             1.000                               1.000    1.000
    Q7_2              1.000                               1.000    1.000
    Q7_4              1.000                               1.000    1.000
    Q7_5              1.000                               1.000    1.000
    Q7_7              1.000                               1.000    1.000
    Q7_8              1.000                               1.000    1.000
    Q7_14             1.000                               1.000    1.000

Defined Parameters:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    rEL               0.750                               0.664    0.664
    rSC               0.641                               0.536    0.536
    rIN               0.759                               0.641    0.641
    rEN               0.625                               0.552    0.552

MIIV

# get the model implied instrumental variables
miivs(mod1)

Warning in if (trySolve(BetaNA)) {: the condition has length > 1 and only the
first element will be used

Model Equation Information 

 LHS   RHS 
 Q4_2  Q4_1
 Q4_3  Q4_1
 Q4_4  Q4_1
 Q4_5  Q4_1
 Q4_8  Q4_1
 Q4_9  Q4_1
 Q4_10 Q4_1
 Q4_11 Q4_1
 Q4_15 Q4_1
 Q4_16 Q4_1
 Q4_17 Q4_1
 Q4_18 Q4_1
 Q5_2  Q5_1
 Q5_3  Q5_1
 Q5_4  Q5_1
 Q5_5  Q5_1
 Q5_6  Q5_1
 Q5_8  Q5_1
 Q5_12 Q5_1
 Q6_2  Q6_1
 Q6_3  Q6_1
 Q6_4  Q6_1
 Q6_5  Q6_1
 Q6_6  Q6_1
 Q6_7  Q6_1
 Q6_8  Q6_1
 Q6_11 Q6_1
 Q7_4  Q7_2
 Q7_5  Q7_2
 Q7_7  Q7_2
 Q7_8  Q7_2
 Q7_12 Q7_2
 Q7_13 Q7_2
 Q7_14 Q7_2
 MIIVs                                                                                                                                                                                                                            
 Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_2, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_2, Q4_3, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_2, Q4_3, Q4_4, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_2, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_2, Q5_3, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_2, Q5_3, Q5_4, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_2, Q5_3, Q5_4, Q5_5, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_2, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_2, Q6_3, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_2, Q6_3, Q6_4, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_2, Q6_3, Q6_4, Q6_5, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14 
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_7, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_8, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_7, Q7_12, Q7_13, Q7_14
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_7, Q7_8, Q7_13, Q7_14 
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_14 
 Q4_1, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q5_1, Q5_2, Q5_3, Q5_4, Q5_5, Q5_6, Q5_8, Q5_12, Q6_1, Q6_2, Q6_3, Q6_4, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_7, Q7_8, Q7_12, Q7_13

fit1 <- MIIVsem::miive(mod1, data=mydata)

Warning in if (trySolve(BetaNA)) {: the condition has length > 1 and only the
first element will be used

summary(fit1)

MIIVsem (0.5.5) results 

Number of observations                                                    312
Number of equations                                                        34
Estimator                                                           MIIV-2SLS
Standard Errors                                                      standard
Missing                                                              listwise


Parameter Estimates:


STRUCTURAL COEFFICIENTS:
                   Estimate  Std.Err  z-value  P(>|z|)   Sargan   df   P(Chi)
  EL =~                                                                      
    Q4_1              1.000                                                  
    Q4_2              0.821    0.051   16.206    0.000   51.794   35    0.034
    Q4_3              0.981    0.052   18.859    0.000   44.647   35    0.127
    Q4_4              0.955    0.055   17.438    0.000   50.830   35    0.041
    Q4_5              0.821    0.064   12.763    0.000   86.108   35    0.000
    Q4_8              0.749    0.064   11.674    0.000   88.220   35    0.000
    Q4_9              0.750    0.072   10.412    0.000  115.835   35    0.000
    Q4_10             0.800    0.053   15.101    0.000   67.545   35    0.001
    Q4_11             0.912    0.065   13.922    0.000   95.853   35    0.000
    Q4_15             0.790    0.063   12.462    0.000   93.144   35    0.000
    Q4_16             0.841    0.067   12.526    0.000   77.422   35    0.000
    Q4_17             0.657    0.069    9.475    0.000   87.201   35    0.000
    Q4_18             0.845    0.053   15.969    0.000   69.329   35    0.000
  EN =~                                                                      
    Q7_2              1.000                                                  
    Q7_4              0.805    0.067   12.053    0.000   64.839   35    0.002
    Q7_5              0.937    0.067   14.065    0.000   55.025   35    0.017
    Q7_7              0.859    0.078   10.956    0.000   98.286   35    0.000
    Q7_8              0.889    0.066   13.563    0.000   84.433   35    0.000
    Q7_12             0.835    0.082   10.191    0.000  112.128   35    0.000
    Q7_13             0.416    0.088    4.731    0.000  107.430   35    0.000
    Q7_14             0.762    0.081    9.445    0.000  102.089   35    0.000
  IN =~                                                                      
    Q6_1              1.000                                                  
    Q6_2              1.002    0.063   15.957    0.000   81.084   35    0.000
    Q6_3              0.949    0.065   14.561    0.000   78.765   35    0.000
    Q6_4              0.976    0.069   14.114    0.000   79.258   35    0.000
    Q6_5              0.616    0.089    6.923    0.000   63.623   35    0.002
    Q6_6              0.683    0.059   11.525    0.000  122.131   35    0.000
    Q6_7              0.850    0.066   12.801    0.000  118.715   35    0.000
    Q6_8              0.817    0.063   12.886    0.000  100.777   35    0.000
    Q6_11             0.608    0.080    7.556    0.000  132.964   35    0.000
  SC =~                                                                      
    Q5_1              1.000                                                  
    Q5_2              0.846    0.078   10.810    0.000   72.158   35    0.000
    Q5_3              0.944    0.083   11.334    0.000   50.500   35    0.044
    Q5_4              0.813    0.089    9.094    0.000  153.271   35    0.000
    Q5_5              0.834    0.088    9.451    0.000  135.691   35    0.000
    Q5_6              0.774    0.076   10.160    0.000   93.279   35    0.000
    Q5_8              0.823    0.086    9.531    0.000  118.497   35    0.000
    Q5_12             0.798    0.084    9.496    0.000   80.721   35    0.000

INTERCEPTS:
                   Estimate  Std.Err  z-value  P(>|z|)   
    Q4_1              0.000                              
    Q4_10             0.037    0.049    0.749    0.454   
    Q4_11             0.041    0.061    0.680    0.496   
    Q4_15            -0.217    0.059   -3.672    0.000   
    Q4_16            -0.146    0.063   -2.336    0.020   
    Q4_17            -0.497    0.065   -7.707    0.000   
    Q4_18            -0.202    0.049   -4.089    0.000   
    Q4_2             -0.297    0.047   -6.326    0.000   
    Q4_3              0.072    0.048    1.490    0.136   
    Q4_4              0.120    0.051    2.352    0.019   
    Q4_5             -0.245    0.060   -4.092    0.000   
    Q4_8             -0.351    0.060   -5.882    0.000   
    Q4_9             -0.142    0.067   -2.119    0.034   
    Q5_1              0.000                              
    Q5_12             0.183    0.070    2.622    0.009   
    Q5_2              0.360    0.063    5.677    0.000   
    Q5_3              0.041    0.069    0.592    0.554   
    Q5_4              0.844    0.074   11.376    0.000   
    Q5_5              0.847    0.073   11.568    0.000   
    Q5_6              0.217    0.063    3.422    0.001   
    Q5_8              0.233    0.072    3.259    0.001   
    Q6_1              0.000                              
    Q6_11             0.492    0.119    4.123    0.000   
    Q6_2              0.368    0.092    3.984    0.000   
    Q6_3              0.247    0.096    2.560    0.010   
    Q6_4              0.398    0.103    3.881    0.000   
    Q6_5              0.205    0.132    1.549    0.121   
    Q6_6             -0.281    0.088   -3.203    0.001   
    Q6_7              0.235    0.099    2.385    0.017   
    Q6_8              0.232    0.094    2.472    0.013   
    Q7_12             0.676    0.064   10.515    0.000   
    Q7_13             0.640    0.069    9.281    0.000   
    Q7_14             0.829    0.063   13.110    0.000   
    Q7_2              0.000                              
    Q7_4              0.069    0.052    1.325    0.185   
    Q7_5              0.116    0.052    2.214    0.027   
    Q7_7              0.870    0.061   14.172    0.000   
    Q7_8              0.127    0.051    2.491    0.013

# get the model implied instrumental variables
miivs(mod2)

Warning in if (trySolve(BetaNA)) {: the condition has length > 1 and only the
first element will be used

Model Equation Information 

 LHS   RHS 
 Q4_4  Q4_3
 Q4_5  Q4_3
 Q4_9  Q4_3
 Q4_11 Q4_3
 Q4_15 Q4_3
 Q4_18 Q4_3
 Q5_2  Q5_1
 Q5_3  Q5_1
 Q5_5  Q5_1
 Q5_6  Q5_1
 Q5_12 Q5_1
 Q6_5  Q6_2
 Q6_6  Q6_2
 Q6_7  Q6_2
 Q6_8  Q6_2
 Q6_11 Q6_2
 Q7_4  Q7_2
 Q7_5  Q7_2
 Q7_7  Q7_2
 Q7_8  Q7_2
 Q7_14 Q7_2
 MIIVs                                                                                                                                         
 Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_4, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_4, Q4_5, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_4, Q4_5, Q4_9, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14 
 Q4_4, Q4_5, Q4_9, Q4_11, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14 
 Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14 
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_2, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_2, Q5_3, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_2, Q5_3, Q5_5, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_2, Q5_3, Q5_5, Q5_6, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14 
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_6, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_5, Q6_7, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_5, Q6_6, Q6_8, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_5, Q6_6, Q6_7, Q6_11, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_5, Q6_6, Q6_7, Q6_8, Q7_2, Q7_4, Q7_5, Q7_7, Q7_8, Q7_14 
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_5, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_7, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_8, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_7, Q7_14
 Q4_3, Q4_4, Q4_5, Q4_9, Q4_11, Q4_15, Q4_18, Q5_1, Q5_2, Q5_3, Q5_5, Q5_6, Q5_12, Q6_2, Q6_5, Q6_6, Q6_7, Q6_8, Q6_11, Q7_4, Q7_5, Q7_7, Q7_8

fit2 <- MIIVsem::miive(mod2, data=mydata)

