Last updated: 2020-11-19
Checks: 6 1
Knit directory: pools-projects/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish
to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20201007)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 34e8a9d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Unstaged changes:
Modified: analysis/index.Rmd
Modified: analysis/pilot-study-CFA.Rmd
Modified: analysis/pilot-study-EFA.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/pilot-study-CFA.Rmd
) and HTML (docs/pilot-study-CFA.html
) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 34e8a9d | noah-padgett | 2020-11-19 | updated pilot FAs |
html | 34e8a9d | noah-padgett | 2020-11-19 | updated pilot FAs |
Rmd | 4856250 | noah-padgett | 2020-11-19 | pilot-efa-results |
html | 4856250 | noah-padgett | 2020-11-19 | pilot-efa-results |
source("code/load_packages.R")
-- Attaching packages --------------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.2 v purrr 0.3.4
v tibble 3.0.3 v dplyr 1.0.1
v tidyr 1.1.1 v stringr 1.4.0
v readr 1.3.1 v forcats 0.5.0
-- Conflicts ------------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
The following object is masked from 'package:purrr':
transpose
This is lavaan 0.6-7
lavaan is BETA software! Please report any bugs.
###############################################################################
This is semTools 0.5-3
All users of R (or SEM) are invited to submit functions or ideas for functions.
###############################################################################
Attaching package: 'semTools'
The following object is masked from 'package:readr':
clipboard
Warning: package 'MIIVsem' was built under R version 4.0.3
This is MIIVsem 0.5.5
MIIVsem is BETA software! Please report any bugs.
#################################################################
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'
The following object is masked from 'package:lavaan':
inspect
Warning: package 'naniar' was built under R version 4.0.3
Warning: package 'ggcorrplot' was built under R version 4.0.3
Loading required package: multilevel
Loading required package: nlme
Attaching package: 'nlme'
The following object is masked from 'package:dplyr':
collapse
Loading required package: MASS
Attaching package: 'MASS'
The following object is masked from 'package:patchwork':
area
The following object is masked from 'package:dplyr':
select
Attaching package: 'psychometric'
The following object is masked from 'package:ggplot2':
alpha
Attaching package: 'psych'
The following object is masked from 'package:psychometric':
alpha
The following object is masked from 'package:simsem':
sim
The following object is masked from 'package:semTools':
skew
The following object is masked from 'package:lavaan':