Warning in if (trySolve(BetaNA)) {: the condition has length > 1 and only the
first element will be used

summary(fit2)

MIIVsem (0.5.5) results 

Number of observations                                                    312
Number of equations                                                        21
Estimator                                                           MIIV-2SLS
Standard Errors                                                      standard
Missing                                                              listwise


Parameter Estimates:


STRUCTURAL COEFFICIENTS:
                   Estimate  Std.Err  z-value  P(>|z|)   Sargan   df   P(Chi)
  EL =~                                                                      
    Q4_3              1.000                                                  
    Q4_4              0.960    0.053   18.216    0.000   43.971   22    0.004
    Q4_5              0.846    0.062   13.536    0.000   44.843   22    0.003
    Q4_9              0.804    0.074   10.892    0.000   72.552   22    0.000
    Q4_11             0.911    0.067   13.584    0.000   78.001   22    0.000
    Q4_15             0.818    0.063   13.052    0.000   60.948   22    0.000
    Q4_18             0.819    0.052   15.809    0.000   65.806   22    0.000
  EN =~                                                                      
    Q7_2              1.000                                                  
    Q7_4              0.824    0.068   12.141    0.000   43.938   22    0.004
    Q7_5              0.957    0.068   14.083    0.000   43.494   22    0.004
    Q7_7              0.879    0.080   11.028    0.000   64.603   22    0.000
    Q7_8              0.917    0.067   13.704    0.000   63.323   22    0.000
    Q7_14             0.805    0.083    9.745    0.000   54.056   22    0.000
  IN =~                                                                      
    Q6_2              1.000                                                  
    Q6_5              0.726    0.095    7.647    0.000   35.204   22    0.037
    Q6_6              0.836    0.064   13.006    0.000   55.962   22    0.000
    Q6_7              1.010    0.073   13.826    0.000   45.851   22    0.002
    Q6_8              0.976    0.069   14.153    0.000   43.062   22    0.005
    Q6_11             0.859    0.082   10.429    0.000   80.208   22    0.000
  SC =~                                                                      
    Q5_1              1.000                                                  
    Q5_2              0.884    0.083   10.673    0.000   60.459   22    0.000
    Q5_3              0.969    0.087   11.114    0.000   38.133   22    0.018
    Q5_5              0.838    0.091    9.186    0.000   82.529   22    0.000
    Q5_6              0.783    0.079    9.909    0.000   78.947   22    0.000
    Q5_12             0.870    0.089    9.745    0.000   47.835   22    0.001

INTERCEPTS:
                   Estimate  Std.Err  z-value  P(>|z|)   
    Q4_11            -0.035    0.058   -0.599    0.549   
    Q4_15            -0.267    0.055   -4.895    0.000   
    Q4_18            -0.286    0.045   -6.393    0.000   
    Q4_3              0.000                              
    Q4_4              0.043    0.044    0.959    0.338   
    Q4_5             -0.301    0.055   -5.510    0.000   
    Q4_9             -0.175    0.064   -2.723    0.006   
    Q5_1              0.000                              
    Q5_12             0.217    0.072    2.993    0.003   
    Q5_2              0.377    0.065    5.793    0.000   
    Q5_3              0.053    0.071    0.746    0.456   
    Q5_5              0.849    0.074   11.455    0.000   
    Q5_6              0.221    0.064    3.439    0.001   
    Q6_11             0.510    0.095    5.352    0.000   
    Q6_2              0.000                              
    Q6_5              0.085    0.110    0.772    0.440   
    Q6_6             -0.384    0.074   -5.177    0.000   
    Q6_7              0.077    0.084    0.912    0.362   
    Q6_8              0.086    0.078    1.111    0.267   
    Q7_14             0.845    0.064   13.170    0.000   
    Q7_2              0.000                              
    Q7_4              0.076    0.052    1.446    0.148   
    Q7_5              0.123    0.053    2.325    0.020   
    Q7_7              0.877    0.062   14.184    0.000   
    Q7_8              0.137    0.052    2.650    0.008

MIIV Categorical

fit1 <- MIIVsem::miive(
  mod1, data=mydata,
  ordered=c(
    paste0("Q4_",c(1:5,8:11, 15:18)),
    paste0("Q5_",c(1:6, 8, 12)),
    paste0("Q6_",c(1:8, 11)),
    paste0("Q7_",c(2, 4:5, 7:8, 12:14))
  )
)

Warning in if (trySolve(BetaNA)) {: the condition has length > 1 and only the
first element will be used

Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
    The variance-covariance matrix of the estimated parameters (vcov)
    does not appear to be positive definite! The smallest eigenvalue
    (= -3.130187e-16) is smaller than zero. This may be a symptom that
    the model is not identified.

summary(fit1)

MIIVsem (0.5.5) results 

Number of observations                                                    312
Number of equations                                                        34
Estimator                                                     MIIV-2SLS (PIV)
Standard Errors                                                      standard
Missing                                                              listwise


Parameter Estimates:


STRUCTURAL COEFFICIENTS:
                   Estimate  Std.Err  z-value  P(>|z|)   Sargan   df   P(Chi)
  EL =~                                                                      
    Q4_1              1.000                                                  
    Q4_2              0.882    0.035   25.306    0.000                       
    Q4_3              0.960    0.032   29.805    0.000                       
    Q4_4              0.924    0.041   22.677    0.000                       
    Q4_5              0.785    0.043   18.207    0.000                       
    Q4_8              0.721    0.047   15.479    0.000                       
    Q4_9              0.635    0.053   12.088    0.000                       
    Q4_10             0.833    0.037   22.508    0.000                       
    Q4_11             0.798    0.041   19.443    0.000                       
    Q4_15             0.743    0.042   17.567    0.000                       
    Q4_16             0.780    0.044   17.895    0.000                       
    Q4_17             0.605    0.051   11.789    0.000                       
    Q4_18             0.902    0.040   22.539    0.000                       
  EN =~                                                                      
    Q7_2              1.000                                                  
    Q7_4              0.674    0.051   13.149    0.000                       
    Q7_5              0.815    0.048   17.056    0.000                       
    Q7_7              0.643    0.057   11.223    0.000                       
    Q7_8              0.757    0.047   16.076    0.000                       
    Q7_12             0.643    0.060   10.764    0.000                       
    Q7_13             0.307    0.066    4.666    0.000                       
    Q7_14             0.569    0.060    9.434    0.000                       
  IN =~                                                                      
    Q6_1              1.000                                                  
    Q6_2              0.830    0.046   17.916    0.000                       
    Q6_3              0.761    0.048   15.696    0.000                       
    Q6_4              0.777    0.045   17.456    0.000                       
    Q6_5              0.442    0.053    8.279    0.000                       
    Q6_6              0.658    0.052   12.747    0.000                       
    Q6_7              0.738    0.049   14.984    0.000                       
    Q6_8              0.741    0.048   15.284    0.000                       
    Q6_11             0.473    0.060    7.872    0.000                       
  SC =~                                                                      
    Q5_1              1.000                                                  
    Q5_2              0.797    0.064   12.364    0.000                       
    Q5_3              0.844    0.061   13.774    0.000                       
    Q5_4              0.685    0.068   10.101    0.000                       
    Q5_5              0.738    0.072   10.206    0.000                       
    Q5_6              0.738    0.062   11.854    0.000                       
    Q5_8              0.706    0.064   10.959    0.000                       
    Q5_12             0.695    0.066   10.573    0.000

PLS

# transform responses to (-2, 2) scale
#mydata[, 7:63] <- apply(mydata[,7:63], 2, as.factor)
mod.pls <- "
# Hypothesized model
EL =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12
IN =~ Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14

EL ~~ 1*EL + SC + IN + EN
SC ~~ 1*SC + IN + EN
IN ~~ 1*IN + EN
EN ~~ 1*EN

# Penalized terms
# cross loadings
pen() * EL =~  Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12 + Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11 + Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14
pen() * SC =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18 + Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11 + Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14
pen() * IN =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18 + Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12 + Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14
pen() * EN =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18 + Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12 + Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11

"
fit.pls <- lslx::plsem(
  model = mod.pls,
  data = mydata,
  loss = "uls",
  penalty_method = "mcp",
  lambda_grid = seq(.02, .60, .02), 
  delta_grid = c(1.5, 3.0, Inf)
)

An 'lslx' R6 class is initialized via 'data' argument. 
  Response Variables: Q4_1 Q4_2 Q4_3 Q4_4 Q4_5 Q4_8 Q4_9 Q4_10 Q4_11 Q4_15 Q4_16 Q4_17 Q4_18 Q5_1 Q5_2 Q5_3 Q5_4 Q5_5 Q5_6 Q5_8 Q5_12 Q6_1 Q6_2 Q6_3 Q6_4 Q6_5 Q6_6 Q6_7 Q6_8 Q6_11 Q7_2 Q7_4 Q7_5 Q7_7 Q7_8 Q7_12 Q7_13 Q7_14 
  Latent Factors: EL SC IN EN 
WARNING: Algorithm doesn't converge under SOME penalty level.
Please try other optimization parameters or specify better starting values.

summary(fit.pls, selector = "rbic")

General Information                                                             
   number of observations                                 312
   number of complete observations                        312
   number of missing patterns                            none
   number of groups                                         1
   number of responses                                     38
   number of factors                                        4
   number of free coefficients                            120
   number of penalized coefficients                       114

Numerical Conditions                                                             
   selected lambda                                      0.240
   selected delta                                       1.500
   selected step                                         none
   objective value                                      1.912
   objective gradient absolute maximum                  0.001
   objective Hessian convexity                          2.000
   number of iterations                                14.000
   loss value                                           1.438
   number of non-zero coefficients                    140.000
   degrees of freedom                                 639.000
   robust degrees of freedom                          219.565
   scaling factor                                       0.344

Fit Indices                                                             
   root mean square error of approximation (rmsea)      0.000
   comparative fit index (cfi)                          1.000
   non-normed fit index (nnfi)                          1.000
   standardized root mean of residual (srmr)            0.049

Likelihood Ratio Test
                    statistic         df    p-value
   unadjusted         448.511    639.000      1.000
   mean-adjusted     1305.301    639.000      0.000