cor2cov
The following objects are masked from 'package:ggplot2':
%+%, alpha
Warning: package 'nFactors' was built under R version 4.0.3
Loading required package: lattice
Attaching package: 'nFactors'
The following object is masked from 'package:lattice':
parallel
Attaching package: 'kableExtra'
The following object is masked from 'package:dplyr':
group_rows
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})
psych::describe(
mydata[, c(paste0("Q4_",c(1:5,8:11, 15:19)),
paste0("Q5_",c(1:6, 8, 12)),
paste0("Q6_",c(1:8, 11)),
paste0("Q7_",c(2, 4:5, 7:8, 12:14)))]
)
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
Q4_19 14 312 -0.51 0.97 -0.5 -0.54 0.74 -2 2 4 0.22 -0.38 0.06
Q5_1 15 312 -0.47 0.95 -1.0 -0.49 1.48 -2 2 4 0.19 -0.49 0.05
Q5_2 16 312 -0.04 1.01 0.0 0.01 1.48 -2 2 4 -0.20 -0.49 0.06
Q5_3 17 312 -0.41 1.04 0.0 -0.43 1.48 -2 2 4 0.26 -0.52 0.06
Q5_4 18 312 0.46 1.10 1.0 0.55 0.00 -2 2 4 -0.80 -0.15 0.06
Q5_5 19 312 0.45 1.06 1.0 0.52 0.00 -2 2 4 -0.78 -0.16 0.06
Q5_6 20 312 -0.15 0.92 0.0 -0.14 1.48 -2 2 4 0.03 -0.07 0.05
Q5_8 21 312 -0.16 1.07 0.0 -0.13 1.48 -2 2 4 -0.09 -0.66 0.06
Q5_12 22 312 -0.20 1.01 0.0 -0.18 1.48 -2 2 4 -0.10 -0.29 0.06
Q6_1 23 312 -1.32 0.86 -1.5 -1.48 0.74 -2 2 4 1.52 2.55 0.05
Q6_2 24 312 -0.96 0.91 -1.0 -1.07 0.00 -2 2 4 0.98 0.92 0.05
Q6_3 25 312 -1.01 0.92 -1.0 -1.13 1.48 -2 2 4 1.03 1.03 0.05
Q6_4 26 312 -0.89 0.94 -1.0 -0.99 1.48 -2 2 4 0.84 0.64 0.05
Q6_5 27 312 -0.61 1.10 -1.0 -0.69 1.48 -2 2 4 0.57 -0.45 0.06
Q6_6 28 312 -1.18 0.79 -1.0 -1.29 0.00 -2 2 4 1.08 1.68 0.04
Q6_7 29 312 -0.89 0.88 -1.0 -0.95 1.48 -2 2 4 0.61 0.19 0.05
Q6_8 30 312 -0.85 0.83 -1.0 -0.90 0.00 -2 2 4 0.70 0.77 0.05
Q6_11 31 312 -0.31 0.98 0.0 -0.30 1.48 -2 2 4 -0.11 -0.29 0.06
Q7_2 32 312 -0.35 0.89 0.0 -0.33 0.00 -2 2 4 -0.35 -0.09 0.05
Q7_4 33 312 -0.21 0.94 0.0 -0.18 1.48 -2 2 4 -0.07 -0.15 0.05
Q7_5 34 312 -0.21 0.94 0.0 -0.19 0.00 -2 2 4 -0.14 0.12 0.05
Q7_7 35 312 0.57 1.07 1.0 0.66 0.00 -2 2 4 -0.86 0.08 0.06
Q7_8 36 312 -0.19 0.94 0.0 -0.16 0.00 -2 2 4 -0.07 0.03 0.05
Q7_12 37 312 0.38 1.07 1.0 0.44 1.48 -2 2 4 -0.55 -0.17 0.06
Q7_13 38 312 0.49 1.11 1.0 0.56 1.48 -2 2 4 -0.50 -0.42 0.06
Q7_14 39 312 0.56 1.04 1.0 0.64 1.48 -2 2 4 -0.69 0.00 0.06
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 + Q4_19
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
"
fit1 <- lavaan::cfa(mod1, data=mydata)
summary(fit1, standardized=T, fit.measures=T)
lavaan 0.6-7 ended normally after 69 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 84
Number of observations 312
Model Test User Model:
Test statistic 2124.858
Degrees of freedom 696
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 8846.565
Degrees of freedom 741
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.824