Root Mean Square Error of Approximation Test
                     estimate      lower      upper
   unadjusted           0.000      0.000      0.000
   mean-adjusted        0.034      0.031      0.037

Coefficient Test (Std.Error = "sandwich")
  Factor Loading
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
     Q4_1<-EL     free     0.636      0.049   12.964    0.000  0.540  0.732
     Q4_2<-EL     free     0.585      0.060    9.670    0.000  0.466  0.704
     Q4_3<-EL     free     0.642      0.050   12.933    0.000  0.545  0.739
     Q4_4<-EL     free     0.665      0.045   14.783    0.000  0.577  0.753
     Q4_5<-EL     free     0.642      0.047   13.795    0.000  0.551  0.734
     Q4_8<-EL     free     0.619      0.046   13.350    0.000  0.528  0.710
     Q4_9<-EL     free     0.656      0.077    8.510    0.000  0.505  0.807
    Q4_10<-EL     free     0.630      0.046   13.744    0.000  0.540  0.719
    Q4_11<-EL     free     0.734      0.079    9.345    0.000  0.580  0.887
    Q4_15<-EL     free     0.646      0.047   13.854    0.000  0.554  0.737
    Q4_16<-EL     free     0.631      0.066    9.508    0.000  0.501  0.761
    Q4_17<-EL     free     0.554      0.050   11.134    0.000  0.457  0.652
    Q4_18<-EL     free     0.651      0.049   13.150    0.000  0.554  0.748
     Q5_1<-EL      pen     0.276      0.067    4.111    0.000  0.144  0.407
     Q5_2<-EL      pen     0.000        -        -        -      -      -  
     Q5_3<-EL      pen     0.054      0.079    0.691    0.490 -0.100  0.209
     Q5_4<-EL      pen     0.000        -        -        -      -      -  
     Q5_5<-EL      pen     0.000        -        -        -      -      -  
     Q5_6<-EL      pen     0.000        -        -        -      -      -  
     Q5_8<-EL      pen     0.000        -        -        -      -      -  
    Q5_12<-EL      pen     0.310      0.079    3.918    0.000  0.155  0.464
     Q6_1<-EL      pen     0.000        -        -        -      -      -  
     Q6_2<-EL      pen     0.000        -        -        -      -      -  
     Q6_3<-EL      pen     0.000        -        -        -      -      -  
     Q6_4<-EL      pen     0.000        -        -        -      -      -  
     Q6_5<-EL      pen     0.000        -        -        -      -      -  
     Q6_6<-EL      pen     0.000        -        -        -      -      -  
     Q6_7<-EL      pen     0.000        -        -        -      -      -  
     Q6_8<-EL      pen     0.000        -        -        -      -      -  
    Q6_11<-EL      pen     0.000        -        -        -      -      -  
     Q7_2<-EL      pen     0.000        -        -        -      -      -  
     Q7_4<-EL      pen     0.000        -        -        -      -      -  
     Q7_5<-EL      pen     0.000        -        -        -      -      -  
     Q7_7<-EL      pen     0.000        -        -        -      -      -  
     Q7_8<-EL      pen     0.211      0.072    2.946    0.003  0.071  0.351
    Q7_12<-EL      pen     0.000        -        -        -      -      -  
    Q7_13<-EL      pen     0.000        -        -        -      -      -  
    Q7_14<-EL      pen     0.000        -        -        -      -      -  
     Q4_1<-SC      pen     0.000        -        -        -      -      -  
     Q4_2<-SC      pen    -0.043      0.043   -0.995    0.320 -0.127  0.042
     Q4_3<-SC      pen     0.000        -        -        -      -      -  
     Q4_4<-SC      pen     0.000        -        -        -      -      -  
     Q4_5<-SC      pen     0.000        -        -        -      -      -  
     Q4_8<-SC      pen     0.000        -        -        -      -      -  
     Q4_9<-SC      pen     0.000        -        -        -      -      -  
    Q4_10<-SC      pen     0.000        -        -        -      -      -  
    Q4_11<-SC      pen     0.000        -        -        -      -      -  
    Q4_15<-SC      pen     0.000        -        -        -      -      -  
    Q4_16<-SC      pen     0.014      0.063    0.214    0.830 -0.110  0.137
    Q4_17<-SC      pen     0.000        -        -        -      -      -  
    Q4_18<-SC      pen     0.000        -        -        -      -      -  
     Q5_1<-SC     free     0.404      0.064    6.266    0.000  0.278  0.530
     Q5_2<-SC     free     0.644      0.058   11.122    0.000  0.530  0.757
     Q5_3<-SC     free     0.646      0.080    8.102    0.000  0.490  0.803
     Q5_4<-SC     free     1.079      0.085   12.645    0.000  0.912  1.247
     Q5_5<-SC     free     1.047      0.080   13.153    0.000  0.891  1.203
     Q5_6<-SC     free     0.705      0.053   13.405    0.000  0.602  0.808
     Q5_8<-SC     free     0.783      0.058   13.482    0.000  0.669  0.897
    Q5_12<-SC     free     0.449      0.079    5.651    0.000  0.293  0.604
     Q6_1<-SC      pen     0.000        -        -        -      -      -  
     Q6_2<-SC      pen     0.000        -        -        -      -      -  
     Q6_3<-SC      pen    -0.031      0.044   -0.709    0.478 -0.117  0.055
     Q6_4<-SC      pen     0.001      0.059    0.014    0.989 -0.116  0.117
     Q6_5<-SC      pen     0.000        -        -        -      -      -  
     Q6_6<-SC      pen     0.000        -        -        -      -      -  
     Q6_7<-SC      pen     0.000        -        -        -      -      -  
     Q6_8<-SC      pen     0.000        -        -        -      -      -  
    Q6_11<-SC      pen     0.000        -        -        -      -      -  
     Q7_2<-SC      pen     0.000        -        -        -      -      -  
     Q7_4<-SC      pen     0.000        -        -        -      -      -  
     Q7_5<-SC      pen     0.000        -        -        -      -      -  
     Q7_7<-SC      pen     0.000        -        -        -      -      -  
     Q7_8<-SC      pen     0.000        -        -        -      -      -  
    Q7_12<-SC      pen     0.000        -        -        -      -      -  
    Q7_13<-SC      pen     0.000        -        -        -      -      -  
    Q7_14<-SC      pen     0.000        -        -        -      -      -  
     Q4_1<-IN      pen     0.000        -        -        -      -      -  
     Q4_2<-IN      pen     0.000        -        -        -      -      -  
     Q4_3<-IN      pen     0.000        -        -        -      -      -  
     Q4_4<-IN      pen     0.000        -        -        -      -      -  
     Q4_5<-IN      pen     0.000        -        -        -      -      -  
     Q4_8<-IN      pen     0.000        -        -        -      -      -  
     Q4_9<-IN      pen     0.000        -        -        -      -      -  
    Q4_10<-IN      pen     0.000        -        -        -      -      -  
    Q4_11<-IN      pen     0.000        -        -        -      -      -  
    Q4_15<-IN      pen     0.000        -        -        -      -      -  
    Q4_16<-IN      pen     0.000        -        -        -      -      -  
    Q4_17<-IN      pen     0.000        -        -        -      -      -  
    Q4_18<-IN      pen     0.000        -        -        -      -      -  
     Q5_1<-IN      pen     0.000        -        -        -      -      -  
     Q5_2<-IN      pen     0.000        -        -        -      -      -  
     Q5_3<-IN      pen     0.000        -        -        -      -      -  
     Q5_4<-IN      pen    -0.337      0.084   -3.998    0.000 -0.502 -0.172
     Q5_5<-IN      pen    -0.310      0.081   -3.806    0.000 -0.469 -0.150
     Q5_6<-IN      pen     0.000        -        -        -      -      -  
     Q5_8<-IN      pen     0.000        -        -        -      -      -  
    Q5_12<-IN      pen     0.000        -        -        -      -      -  
     Q6_1<-IN     free     0.613      0.069    8.947    0.000  0.479  0.748
     Q6_2<-IN     free     0.681      0.056   12.136    0.000  0.571  0.791
     Q6_3<-IN     free     0.701      0.070    9.958    0.000  0.563  0.839
     Q6_4<-IN     free     0.695      0.077    9.003    0.000  0.543  0.846
     Q6_5<-IN     free     0.451      0.085    5.323    0.000  0.285  0.617
     Q6_6<-IN     free     0.635      0.049   12.875    0.000  0.539  0.732
     Q6_7<-IN     free     0.761      0.053   14.244    0.000  0.657  0.866
     Q6_8<-IN     free     0.681      0.049   13.820    0.000  0.584  0.778
    Q6_11<-IN     free     0.366      0.063    5.793    0.000  0.242  0.490
     Q7_2<-IN      pen     0.304      0.075    4.030    0.000  0.156  0.452
     Q7_4<-IN      pen     0.288      0.080    3.614    0.000  0.132  0.445
     Q7_5<-IN      pen     0.271      0.077    3.527    0.000  0.120  0.422
     Q7_7<-IN      pen     0.000        -        -        -      -      -  
     Q7_8<-IN      pen     0.000        -        -        -      -      -  
    Q7_12<-IN      pen     0.000        -        -        -      -      -  
    Q7_13<-IN      pen     0.000        -        -        -      -      -  
    Q7_14<-IN      pen     0.000        -        -        -      -      -  
     Q4_1<-EN      pen     0.000        -        -        -      -      -  
     Q4_2<-EN      pen     0.000        -        -        -      -      -  
     Q4_3<-EN      pen     0.000        -        -        -      -      -  
     Q4_4<-EN      pen     0.000        -        -        -      -      -  
     Q4_5<-EN      pen     0.000        -        -        -      -      -  
     Q4_8<-EN      pen     0.000        -        -        -      -      -  
     Q4_9<-EN      pen     0.036      0.070    0.519    0.604 -0.101  0.174
    Q4_10<-EN      pen     0.000        -        -        -      -      -  
    Q4_11<-EN      pen     0.034      0.083    0.403    0.687 -0.129  0.196
    Q4_15<-EN      pen     0.000        -        -        -      -      -  
    Q4_16<-EN      pen     0.000        -        -        -      -      -  
    Q4_17<-EN      pen     0.000        -        -        -      -      -  
    Q4_18<-EN      pen    -0.045      0.040   -1.125    0.260 -0.125  0.034
     Q5_1<-EN      pen     0.000        -        -        -      -      -  
     Q5_2<-EN      pen     0.000        -        -        -      -      -  
     Q5_3<-EN      pen     0.000        -        -        -      -      -  
     Q5_4<-EN      pen     0.000        -        -        -      -      -  
     Q5_5<-EN      pen     0.000        -        -        -      -      -  
     Q5_6<-EN      pen     0.000        -        -        -      -      -  
     Q5_8<-EN      pen     0.000        -        -        -      -      -  
    Q5_12<-EN      pen     0.000        -        -        -      -      -  
     Q6_1<-EN      pen    -0.073      0.049   -1.499    0.134 -0.168  0.022
     Q6_2<-EN      pen     0.000        -        -        -      -      -  
     Q6_3<-EN      pen     0.000        -        -        -      -      -  
     Q6_4<-EN      pen     0.000        -        -        -      -      -  
     Q6_5<-EN      pen     0.139      0.081    1.719    0.086 -0.019  0.297
     Q6_6<-EN      pen     0.000        -        -        -      -      -  
     Q6_7<-EN      pen     0.007      0.056    0.116    0.908 -0.104  0.117
     Q6_8<-EN      pen     0.000        -        -        -      -      -  
    Q6_11<-EN      pen     0.512      0.059    8.721    0.000  0.397  0.627
     Q7_2<-EN     free     0.455      0.063    7.264    0.000  0.332  0.578
     Q7_4<-EN     free     0.394      0.073    5.375    0.000  0.251  0.538
     Q7_5<-EN     free     0.506      0.070    7.228    0.000  0.369  0.643
     Q7_7<-EN     free     0.777      0.066   11.843    0.000  0.648  0.906
     Q7_8<-EN     free     0.552      0.071    7.796    0.000  0.413  0.691
    Q7_12<-EN     free     0.775      0.066   11.805    0.000  0.647  0.904
    Q7_13<-EN     free     0.449      0.090    4.985    0.000  0.272  0.625
    Q7_14<-EN     free     0.749      0.070   10.696    0.000  0.612  0.887