Tucker-Lewis Index (TLI) 0.812
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -13071.075
Loglikelihood unrestricted model (H1) -12008.646
Akaike (AIC) 26310.149
Bayesian (BIC) 26624.561
Sample-size adjusted Bayesian (BIC) 26358.142
Root Mean Square Error of Approximation:
RMSEA 0.081
90 Percent confidence interval - lower 0.077
90 Percent confidence interval - upper 0.085
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.080
Parameter Estimates:
Standard errors Standard
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 0.658 0.774
Q4_2 0.891 0.062 14.261 0.000 0.587 0.750
Q4_3 1.023 0.067 15.358 0.000 0.674 0.796
Q4_4 1.034 0.066 15.544 0.000 0.681 0.804
Q4_5 0.990 0.069 14.257 0.000 0.652 0.749
Q4_8 0.939 0.069 13.689 0.000 0.618 0.725
Q4_9 1.008 0.078 12.867 0.000 0.664 0.688
Q4_10 0.930 0.064 14.644 0.000 0.613 0.766
Q4_11 1.088 0.076 14.348 0.000 0.716 0.753
Q4_15 0.963 0.071 13.544 0.000 0.634 0.718
Q4_16 0.941 0.076 12.408 0.000 0.620 0.667
Q4_17 0.856 0.077 11.130 0.000 0.563 0.607
Q4_18 0.980 0.062 15.887 0.000 0.645 0.818
Q4_19 1.115 0.077 14.418 0.000 0.734 0.756
SC =~
Q5_1 1.000 0.547 0.575
Q5_2 1.144 0.130 8.792 0.000 0.625 0.619
Q5_3 1.250 0.136 9.170 0.000 0.684 0.657
Q5_4 1.593 0.154 10.326 0.000 0.871 0.792
Q5_5 1.548 0.149 10.387 0.000 0.846 0.800
Q5_6 1.310 0.128 10.225 0.000 0.716 0.779
Q5_8 1.527 0.149 10.234 0.000 0.835 0.780
Q5_12 1.111 0.129 8.603 0.000 0.607 0.600
IN =~
Q6_1 1.000 0.587 0.686
Q6_2 1.218 0.095 12.824 0.000 0.714 0.786
Q6_3 1.258 0.097 13.010 0.000 0.738 0.799
Q6_4 1.236 0.098 12.674 0.000 0.725 0.776
Q6_5 0.855 0.112 7.663 0.000 0.501 0.456
Q6_6 1.050 0.082 12.784 0.000 0.616 0.784
Q6_7 1.226 0.092 13.345 0.000 0.719 0.822
Q6_8 1.164 0.087 13.314 0.000 0.683 0.820
Q6_11 1.029 0.100 10.258 0.000 0.603 0.619
EN =~
Q7_2 1.000 0.654 0.737
Q7_4 0.926 0.084 11.058 0.000 0.605 0.643
Q7_5 1.077 0.083 12.945 0.000 0.704 0.747
Q7_7 1.146 0.094 12.134 0.000 0.749 0.702
Q7_8 1.103 0.083 13.372 0.000 0.721 0.771
Q7_12 1.083 0.095 11.449 0.000 0.708 0.665
Q7_13 0.635 0.100 6.365 0.000 0.415 0.376
Q7_14 1.017 0.093 10.969 0.000 0.665 0.638
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL ~~
SC 0.205 0.032 6.505 0.000 0.571 0.571
IN 0.267 0.034 7.767 0.000 0.691 0.691
EN 0.309 0.038 8.080 0.000 0.718 0.718
SC ~~
IN 0.169 0.028 6.038 0.000 0.528 0.528
EN 0.275 0.038 7.180 0.000 0.771 0.771
IN ~~
EN 0.257 0.035 7.422 0.000 0.671 0.671
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL 0.434 0.054 8.040 0.000 1.000 1.000
SC 0.299 0.056 5.337 0.000 1.000 1.000
IN 0.344 0.051 6.741 0.000 1.000 1.000
EN 0.427 0.058 7.335 0.000 1.000 1.000
.Q4_1 0.290 0.025 11.525 0.000 0.290 0.400
.Q4_2 0.268 0.023 11.664 0.000 0.268 0.438
.Q4_3 0.263 0.023 11.375 0.000 0.263 0.366