  Covariance
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
      SC<->EL     free     0.583      0.058   10.134    0.000  0.470  0.696
      IN<->EL     free     0.711      0.045   15.760    0.000  0.622  0.799
      EN<->EL     free     0.627      0.056   11.283    0.000  0.518  0.736
      IN<->SC     free     0.597      0.064    9.306    0.000  0.471  0.723
      EN<->SC     free     0.739      0.038   19.527    0.000  0.665  0.813
      EN<->IN     free     0.474      0.067    7.056    0.000  0.342  0.606

  Variance
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
      EL<->EL    fixed     1.000        -        -        -      -      -  
      SC<->SC    fixed     1.000        -        -        -      -      -  
      IN<->IN    fixed     1.000        -        -        -      -      -  
      EN<->EN    fixed     1.000        -        -        -      -      -  
  Q4_1<->Q4_1     free     0.319      0.039    8.203    0.000  0.243  0.395
  Q4_2<->Q4_2     free     0.298      0.027   10.925    0.000  0.244  0.351
  Q4_3<->Q4_3     free     0.304      0.036    8.522    0.000  0.234  0.375
  Q4_4<->Q4_4     free     0.275      0.028    9.664    0.000  0.219  0.331
  Q4_5<->Q4_5     free     0.344      0.040    8.671    0.000  0.266  0.421
  Q4_8<->Q4_8     free     0.345      0.047    7.400    0.000  0.253  0.436
  Q4_9<->Q4_9     free     0.470      0.050    9.371    0.000  0.372  0.568
Q4_10<->Q4_10     free     0.243      0.029    8.481    0.000  0.187  0.299
Q4_11<->Q4_11     free     0.333      0.039    8.495    0.000  0.256  0.410
Q4_15<->Q4_15     free     0.362      0.041    8.815    0.000  0.281  0.442
Q4_16<->Q4_16     free     0.455      0.042   10.725    0.000  0.372  0.538
Q4_17<->Q4_17     free     0.555      0.049   11.257    0.000  0.459  0.652
Q4_18<->Q4_18     free     0.234      0.024    9.571    0.000  0.186  0.282
  Q5_1<->Q5_1     free     0.534      0.044   12.019    0.000  0.447  0.621
  Q5_2<->Q5_2     free     0.606      0.058   10.486    0.000  0.493  0.720
  Q5_3<->Q5_3     free     0.619      0.058   10.724    0.000  0.506  0.733
  Q5_4<->Q5_4     free     0.365      0.058    6.260    0.000  0.251  0.479
  Q5_5<->Q5_5     free     0.314      0.048    6.493    0.000  0.220  0.409
  Q5_6<->Q5_6     free     0.349      0.045    7.848    0.000  0.262  0.437
  Q5_8<->Q5_8     free     0.532      0.069    7.703    0.000  0.396  0.667
Q5_12<->Q5_12     free     0.564      0.055   10.173    0.000  0.455  0.672
  Q6_1<->Q6_1     free     0.391      0.069    5.710    0.000  0.257  0.526
  Q6_2<->Q6_2     free     0.361      0.048    7.466    0.000  0.266  0.455
  Q6_3<->Q6_3     free     0.387      0.048    8.073    0.000  0.293  0.481
  Q6_4<->Q6_4     free     0.390      0.061    6.366    0.000  0.270  0.510
  Q6_5<->Q6_5     free     0.924      0.085   10.839    0.000  0.757  1.091
  Q6_6<->Q6_6     free     0.214      0.032    6.729    0.000  0.151  0.276
  Q6_7<->Q6_7     free     0.182      0.035    5.162    0.000  0.113  0.251
  Q6_8<->Q6_8     free     0.230      0.036    6.420    0.000  0.160  0.301
Q6_11<->Q6_11     free     0.377      0.041    9.205    0.000  0.297  0.457
  Q7_2<->Q7_2     free     0.355      0.037    9.516    0.000  0.282  0.429
  Q7_4<->Q7_4     free     0.540      0.053   10.264    0.000  0.437  0.643
  Q7_5<->Q7_5     free     0.427      0.052    8.133    0.000  0.324  0.530
  Q7_7<->Q7_7     free     0.533      0.062    8.581    0.000  0.411  0.654
  Q7_8<->Q7_8     free     0.381      0.050    7.569    0.000  0.282  0.479
Q7_12<->Q7_12     free     0.532      0.069    7.710    0.000  0.397  0.668
Q7_13<->Q7_13     free     1.017      0.088   11.529    0.000  0.844  1.189
Q7_14<->Q7_14     free     0.524      0.069    7.652    0.000  0.390  0.659

  Intercept
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
      Q4_1<-1     free    -0.619      0.048  -12.849    0.000 -0.713 -0.524
      Q4_2<-1     free    -0.804      0.044  -18.158    0.000 -0.891 -0.718
      Q4_3<-1     free    -0.535      0.048  -11.168    0.000 -0.629 -0.441
      Q4_4<-1     free    -0.471      0.048   -9.828    0.000 -0.565 -0.377
      Q4_5<-1     free    -0.753      0.049  -15.297    0.000 -0.850 -0.657
      Q4_8<-1     free    -0.814      0.048  -16.850    0.000 -0.909 -0.719
      Q4_9<-1     free    -0.606      0.055  -11.089    0.000 -0.713 -0.499
     Q4_10<-1     free    -0.458      0.045  -10.125    0.000 -0.547 -0.370
     Q4_11<-1     free    -0.522      0.054   -9.709    0.000 -0.628 -0.417
     Q4_15<-1     free    -0.705      0.050  -14.117    0.000 -0.803 -0.607
     Q4_16<-1     free    -0.667      0.053  -12.674    0.000 -0.770 -0.564
     Q4_17<-1     free    -0.904      0.053  -17.190    0.000 -1.007 -0.801
     Q4_18<-1     free    -0.724      0.045  -16.214    0.000 -0.812 -0.637
      Q5_1<-1     free    -0.474      0.054   -8.816    0.000 -0.580 -0.369
      Q5_2<-1     free    -0.042      0.057   -0.728    0.466 -0.154  0.070
      Q5_3<-1     free    -0.407      0.059   -6.915    0.000 -0.522 -0.292
      Q5_4<-1     free     0.458      0.062    7.360    0.000  0.336  0.580
      Q5_5<-1     free     0.452      0.060    7.545    0.000  0.335  0.569
      Q5_6<-1     free    -0.151      0.052   -2.893    0.004 -0.253 -0.049
      Q5_8<-1     free    -0.157      0.061   -2.592    0.010 -0.276 -0.038
     Q5_12<-1     free    -0.196      0.057   -3.415    0.001 -0.308 -0.083
      Q6_1<-1     free    -1.321      0.048  -27.288    0.000 -1.415 -1.226
      Q6_2<-1     free    -0.955      0.051  -18.575    0.000 -1.056 -0.854
      Q6_3<-1     free    -1.006      0.052  -19.253    0.000 -1.109 -0.904
      Q6_4<-1     free    -0.891      0.053  -16.847    0.000 -0.995 -0.787
      Q6_5<-1     free    -0.609      0.062   -9.795    0.000 -0.731 -0.487
      Q6_6<-1     free    -1.183      0.044  -26.590    0.000 -1.270 -1.096
      Q6_7<-1     free    -0.888      0.050  -17.915    0.000 -0.985 -0.791
      Q6_8<-1     free    -0.846      0.047  -17.937    0.000 -0.939 -0.754
     Q6_11<-1     free    -0.311      0.055   -5.630    0.000 -0.419 -0.203
      Q7_2<-1     free    -0.353      0.050   -7.025    0.000 -0.451 -0.254
      Q7_4<-1     free    -0.215      0.053   -4.028    0.000 -0.319 -0.110
      Q7_5<-1     free    -0.215      0.053   -4.028    0.000 -0.319 -0.110
      Q7_7<-1     free     0.567      0.060    9.400    0.000  0.449  0.686
      Q7_8<-1     free    -0.186      0.053   -3.509    0.000 -0.290 -0.082
     Q7_12<-1     free     0.381      0.060    6.328    0.000  0.263  0.500
     Q7_13<-1     free     0.494      0.062    7.900    0.000  0.371  0.616
     Q7_14<-1     free     0.561      0.059    9.507    0.000  0.445  0.677

PLS Categorical

mod.pls <- "
# Hypothesized model
EL =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12
IN =~ Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14

EL ~~ 1*EL + SC + IN + EN
SC ~~ 1*SC + IN + EN
IN ~~ 1*IN + EN
EN ~~ 1*EN

# Penalized terms
# cross loadings
pen() * EL =~  Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12 + Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11 + Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14
pen() * SC =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18 + Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11 + Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14
pen() * IN =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18 + Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12 + Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_12 + Q7_13 + Q7_14
pen() * EN =~ Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_8 + Q4_9 + Q4_10 + Q4_11 + Q4_15 + Q4_16 + Q4_17 + Q4_18 + Q5_1 + Q5_2 + Q5_3 + Q5_4 + Q5_5 + Q5_6 + Q5_8 + Q5_12 + Q6_1 + Q6_2 + Q6_3 + Q6_4 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11

"

fit.pls <- lslx::plsem(
  model = mod.pls,
  data = mydata,
  ordered_variable = c(
    paste0("Q4_",c(1:5,8:11, 15:18)),
    paste0("Q5_",c(1:6, 8, 12)),
    paste0("Q6_",c(1:8, 11)),
    paste0("Q7_",c(2, 4:5, 7:8, 12:14))
  ),
  loss = "uls",
  penalty_method = "mcp"
)

An 'lslx' R6 class is initialized via 'data' argument. 
  Response Variables: Q4_1 Q4_2 Q4_3 Q4_4 Q4_5 Q4_8 Q4_9 Q4_10 Q4_11 Q4_15 Q4_16 Q4_17 Q4_18 Q5_1 Q5_2 Q5_3 Q5_4 Q5_5 Q5_6 Q5_8 Q5_12 Q6_1 Q6_2 Q6_3 Q6_4 Q6_5 Q6_6 Q6_7 Q6_8 Q6_11 Q7_2 Q7_4 Q7_5 Q7_7 Q7_8 Q7_12 Q7_13 Q7_14 
  Latent Factors: EL SC IN EN

Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
    The variance-covariance matrix of the estimated parameters (vcov)
    does not appear to be positive definite! The smallest eigenvalue
    (= -3.130187e-16) is smaller than zero. This may be a symptom that
    the model is not identified.

CONGRATS: Algorithm converges under EVERY specified penalty level.
  Specified Tolerance for Convergence: 0.001 
  Specified Maximal Number of Iterations: 100

summary(fit.pls, selector = "rbic")

General Information                                                             
   number of observations                                 312
   number of complete observations                        312
   number of missing patterns                            none
   number of groups                                         1
   number of responses                                     38
   number of factors                                        4
   number of free coefficients                            196
   number of penalized coefficients                       114

Numerical Conditions                                                             
   selected lambda                                      0.183
   selected delta                                       2.000
   selected step                                         none
   objective value                                      2.376
   objective gradient absolute maximum                  0.001
   objective Hessian convexity                          2.000
   number of iterations                                36.000
   loss value                                           1.748
   number of non-zero coefficients                    232.000
   degrees of freedom                                 623.000
   robust degrees of freedom                          168.217
   scaling factor                                       0.270

Fit Indices                                                             
   root mean square error of approximation (rmsea)      0.000
   comparative fit index (cfi)                          1.000
   non-normed fit index (nnfi)                          1.000
   standardized root mean of residual (srmr)            0.049

Likelihood Ratio Test
                    statistic         df    p-value
   unadjusted         545.258    623.000      0.989
   mean-adjusted     2019.396    623.000      0.000

Root Mean Square Error of Approximation Test
                     estimate      lower      upper
   unadjusted           0.000      0.000      0.000
   mean-adjusted        0.044      0.041      0.047

Coefficient Test (Std.Error = "sandwich")
  Factor Loading
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
     Q4_1<-EL     free     0.805      0.025   32.833    0.000  0.756  0.853
     Q4_2<-EL     free     0.838      0.033   25.163    0.000  0.773  0.903
     Q4_3<-EL     free     0.832      0.031   26.808    0.000  0.772  0.893
     Q4_4<-EL     free     0.841      0.020   42.265    0.000  0.802  0.880
     Q4_5<-EL     free     0.804      0.024   33.059    0.000  0.757  0.852
     Q4_8<-EL     free     0.786      0.027   29.435    0.000  0.734  0.839
     Q4_9<-EL     free     0.610      0.046   13.209    0.000  0.519  0.700
    Q4_10<-EL     free     0.760      0.039   19.583    0.000  0.684  0.836
    Q4_11<-EL     free     0.695      0.048   14.605    0.000  0.602  0.788
    Q4_15<-EL     free     0.751      0.040   18.620    0.000  0.672  0.830
    Q4_16<-EL     free     0.680      0.045   15.011    0.000  0.591  0.768
    Q4_17<-EL     free     0.668      0.033   20.229    0.000  0.603  0.732
    Q4_18<-EL     free     0.914      0.031   29.309    0.000  0.853  0.975
     Q5_1<-EL      pen     0.253      0.068    3.739    0.000  0.120  0.386
     Q5_2<-EL      pen     0.000        -        -        -      -      -  
     Q5_3<-EL      pen     0.110      0.053    2.071    0.038  0.006  0.215
     Q5_4<-EL      pen    -0.152      0.054   -2.827    0.005 -0.257 -0.047
     Q5_5<-EL      pen     0.000        -        -        -      -      -  
     Q5_6<-EL      pen     0.061      0.045    1.357    0.175 -0.027  0.149
     Q5_8<-EL      pen     0.000        -        -        -      -      -  
    Q5_12<-EL      pen     0.000        -        -        -      -      -  
     Q6_1<-EL      pen     0.000        -        -        -      -      -  
     Q6_2<-EL      pen    -0.026      0.058   -0.459    0.646 -0.139  0.086
     Q6_3<-EL      pen    -0.116      0.061   -1.907    0.057 -0.236  0.003
     Q6_4<-EL      pen    -0.147      0.054   -2.714    0.007 -0.252 -0.041
     Q6_5<-EL      pen     0.000        -        -        -      -      -  
     Q6_6<-EL      pen     0.000        -        -        -      -      -  
     Q6_7<-EL      pen     0.000        -        -        -      -      -  
     Q6_8<-EL      pen     0.000        -        -        -      -      -  
    Q6_11<-EL      pen     0.120      0.067    1.805    0.071 -0.010  0.251
     Q7_2<-EL      pen     0.000        -        -        -      -      -  
     Q7_4<-EL      pen     0.000        -        -        -      -      -  
     Q7_5<-EL      pen     0.000        -        -        -      -      -  
     Q7_7<-EL      pen     0.000        -        -        -      -      -  
     Q7_8<-EL      pen     0.215      0.061    3.527    0.000  0.096  0.335
    Q7_12<-EL      pen     0.000        -        -        -      -      -  
    Q7_13<-EL      pen     0.000        -        -        -      -      -  
    Q7_14<-EL      pen    -0.002      0.065   -0.030    0.976 -0.130  0.126
     Q4_1<-SC      pen     0.000        -        -        -      -      -  
     Q4_2<-SC      pen    -0.097      0.048   -2.026    0.043 -0.190 -0.003
     Q4_3<-SC      pen    -0.017      0.046   -0.379    0.705 -0.107  0.073
     Q4_4<-SC      pen     0.000        -        -        -      -      -  
     Q4_5<-SC      pen     0.000        -        -        -      -      -  
     Q4_8<-SC      pen     0.000        -        -        -      -      -  
     Q4_9<-SC      pen     0.000        -        -        -      -      -  
    Q4_10<-SC      pen     0.000        -        -        -      -      -  
    Q4_11<-SC      pen     0.000        -        -        -      -      -  
    Q4_15<-SC      pen     0.046      0.053    0.867    0.386 -0.058  0.150
    Q4_16<-SC      pen     0.085      0.056    1.521    0.128 -0.025  0.195
    Q4_17<-SC      pen     0.000        -        -        -      -      -  
    Q4_18<-SC      pen     0.000        -        -        -      -      -  
     Q5_1<-SC     free     0.455      0.060    7.645    0.000  0.339  0.572
     Q5_2<-SC     free     0.644      0.057   11.215    0.000  0.531  0.756
     Q5_3<-SC     free     0.639      0.047   13.598    0.000  0.547  0.731
     Q5_4<-SC     free     0.967      0.041   23.464    0.000  0.886  1.047
     Q5_5<-SC     free     0.933      0.036   25.636    0.000  0.862  1.004
     Q5_6<-SC     free     0.767      0.041   18.689    0.000  0.687  0.847
     Q5_8<-SC     free     0.803      0.030   26.423    0.000  0.743  0.863
    Q5_12<-SC     free     0.227      0.072    3.132    0.002  0.085  0.369
     Q6_1<-SC      pen     0.000        -        -        -      -      -  
     Q6_2<-SC      pen     0.000        -        -        -      -      -  
     Q6_3<-SC      pen    -0.082      0.048   -1.724    0.085 -0.176  0.011
     Q6_4<-SC      pen     0.020      0.057    0.351    0.726 -0.092  0.132
     Q6_5<-SC      pen     0.000        -        -        -      -      -  
     Q6_6<-SC      pen     0.000        -        -        -      -      -  
     Q6_7<-SC      pen     0.000        -        -        -      -      -  
     Q6_8<-SC      pen     0.000        -        -        -      -      -  
    Q6_11<-SC      pen     0.000        -        -        -      -      -  
     Q7_2<-SC      pen     0.000        -        -        -      -      -  
     Q7_4<-SC      pen     0.000        -        -        -      -      -  
     Q7_5<-SC      pen     0.000        -        -        -      -      -  
     Q7_7<-SC      pen     0.000        -        -        -      -      -  
     Q7_8<-SC      pen     0.000        -        -        -      -      -  
    Q7_12<-SC      pen     0.000        -        -        -      -      -  
    Q7_13<-SC      pen    -0.044      0.082   -0.540    0.589 -0.206  0.117
    Q7_14<-SC      pen     0.000        -        -        -      -      -  
     Q4_1<-IN      pen     0.000        -        -        -      -      -  
     Q4_2<-IN      pen     0.000        -        -        -      -      -  
     Q4_3<-IN      pen     0.000        -        -        -      -      -  
     Q4_4<-IN      pen     0.000        -        -        -      -      -  
     Q4_5<-IN      pen     0.000        -        -        -      -      -  
     Q4_8<-IN      pen     0.000        -        -        -      -      -  
     Q4_9<-IN      pen     0.000        -        -        -      -      -  
    Q4_10<-IN      pen     0.000        -        -        -      -      -  
    Q4_11<-IN      pen     0.000        -        -        -      -      -  
    Q4_15<-IN      pen     0.000        -        -        -      -      -  
    Q4_16<-IN      pen     0.000        -        -        -      -      -  
    Q4_17<-IN      pen     0.000        -        -        -      -      -  
    Q4_18<-IN      pen     0.000        -        -        -      -      -  
     Q5_1<-IN      pen     0.084      0.079    1.066    0.287 -0.071  0.239
     Q5_2<-IN      pen     0.058      0.067    0.859    0.390 -0.074  0.189
     Q5_3<-IN      pen     0.000        -        -        -      -      -  
     Q5_4<-IN      pen    -0.056      0.072   -0.774    0.439 -0.198  0.086
     Q5_5<-IN      pen    -0.149      0.057   -2.591    0.010 -0.261 -0.036
     Q5_6<-IN      pen     0.000        -        -        -      -      -  
     Q5_8<-IN      pen     0.000        -        -        -      -      -  
    Q5_12<-IN      pen     0.289      0.051    5.713    0.000  0.190  0.388
     Q6_1<-IN     free     0.871      0.045   19.558    0.000  0.784  0.958
     Q6_2<-IN     free     0.874      0.041   21.308    0.000  0.793  0.954
     Q6_3<-IN     free     0.958      0.042   23.089    0.000  0.877  1.040
     Q6_4<-IN     free     0.904      0.055   16.535    0.000  0.797  1.011
     Q6_5<-IN     free     0.429      0.059    7.216    0.000  0.312  0.546
     Q6_6<-IN     free     0.856      0.022   38.095    0.000  0.812  0.900
     Q6_7<-IN     free     0.875      0.032   27.162    0.000  0.812  0.938
     Q6_8<-IN     free     0.847      0.022   37.767    0.000  0.803  0.891
    Q6_11<-IN     free     0.305      0.070    4.381    0.000  0.169  0.442
     Q7_2<-IN      pen     0.385      0.070    5.518    0.000  0.249  0.522
     Q7_4<-IN      pen     0.337      0.071    4.724    0.000  0.197  0.476
     Q7_5<-IN      pen     0.290      0.064    4.558    0.000  0.165  0.415
     Q7_7<-IN      pen     0.000        -        -        -      -      -  
     Q7_8<-IN      pen     0.005      0.073    0.067    0.947 -0.137  0.147
    Q7_12<-IN      pen     0.000        -        -        -      -      -  
    Q7_13<-IN      pen     0.000        -        -        -      -      -  
    Q7_14<-IN      pen     0.000        -        -        -      -      -  
     Q4_1<-EN      pen     0.000        -        -        -      -      -  
     Q4_2<-EN      pen     0.000        -        -        -      -      -  
     Q4_3<-EN      pen     0.000        -        -        -      -      -  
     Q4_4<-EN      pen     0.000        -        -        -      -      -  
     Q4_5<-EN      pen     0.000        -        -        -      -      -  
     Q4_8<-EN      pen     0.000        -        -        -      -      -  
     Q4_9<-EN      pen     0.182      0.052    3.492    0.000  0.080  0.285
    Q4_10<-EN      pen     0.094      0.050    1.867    0.062 -0.005  0.192
    Q4_11<-EN      pen     0.185      0.060    3.080    0.002  0.067  0.302
    Q4_15<-EN      pen     0.000        -        -        -      -      -  
    Q4_16<-EN      pen     0.000        -        -        -      -      -  
    Q4_17<-EN      pen     0.000        -        -        -      -      -  
    Q4_18<-EN      pen    -0.075      0.045   -1.654    0.098 -0.164  0.014
     Q5_1<-EN      pen     0.000        -        -        -      -      -  
     Q5_2<-EN      pen     0.000        -        -        -      -      -  
     Q5_3<-EN      pen     0.000        -        -        -      -      -  
     Q5_4<-EN      pen     0.000        -        -        -      -      -  
     Q5_5<-EN      pen     0.000        -        -        -      -      -  
     Q5_6<-EN      pen     0.000        -        -        -      -      -  
     Q5_8<-EN      pen     0.000        -        -        -      -      -  
    Q5_12<-EN      pen     0.305      0.074    4.123    0.000  0.160  0.450
     Q6_1<-EN      pen    -0.164      0.061   -2.666    0.008 -0.284 -0.043
     Q6_2<-EN      pen    -0.056      0.054   -1.043    0.297 -0.162  0.050
     Q6_3<-EN      pen     0.000        -        -        -      -      -  
     Q6_4<-EN      pen     0.000        -        -        -      -      -  
     Q6_5<-EN      pen     0.154      0.058    2.671    0.008  0.041  0.267
     Q6_6<-EN      pen     0.000        -        -        -      -      -  
     Q6_7<-EN      pen     0.034      0.043    0.802    0.423 -0.049  0.117
     Q6_8<-EN      pen     0.000        -        -        -      -      -  
    Q6_11<-EN      pen     0.498      0.051    9.685    0.000  0.397  0.598
     Q7_2<-EN     free     0.508      0.057    8.916    0.000  0.397  0.620
     Q7_4<-EN     free     0.411      0.061    6.744    0.000  0.292  0.531
     Q7_5<-EN     free     0.558      0.052   10.745    0.000  0.457  0.660
     Q7_7<-EN     free     0.757      0.035   21.844    0.000  0.689  0.825
     Q7_8<-EN     free     0.632      0.056   11.228    0.000  0.521  0.742
    Q7_12<-EN     free     0.749      0.035   21.283    0.000  0.680  0.819
    Q7_13<-EN     free     0.466      0.083    5.616    0.000  0.303  0.629
    Q7_14<-EN     free     0.728      0.054   13.567    0.000  0.623  0.833