.Q4_4 0.254 0.022 11.314 0.000 0.254 0.354
.Q4_5 0.332 0.028 11.665 0.000 0.332 0.438
.Q4_8 0.346 0.029 11.778 0.000 0.346 0.475
.Q4_9 0.490 0.041 11.912 0.000 0.490 0.527
.Q4_10 0.264 0.023 11.575 0.000 0.264 0.413
.Q4_11 0.391 0.034 11.645 0.000 0.391 0.432
.Q4_15 0.377 0.032 11.804 0.000 0.377 0.484
.Q4_16 0.479 0.040 11.975 0.000 0.479 0.555
.Q4_17 0.545 0.045 12.115 0.000 0.545 0.632
.Q4_18 0.206 0.018 11.189 0.000 0.206 0.331
.Q4_19 0.403 0.035 11.629 0.000 0.403 0.428
.Q5_1 0.604 0.051 11.879 0.000 0.604 0.669
.Q5_2 0.630 0.054 11.722 0.000 0.630 0.617
.Q5_3 0.614 0.053 11.547 0.000 0.614 0.568
.Q5_4 0.452 0.043 10.396 0.000 0.452 0.373
.Q5_5 0.403 0.039 10.275 0.000 0.403 0.360
.Q5_6 0.333 0.031 10.569 0.000 0.333 0.394
.Q5_8 0.449 0.043 10.555 0.000 0.449 0.392
.Q5_12 0.654 0.055 11.793 0.000 0.654 0.640
.Q6_1 0.387 0.033 11.660 0.000 0.387 0.529
.Q6_2 0.315 0.029 10.974 0.000 0.315 0.382
.Q6_3 0.308 0.028 10.838 0.000 0.308 0.362
.Q6_4 0.347 0.031 11.072 0.000 0.347 0.397
.Q6_5 0.955 0.078 12.246 0.000 0.955 0.792
.Q6_6 0.238 0.022 11.001 0.000 0.238 0.386
.Q6_7 0.249 0.024 10.541 0.000 0.249 0.325
.Q6_8 0.228 0.022 10.571 0.000 0.228 0.328
.Q6_11 0.587 0.049 11.913 0.000 0.587 0.617
.Q7_2 0.359 0.033 10.914 0.000 0.359 0.457
.Q7_4 0.520 0.045 11.561 0.000 0.520 0.587
.Q7_5 0.392 0.036 10.815 0.000 0.392 0.442
.Q7_7 0.576 0.051 11.202 0.000 0.576 0.507
.Q7_8 0.356 0.034 10.549 0.000 0.356 0.406
.Q7_12 0.633 0.055 11.446 0.000 0.633 0.558
.Q7_13 1.046 0.085 12.275 0.000 1.046 0.859
.Q7_14 0.644 0.056 11.586 0.000 0.644 0.593
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
(= -1.720588e-17) 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 67 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 201
Number of observations 312
Model Test User Model:
Standard Robust
Test Statistic 2549.695 2222.269
Degrees of freedom 696 696
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.437
Shift parameter 447.847
simple second-order correction
Model Test Baseline Model:
Test statistic 113152.945 17274.138
Degrees of freedom 741 741
P-value 0.000 0.000
Scaling correction factor 6.799
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.984 0.908
Tucker-Lewis Index (TLI) 0.982 0.902
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.093 0.084
90 Percent confidence interval - lower 0.089 0.080
90 Percent confidence interval - upper 0.096 0.088
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 0.815 0.815
Q4_2 0.958 0.030 31.941 0.000 0.781 0.781
Q4_3 1.039 0.029 36.179 0.000 0.846 0.846
Q4_4 1.042 0.028 37.342 0.000 0.849 0.849
Q4_5 0.981 0.033 29.651 0.000 0.799 0.799
Q4_8 0.958 0.034 27.917 0.000 0.781 0.781
Q4_9 0.953 0.035 27.360 0.000 0.776 0.776
Q4_10 1.007 0.031 32.389 0.000 0.821 0.821
Q4_11 1.018 0.033 31.193 0.000 0.829 0.829