  Covariance
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
      SC<->EL     free     0.543      0.046   11.721    0.000  0.452  0.634
      IN<->EL     free     0.732      0.028   25.783    0.000  0.676  0.787
      EN<->EL     free     0.599      0.048   12.552    0.000  0.506  0.693
      IN<->SC     free     0.554      0.052   10.697    0.000  0.453  0.656
      EN<->SC     free     0.721      0.030   23.933    0.000  0.662  0.780
      EN<->IN     free     0.537      0.057    9.351    0.000  0.425  0.650

  Variance
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
      EL<->EL    fixed     1.000        -        -        -      -      -  
      SC<->SC    fixed     1.000        -        -        -      -      -  
      IN<->IN    fixed     1.000        -        -        -      -      -  
      EN<->EN    fixed     1.000        -        -        -      -      -  
  Q4_1<->Q4_1    fixed     0.353        -        -        -      -      -  
  Q4_2<->Q4_2    fixed     0.376        -        -        -      -      -  
  Q4_3<->Q4_3    fixed     0.323        -        -        -      -      -  
  Q4_4<->Q4_4    fixed     0.293        -        -        -      -      -  
  Q4_5<->Q4_5    fixed     0.353        -        -        -      -      -  
  Q4_8<->Q4_8    fixed     0.381        -        -        -      -      -  
  Q4_9<->Q4_9    fixed     0.462        -        -        -      -      -  
Q4_10<->Q4_10    fixed     0.327        -        -        -      -      -  
Q4_11<->Q4_11    fixed     0.329        -        -        -      -      -  
Q4_15<->Q4_15    fixed     0.397        -        -        -      -      -  
Q4_16<->Q4_16    fixed     0.468        -        -        -      -      -  
Q4_17<->Q4_17    fixed     0.554        -        -        -      -      -  
Q4_18<->Q4_18    fixed     0.241        -        -        -      -      -  
  Q5_1<->Q5_1    fixed     0.523        -        -        -      -      -  
  Q5_2<->Q5_2    fixed     0.541        -        -        -      -      -  
  Q5_3<->Q5_3    fixed     0.503        -        -        -      -      -  
  Q5_4<->Q5_4    fixed     0.247        -        -        -      -      -  
  Q5_5<->Q5_5    fixed     0.261        -        -        -      -      -  
  Q5_6<->Q5_6    fixed     0.357        -        -        -      -      -  
  Q5_8<->Q5_8    fixed     0.355        -        -        -      -      -  
Q5_12<->Q5_12    fixed     0.505        -        -        -      -      -  
  Q6_1<->Q6_1    fixed     0.368        -        -        -      -      -  
  Q6_2<->Q6_2    fixed     0.318        -        -        -      -      -  
  Q6_3<->Q6_3    fixed     0.301        -        -        -      -      -  
  Q6_4<->Q6_4    fixed     0.338        -        -        -      -      -  
  Q6_5<->Q6_5    fixed     0.721        -        -        -      -      -  
  Q6_6<->Q6_6    fixed     0.267        -        -        -      -      -  
  Q6_7<->Q6_7    fixed     0.201        -        -        -      -      -  
  Q6_8<->Q6_8    fixed     0.282        -        -        -      -      -  
Q6_11<->Q6_11    fixed     0.356        -        -        -      -      -  
  Q7_2<->Q7_2    fixed     0.383        -        -        -      -      -  
  Q7_4<->Q7_4    fixed     0.569        -        -        -      -      -  
  Q7_5<->Q7_5    fixed     0.430        -        -        -      -      -  
  Q7_7<->Q7_7    fixed     0.426        -        -        -      -      -  
  Q7_8<->Q7_8    fixed     0.387        -        -        -      -      -  
Q7_12<->Q7_12    fixed     0.438        -        -        -      -      -  
Q7_13<->Q7_13    fixed     0.811        -        -        -      -      -  
Q7_14<->Q7_14    fixed     0.472        -        -        -      -      -  