Q4_15 0.957 0.030 31.435 0.000 0.780 0.780
Q4_16 0.910 0.031 28.934 0.000 0.742 0.742
Q4_17 0.821 0.038 21.493 0.000 0.669 0.669
Q4_18 1.051 0.029 35.739 0.000 0.856 0.856
Q4_19 1.007 0.030 33.139 0.000 0.821 0.821
SC =~
Q5_1 1.000 0.783 0.783
Q5_2 0.860 0.046 18.535 0.000 0.673 0.673
Q5_3 0.926 0.052 17.815 0.000 0.725 0.725
Q5_4 1.046 0.046 22.706 0.000 0.819 0.819
Q5_5 1.077 0.045 23.921 0.000 0.843 0.843
Q5_6 1.027 0.049 21.132 0.000 0.804 0.804
Q5_8 0.995 0.051 19.418 0.000 0.779 0.779
Q5_12 1.017 0.051 19.765 0.000 0.796 0.796
IN =~
Q6_1 1.000 0.757 0.757
Q6_2 1.108 0.045 24.546 0.000 0.840 0.840
Q6_3 1.058 0.049 21.527 0.000 0.801 0.801
Q6_4 1.062 0.046 22.874 0.000 0.804 0.804
Q6_5 0.731 0.057 12.885 0.000 0.554 0.554
Q6_6 1.124 0.052 21.505 0.000 0.851 0.851
Q6_7 1.193 0.059 20.355 0.000 0.903 0.903
Q6_8 1.121 0.052 21.466 0.000 0.849 0.849
Q6_11 1.130 0.062 18.216 0.000 0.856 0.856
EN =~
Q7_2 1.000 0.831 0.831
Q7_4 0.843 0.038 22.316 0.000 0.701 0.701
Q7_5 0.948 0.038 25.129 0.000 0.788 0.788
Q7_7 0.837 0.043 19.240 0.000 0.695 0.695
Q7_8 0.959 0.040 24.249 0.000 0.797 0.797
Q7_12 0.844 0.044 19.218 0.000 0.702 0.702
Q7_13 0.484 0.054 8.890 0.000 0.403 0.403
Q7_14 0.803 0.046 17.628 0.000 0.668 0.668
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL ~~
SC 0.385 0.031 12.441 0.000 0.603 0.603
IN 0.440 0.033 13.191 0.000 0.713 0.713
EN 0.491 0.028 17.588 0.000 0.725 0.725
SC ~~
IN 0.356 0.033 10.644 0.000 0.600 0.600
EN 0.490 0.032 15.155 0.000 0.753 0.753
IN ~~
EN 0.453 0.035 13.107 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
.Q4_19 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
Q4_19|t1 -0.967 0.085 -11.438 0.000 -0.967 -0.967
Q4_19|t2 0.000 0.071 0.000 1.000 0.000 0.000
Q4_19|t3 1.105 0.089 12.370 0.000 1.105 1.105
Q4_19|t4 2.006 0.157 12.746 0.000 2.006 2.006
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 0.664 0.034 19.547 0.000 1.000 1.000
SC 0.613 0.047 13.018 0.000 1.000 1.000
IN 0.574 0.051 11.165 0.000 1.000 1.000
EN 0.691 0.043 16.158 0.000 1.000 1.000
.Q4_1 0.336 0.336 0.336
.Q4_2 0.391 0.391 0.391
.Q4_3 0.284 0.284 0.284
.Q4_4 0.280 0.280 0.280
.Q4_5 0.362 0.362 0.362
.Q4_8 0.390 0.390 0.390
.Q4_9 0.398 0.398 0.398
.Q4_10 0.327 0.327 0.327
.Q4_11 0.313 0.313 0.313
.Q4_15 0.392 0.392 0.392
.Q4_16 0.450 0.450 0.450
.Q4_17 0.553 0.553 0.553
.Q4_18 0.267 0.267 0.267
.Q4_19 0.326 0.326 0.326
.Q5_1 0.387 0.387 0.387
.Q5_2 0.547 0.547 0.547
.Q5_3 0.475 0.475 0.475
.Q5_4 0.330 0.330 0.330
.Q5_5 0.289 0.289 0.289
.Q5_6 0.354 0.354 0.354
.Q5_8 0.393 0.393 0.393
.Q5_12 0.366 0.366 0.366
.Q6_1 0.426 0.426 0.426
.Q6_2 0.295 0.295 0.295
.Q6_3 0.358 0.358 0.358
.Q6_4 0.353 0.353 0.353
.Q6_5 0.693 0.693 0.693
.Q6_6 0.275 0.275 0.275
.Q6_7 0.184 0.184 0.184