  Threshold
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
      Q4_1|t1     free    -1.062      0.088  -12.121    0.000 -1.233 -0.890
      Q4_1|t2     free     0.153      0.071    2.151    0.032  0.014  0.293
      Q4_1|t3     free     1.449      0.106   13.680    0.000  1.241  1.656
      Q4_1|t4     free     2.232      0.193   11.589    0.000  1.854  2.609
      Q4_2|t1     free    -0.993      0.085  -11.650    0.000 -1.161 -0.826
      Q4_2|t2     free     0.521      0.075    6.983    0.000  0.375  0.667
      Q4_2|t3     free     1.732      0.127   13.635    0.000  1.483  1.981
      Q4_2|t4     free     2.232      0.193   11.589    0.000  1.854  2.609
      Q4_3|t1     free    -1.198      0.093  -12.891    0.000 -1.381 -1.016
      Q4_3|t2     free     0.040      0.071    0.566    0.571 -0.099  0.179
      Q4_3|t3     free     1.449      0.106   13.680    0.000  1.241  1.656
      Q4_3|t4     free     2.006      0.157   12.766    0.000  1.698  2.314
      Q4_4|t1     free    -1.150      0.091  -12.648    0.000 -1.329 -0.972
      Q4_4|t2     free    -0.153      0.071   -2.151    0.032 -0.293 -0.014
      Q4_4|t3     free     1.426      0.105   13.642    0.000  1.221  1.631
      Q4_4|t4     free     2.144      0.177   12.087    0.000  1.796  2.491
      Q4_5|t1     free    -0.930      0.083  -11.159    0.000 -1.093 -0.766
      Q4_5|t2     free     0.448      0.074    6.092    0.000  0.304  0.593
      Q4_5|t3     free     1.383      0.102   13.551    0.000  1.183  1.583
      Q4_5|t4     free     2.232      0.193   11.589    0.000  1.854  2.609
      Q4_8|t1     free    -0.812      0.080  -10.133    0.000 -0.969 -0.655
      Q4_8|t2     free     0.457      0.074    6.204    0.000  0.313  0.602
      Q4_8|t3     free     1.547      0.112   13.773    0.000  1.327  1.767
      Q4_8|t4     free     2.341      0.215   10.913    0.000  1.921  2.761
      Q4_9|t1     free    -0.955      0.084  -11.358    0.000 -1.119 -0.790
      Q4_9|t2     free     0.235      0.072    3.281    0.001  0.095  0.376
      Q4_9|t3     free     1.090      0.089   12.301    0.000  0.916  1.264
      Q4_9|t4     free     2.070      0.166   12.468    0.000  1.745  2.395
     Q4_10|t1     free    -1.198      0.093  -12.891    0.000 -1.381 -1.016
     Q4_10|t2     free    -0.227      0.072   -3.168    0.002 -0.367 -0.087
     Q4_10|t3     free     1.603      0.116   13.773    0.000  1.375  1.831
     Q4_10|t4     free     2.232      0.193   11.589    0.000  1.854  2.609
     Q4_11|t1     free    -1.007      0.086  -11.746    0.000 -1.175 -0.839
     Q4_11|t2     free     0.032      0.071    0.453    0.651 -0.107  0.171
     Q4_11|t3     free     1.150      0.091   12.648    0.000  0.972  1.329
     Q4_11|t4     free     2.006      0.157   12.766    0.000  1.698  2.314
     Q4_15|t1     free    -0.905      0.083  -10.958    0.000 -1.067 -0.743
     Q4_15|t2     free     0.302      0.072    4.183    0.000  0.160  0.443
     Q4_15|t3     free     1.342      0.100   13.443    0.000  1.147  1.538
     Q4_15|t4     free     2.489      0.251    9.931    0.000  1.998  2.980
     Q4_16|t1     free    -0.823      0.080  -10.238    0.000 -0.981 -0.666
     Q4_16|t2     free     0.170      0.071    2.377    0.017  0.030  0.309
     Q4_16|t3     free     1.304      0.098   13.321    0.000  1.112  1.496
     Q4_16|t4     free     2.341      0.215   10.913    0.000  1.921  2.761
     Q4_17|t1     free    -0.539      0.075   -7.205    0.000 -0.686 -0.393
     Q4_17|t2     free     0.521      0.075    6.983    0.000  0.375  0.667
     Q4_17|t3     free     1.362      0.101   13.499    0.000  1.165  1.560
     Q4_17|t4     free     2.726      0.330    8.272    0.000  2.080  3.372
     Q4_18|t1     free    -1.105      0.089  -12.390    0.000 -1.280 -0.930
     Q4_18|t2     free     0.413      0.073    5.644    0.000  0.270  0.557
     Q4_18|t3     free     1.521      0.111   13.760    0.000  1.305  1.738
     Q4_18|t4     free     2.489      0.251    9.931    0.000  1.998  2.980
      Q5_1|t1     free    -1.076      0.088  -12.212    0.000 -1.248 -0.903
      Q5_1|t2     free     0.008      0.071    0.113    0.910 -0.131  0.147
      Q5_1|t3     free     1.020      0.086   11.841    0.000  0.851  1.189
      Q5_1|t4     free     2.144      0.177   12.087    0.000  1.796  2.491
      Q5_2|t1     free    -1.323      0.099  -13.384    0.000 -1.517 -1.129
      Q5_2|t2     free    -0.521      0.075   -6.983    0.000 -0.667 -0.375
      Q5_2|t3     free     0.502      0.074    6.761    0.000  0.357  0.648
      Q5_2|t4     free     1.697      0.124   13.691    0.000  1.454  1.940
      Q5_3|t1     free    -1.020      0.086  -11.841    0.000 -1.189 -0.851
      Q5_3|t2     free    -0.048      0.071   -0.679    0.497 -0.187  0.091
      Q5_3|t3     free     0.881      0.082   10.755    0.000  0.721  1.042
      Q5_3|t4     free     1.769      0.130   13.561    0.000  1.513  2.024
      Q5_4|t1     free    -1.404      0.103  -13.598    0.000 -1.607 -1.202
      Q5_4|t2     free    -0.835      0.081  -10.342    0.000 -0.993 -0.677
      Q5_4|t3     free    -0.310      0.072   -4.296    0.000 -0.452 -0.169
      Q5_4|t4     free     1.182      0.092   12.812    0.000  1.001  1.363
      Q5_5|t1     free    -1.521      0.111  -13.760    0.000 -1.738 -1.305
      Q5_5|t2     free    -0.823      0.080  -10.238    0.000 -0.981 -0.666
      Q5_5|t3     free    -0.302      0.072   -4.183    0.000 -0.443 -0.160
      Q5_5|t4     free     1.267      0.096   13.188    0.000  1.079  1.455
      Q5_6|t1     free    -1.449      0.106  -13.680    0.000 -1.656 -1.241
      Q5_6|t2     free    -0.457      0.074   -6.204    0.000 -0.602 -0.313
      Q5_6|t3     free     0.812      0.080   10.133    0.000  0.655  0.969
      Q5_6|t4     free     1.769      0.130   13.561    0.000  1.513  2.024
      Q5_8|t1     free    -1.120      0.090  -12.477    0.000 -1.296 -0.944
      Q5_8|t2     free    -0.378      0.073   -5.196    0.000 -0.521 -0.236
      Q5_8|t3     free     0.586      0.076    7.758    0.000  0.438  0.734
      Q5_8|t4     free     1.664      0.121   13.731    0.000  1.426  1.901
     Q5_12|t1     free    -1.120      0.090  -12.477    0.000 -1.296 -0.944
     Q5_12|t2     free    -0.466      0.074   -6.315    0.000 -0.611 -0.322
     Q5_12|t3     free     0.801      0.080   10.028    0.000  0.645  0.958
     Q5_12|t4     free     1.697      0.124   13.691    0.000  1.454  1.940
      Q6_1|t1     free     0.000      0.071    0.000    1.000 -0.139  0.139
      Q6_1|t2     free     1.198      0.093   12.891    0.000  1.016  1.381
      Q6_1|t3     free     1.633      0.119   13.758    0.000  1.400  1.865
      Q6_1|t4     free     2.232      0.193   11.589    0.000  1.854  2.609
      Q6_2|t1     free    -0.605      0.076   -7.978    0.000 -0.754 -0.457
      Q6_2|t2     free     0.790      0.080    9.923    0.000  0.634  0.946
      Q6_2|t3     free     1.362      0.101   13.499    0.000  1.165  1.560
      Q6_2|t4     free     2.144      0.177   12.087    0.000  1.796  2.491
      Q6_3|t1     free    -0.484      0.074   -6.538    0.000 -0.629 -0.339
      Q6_3|t2     free     0.801      0.080   10.028    0.000  0.645  0.958
      Q6_3|t3     free     1.426      0.105   13.642    0.000  1.221  1.631
      Q6_3|t4     free     2.070      0.166   12.468    0.000  1.745  2.395
      Q6_4|t1     free    -0.625      0.076   -8.198    0.000 -0.774 -0.475
      Q6_4|t2     free     0.596      0.076    7.868    0.000  0.447  0.744
      Q6_4|t3     free     1.426      0.105   13.642    0.000  1.221  1.631
      Q6_4|t4     free     2.006      0.157   12.766    0.000  1.698  2.314
      Q6_5|t1     free    -0.779      0.079   -9.817    0.000 -0.935 -0.624
      Q6_5|t2     free     0.293      0.072    4.070    0.000  0.152  0.435
      Q6_5|t3     free     0.905      0.083   10.958    0.000  0.743  1.067
      Q6_5|t4     free     1.732      0.127   13.635    0.000  1.483  1.981
      Q6_6|t1     free    -0.353      0.073   -4.859    0.000 -0.495 -0.210
      Q6_6|t2     free     1.120      0.090   12.477    0.000  0.944  1.296
      Q6_6|t3     free     1.732      0.127   13.635    0.000  1.483  1.981
      Q6_6|t4     free     2.489      0.251    9.931    0.000  1.998  2.980
      Q6_7|t1     free    -0.674      0.077   -8.743    0.000 -0.826 -0.523
      Q6_7|t2     free     0.558      0.075    7.427    0.000  0.411  0.705
      Q6_7|t3     free     1.521      0.111   13.760    0.000  1.305  1.738
      Q6_7|t4     free     2.341      0.215   10.913    0.000  1.921  2.761
      Q6_8|t1     free    -0.846      0.081  -10.446    0.000 -1.005 -0.687
      Q6_8|t2     free     0.577      0.075    7.648    0.000  0.429  0.725
      Q6_8|t3     free     1.574      0.114   13.778    0.000  1.350  1.798
      Q6_8|t4     free     2.232      0.193   11.589    0.000  1.854  2.609
     Q6_11|t1     free    -1.047      0.087  -12.028    0.000 -1.218 -0.877
     Q6_11|t2     free    -0.370      0.073   -5.083    0.000 -0.512 -0.227
     Q6_11|t3     free     0.980      0.085   11.554    0.000  0.814  1.147
     Q6_11|t4     free     1.898      0.144   13.192    0.000  1.616  2.180
      Q7_2|t1     free    -1.076      0.088  -12.212    0.000 -1.248 -0.903
      Q7_2|t2     free    -0.422      0.073   -5.756    0.000 -0.566 -0.278
      Q7_2|t3     free     1.215      0.094   12.968    0.000  1.031  1.399
      Q7_2|t4     free     2.232      0.193   11.589    0.000  1.854  2.609
      Q7_4|t1     free    -1.267      0.096  -13.188    0.000 -1.455 -1.079
      Q7_4|t2     free    -0.422      0.073   -5.756    0.000 -0.566 -0.278
      Q7_4|t3     free     0.869      0.082   10.653    0.000  0.709  1.029
      Q7_4|t4     free     1.851      0.139   13.344    0.000  1.580  2.123
      Q7_5|t1     free    -1.182      0.092  -12.812    0.000 -1.363 -1.001
      Q7_5|t2     free    -0.530      0.075   -7.095    0.000 -0.676 -0.384
      Q7_5|t3     free     0.980      0.085   11.554    0.000  0.814  1.147
      Q7_5|t4     free     1.769      0.130   13.561    0.000  1.513  2.024
      Q7_7|t1     free    -1.547      0.112  -13.773    0.000 -1.767 -1.327
      Q7_7|t2     free    -0.917      0.083  -11.059    0.000 -1.080 -0.755
      Q7_7|t3     free    -0.422      0.073   -5.756    0.000 -0.566 -0.278
      Q7_7|t4     free     1.062      0.088   12.121    0.000  0.890  1.233
      Q7_8|t1     free    -1.285      0.097  -13.256    0.000 -1.475 -1.095
      Q7_8|t2     free    -0.493      0.074   -6.650    0.000 -0.639 -0.348
      Q7_8|t3     free     0.893      0.082   10.857    0.000  0.732  1.054
      Q7_8|t4     free     1.769      0.130   13.561    0.000  1.513  2.024
     Q7_12|t1     free    -1.449      0.106  -13.680    0.000 -1.656 -1.241
     Q7_12|t2     free    -0.930      0.083  -11.159    0.000 -1.093 -0.766
     Q7_12|t3     free    -0.016      0.071   -0.226    0.821 -0.155  0.123
     Q7_12|t4     free     1.150      0.091   12.648    0.000  0.972  1.329
     Q7_13|t1     free    -1.574      0.114  -13.778    0.000 -1.798 -1.350
     Q7_13|t2     free    -0.893      0.082  -10.857    0.000 -1.054 -0.732
     Q7_13|t3     free    -0.137      0.071   -1.924    0.054 -0.277  0.003
     Q7_13|t4     free     0.905      0.083   10.958    0.000  0.743  1.067
     Q7_14|t1     free    -1.633      0.119  -13.758    0.000 -1.865 -1.400
     Q7_14|t2     free    -1.007      0.086  -11.746    0.000 -1.175 -0.839
     Q7_14|t3     free    -0.277      0.072   -3.845    0.000 -0.418 -0.136
     Q7_14|t4     free     0.993      0.085   11.650    0.000  0.826  1.161