.Q6_8 0.279 0.279 0.279
.Q6_11 0.267 0.267 0.267
.Q7_2 0.309 0.309 0.309
.Q7_4 0.509 0.509 0.509
.Q7_5 0.380 0.380 0.380
.Q7_7 0.516 0.516 0.516
.Q7_8 0.365 0.365 0.365
.Q7_12 0.508 0.508 0.508
.Q7_13 0.838 0.838 0.838
.Q7_14 0.554 0.554 0.554
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
Q4_19 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
# 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
Q4_19 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, 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, Q4_2, Q4_3, Q4_4, Q4_5, Q4_8, Q4_9, Q4_10, Q4_11, Q4_15, Q4_16, Q4_17, Q4_18, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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, Q4_19, 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 35
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 0.813 0.050 16.136 0.000 57.558 36 0.013
Q4_3 0.978 0.052 18.928 0.000 45.111 36 0.142
Q4_4 0.950 0.054 17.449 0.000 52.226 36 0.039
Q4_5 0.812 0.064 12.705 0.000 89.531 36 0.000
Q4_8 0.754 0.064 11.764 0.000 89.092 36 0.000
Q4_9 0.770 0.072 10.678 0.000 126.594 36 0.000
Q4_10 0.795 0.053 15.081 0.000 69.268 36 0.001
Q4_11 0.906 0.065 13.909 0.000 97.157 36 0.000
Q4_15 0.781 0.063 12.397 0.000 96.437 36 0.000
Q4_16 0.836 0.067 12.512 0.000 78.464 36 0.000
Q4_17 0.674 0.069 9.729 0.000 93.603 36 0.000
Q4_18 0.845 0.053 16.028 0.000 69.339 36 0.001
Q4_19 0.891 0.068 13.201 0.000 118.605 36 0.000
EN =~
Q7_2 1.000
Q7_4 0.808 0.067 12.136 0.000 65.024 36 0.002
Q7_5 0.933 0.066 14.066 0.000 55.791 36 0.019
Q7_7 0.853 0.078 10.922 0.000 99.736 36 0.000
Q7_8 0.883 0.065 13.532 0.000 86.489 36 0.000
Q7_12 0.838 0.082 10.239 0.000 112.196 36 0.000
Q7_13 0.411 0.088 4.692 0.000 108.095 36 0.000
Q7_14 0.761 0.080 9.459 0.000 102.168 36 0.000
IN =~
Q6_1 1.000
Q6_2 1.004 0.063 16.000 0.000 81.381 36 0.000
Q6_3 0.949 0.065 14.557 0.000 79.387 36 0.000
Q6_4 0.975 0.069 14.101 0.000 82.202 36 0.000
Q6_5 0.615 0.089 6.914 0.000 64.094 36 0.003
Q6_6 0.682 0.059 11.518 0.000 123.462 36 0.000
Q6_7 0.851 0.066 12.807 0.000 118.819 36 0.000
Q6_8 0.817 0.063 12.891 0.000 100.842 36 0.000
Q6_11 0.608 0.080 7.559 0.000 132.969 36 0.000
SC =~
Q5_1 1.000
Q5_2 0.836 0.078 10.755 0.000 74.900 36 0.000
Q5_3 0.951 0.083 11.438 0.000 50.812 36 0.052
Q5_4 0.803 0.089 9.041 0.000 155.872 36 0.000
Q5_5 0.829 0.088 9.452 0.000 136.462 36 0.000
Q5_6 0.772 0.076 10.183 0.000 93.538 36 0.000
Q5_8 0.808 0.086 9.428 0.000 124.326 36 0.000
Q5_12 0.794 0.084 9.500 0.000 81.247 36 0.000
INTERCEPTS:
Estimate Std.Err z-value P(>|z|)
Q4_1 0.000
Q4_10 0.033 0.049 0.684 0.494
Q4_11 0.038 0.061 0.627 0.531
Q4_15 -0.222 0.059 -3.777 0.000
Q4_16 -0.149 0.062 -2.392 0.017
Q4_17 -0.487 0.065 -7.535 0.000
Q4_18 -0.202 0.049 -4.098 0.000
Q4_19 0.042 0.063 0.665 0.506