  Scale
                  type  estimate  std.error  z-value  P(>|z|)  lower  upper
   Q4_1**Q4_1    fixed     1.000        -        -        -      -      -  
   Q4_2**Q4_2    fixed     1.000        -        -        -      -      -  
   Q4_3**Q4_3    fixed     1.000        -        -        -      -      -  
   Q4_4**Q4_4    fixed     1.000        -        -        -      -      -  
   Q4_5**Q4_5    fixed     1.000        -        -        -      -      -  
   Q4_8**Q4_8    fixed     1.000        -        -        -      -      -  
   Q4_9**Q4_9    fixed     1.000        -        -        -      -      -  
 Q4_10**Q4_10    fixed     1.000        -        -        -      -      -  
 Q4_11**Q4_11    fixed     1.000        -        -        -      -      -  
 Q4_15**Q4_15    fixed     1.000        -        -        -      -      -  
 Q4_16**Q4_16    fixed     1.000        -        -        -      -      -  
 Q4_17**Q4_17    fixed     1.000        -        -        -      -      -  
 Q4_18**Q4_18    fixed     1.000        -        -        -      -      -  
   Q5_1**Q5_1    fixed     1.000        -        -        -      -      -  
   Q5_2**Q5_2    fixed     1.000        -        -        -      -      -  
   Q5_3**Q5_3    fixed     1.000        -        -        -      -      -  
   Q5_4**Q5_4    fixed     1.000        -        -        -      -      -  
   Q5_5**Q5_5    fixed     1.000        -        -        -      -      -  
   Q5_6**Q5_6    fixed     1.000        -        -        -      -      -  
   Q5_8**Q5_8    fixed     1.000        -        -        -      -      -  
 Q5_12**Q5_12    fixed     1.000        -        -        -      -      -  
   Q6_1**Q6_1    fixed     1.000        -        -        -      -      -  
   Q6_2**Q6_2    fixed     1.000        -        -        -      -      -  
   Q6_3**Q6_3    fixed     1.000        -        -        -      -      -  
   Q6_4**Q6_4    fixed     1.000        -        -        -      -      -  
   Q6_5**Q6_5    fixed     1.000        -        -        -      -      -  
   Q6_6**Q6_6    fixed     1.000        -        -        -      -      -  
   Q6_7**Q6_7    fixed     1.000        -        -        -      -      -  
   Q6_8**Q6_8    fixed     1.000        -        -        -      -      -  
 Q6_11**Q6_11    fixed     1.000        -        -        -      -      -  
   Q7_2**Q7_2    fixed     1.000        -        -        -      -      -  
   Q7_4**Q7_4    fixed     1.000        -        -        -      -      -  
   Q7_5**Q7_5    fixed     1.000        -        -        -      -      -  
   Q7_7**Q7_7    fixed     1.000        -        -        -      -      -  
   Q7_8**Q7_8    fixed     1.000        -        -        -      -      -  
 Q7_12**Q7_12    fixed     1.000        -        -        -      -      -  
 Q7_13**Q7_13    fixed     1.000        -        -        -      -      -  
 Q7_14**Q7_14    fixed     1.000        -        -        -      -      -

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] xtable_1.8-4      kableExtra_1.3.1  readxl_1.3.1      coda_0.19-4      
 [5] nFactors_2.4.1    lattice_0.20-41   psych_2.0.12      psychometric_2.2 
 [9] multilevel_2.6    MASS_7.3-53       nlme_3.1-151      mvtnorm_1.1-1    
[13] ggcorrplot_0.1.3  naniar_0.6.0      simsem_0.5-15     lslx_0.6.10      
[17] MIIVsem_0.5.5     lavaanPlot_0.5.1  semTools_0.5-4    lavaan_0.6-7     
[21] data.table_1.13.6 patchwork_1.1.1   forcats_0.5.0     stringr_1.4.0    
[25] dplyr_1.0.3       purrr_0.3.4       readr_1.4.0       tidyr_1.1.2      
[29] tibble_3.0.5      ggplot2_3.3.3     tidyverse_1.3.0  

loaded via a namespace (and not attached):
 [1] fs_1.5.0           lubridate_1.7.9.2  webshot_0.5.2      RColorBrewer_1.1-2
 [5] httr_1.4.2         rprojroot_2.0.2    tools_4.0.3        backports_1.2.0   
 [9] R6_2.5.0           DBI_1.1.1          colorspace_2.0-0   withr_2.4.0       
[13] tidyselect_1.1.0   mnormt_2.0.2       nonnest2_0.5-5     compiler_4.0.3    
[17] git2r_0.28.0       cli_2.2.0          rvest_0.3.6        xml2_1.3.2        
[21] sandwich_3.0-0     labeling_0.4.2     scales_1.1.1       digest_0.6.27     
[25] pbivnorm_0.6.0     rmarkdown_2.6      pkgconfig_2.0.3    htmltools_0.5.1   
[29] highr_0.8          dbplyr_2.0.0       htmlwidgets_1.5.3  rlang_0.4.10      
[33] rstudioapi_0.13    farver_2.0.3       visNetwork_2.0.9   generics_0.1.0    
[37] zoo_1.8-8          jsonlite_1.7.2     magrittr_2.0.1     Matrix_1.2-18     
[41] Rcpp_1.0.6         munsell_0.5.0      fansi_0.4.2        lifecycle_0.2.0   
[45] visdat_0.5.3       stringi_1.5.3      yaml_2.2.1         CompQuadForm_1.4.3
[49] plyr_1.8.6         grid_4.0.3         parallel_4.0.3     promises_1.1.1    
[53] crayon_1.3.4       haven_2.3.1        hms_1.0.0          tmvnsim_1.0-2     
[57] knitr_1.30         ps_1.5.0           pillar_1.4.7       reshape2_1.4.4    
[61] stats4_4.0.3       reprex_0.3.0       glue_1.4.2         evaluate_0.14     
[65] modelr_0.1.8       vctrs_0.3.6        httpuv_1.5.5       cellranger_1.1.0  
[69] gtable_0.3.0       assertthat_0.2.1   xfun_0.20          broom_0.7.3       
[73] later_1.1.0.1      viridisLite_0.3.0  workflowr_1.6.2    DiagrammeR_1.0.6.1
[77] ellipsis_0.3.1

Pilot Study: Confirmatory Factor Analysis

R. Noah Padgett

2020-11-16

Data

Data Summary

CFA

Maximum Likelihood

Local Fit Assessment

ROPE Probability Approx

Modified Model

Final Model with Reliability Estimates

Best fitting model

DWLS

Modified Model

Final Model with Reliability Estimates

MIIV

MIIV Categorical

PLS

PLS Categorical