Q4_2 -0.302 0.047 -6.463 0.000
Q4_3 0.070 0.048 1.456 0.145
Q4_4 0.117 0.051 2.301 0.021
Q4_5 -0.251 0.060 -4.208 0.000
Q4_8 -0.348 0.060 -5.827 0.000
Q4_9 -0.130 0.067 -1.927 0.054
Q5_1 0.000
Q5_12 0.181 0.070 2.601 0.009
Q5_2 0.355 0.063 5.628 0.000
Q5_3 0.044 0.069 0.634 0.526
Q5_4 0.839 0.074 11.358 0.000
Q5_5 0.845 0.073 11.571 0.000
Q5_6 0.216 0.063 3.414 0.001
Q5_8 0.226 0.071 3.175 0.001
Q6_1 0.000
Q6_11 0.492 0.119 4.125 0.000
Q6_2 0.371 0.092 4.017 0.000
Q6_3 0.246 0.096 2.556 0.011
Q6_4 0.396 0.103 3.864 0.000
Q6_5 0.203 0.132 1.540 0.124
Q6_6 -0.282 0.088 -3.212 0.001
Q6_7 0.236 0.099 2.390 0.017
Q6_8 0.233 0.094 2.477 0.013
Q7_12 0.677 0.064 10.526 0.000
Q7_13 0.639 0.069 9.266 0.000
Q7_14 0.829 0.063 13.114 0.000
Q7_2 0.000
Q7_4 0.070 0.052 1.346 0.178
Q7_5 0.114 0.052 2.193 0.028
Q7_7 0.868 0.061 14.165 0.000
Q7_8 0.125 0.051 2.457 0.014
fit1 <- MIIVsem::miive(
mod1, data=mydata,
ordered=c(
paste0("Q4_",c(1:5,8:11, 15:19)),
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
(= -2.521472e-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 35
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 0.872 0.034 26.013 0.000
Q4_3 0.957 0.031 30.734 0.000
Q4_4 0.919 0.039 23.338 0.000
Q4_5 0.767 0.045 16.961 0.000
Q4_8 0.728 0.046 15.666 0.000
Q4_9 0.653 0.051 12.794 0.000
Q4_10 0.824 0.036 22.656 0.000
Q4_11 0.787 0.042 18.937 0.000
Q4_15 0.731 0.041 17.718 0.000
Q4_16 0.776 0.043 18.051 0.000
Q4_17 0.626 0.051 12.294 0.000
Q4_18 0.896 0.041 21.975 0.000
Q4_19 0.748 0.042 17.885 0.000
EN =~
Q7_2 1.000
Q7_4 0.674 0.050 13.359 0.000
Q7_5 0.809 0.047 17.046 0.000
Q7_7 0.642 0.056 11.396 0.000
Q7_8 0.746 0.048 15.671 0.000
Q7_12 0.644 0.060 10.708 0.000
Q7_13 0.298 0.067 4.469 0.000
Q7_14 0.571 0.060 9.481 0.000
IN =~
Q6_1 1.000
Q6_2 0.834 0.047 17.890 0.000
Q6_3 0.762 0.048 15.791 0.000
Q6_4 0.777 0.045 17.420 0.000
Q6_5 0.442 0.053 8.271 0.000
Q6_6 0.661 0.051 12.916 0.000
Q6_7 0.737 0.051 14.534 0.000
Q6_8 0.741 0.049 14.991 0.000
Q6_11 0.473 0.060 7.843 0.000
SC =~
Q5_1 1.000
Q5_2 0.782 0.067 11.646 0.000
Q5_3 0.846 0.060 13.996 0.000
Q5_4 0.667 0.069 9.651 0.000
Q5_5 0.728 0.074 9.899 0.000
Q5_6 0.739 0.062 11.990 0.000
Q5_8 0.684 0.065 10.478 0.000
Q5_12 0.679 0.069 9.802 0.000
sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
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_1.1.0 readxl_1.3.1 nFactors_2.4.1
[5] lattice_0.20-41 psych_2.0.7 psychometric_2.2 multilevel_2.6
[9] MASS_7.3-51.6 nlme_3.1-148 mvtnorm_1.1-1 ggcorrplot_0.1.3
[13] naniar_0.6.0 simsem_0.5-15 MIIVsem_0.5.5 lavaanPlot_0.5.1
[17] semTools_0.5-3 lavaan_0.6-7 data.table_1.13.0 patchwork_1.0.1
[21] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.1 purrr_0.3.4
[25] readr_1.3.1 tidyr_1.1.1 tibble_3.0.3 ggplot2_3.3.2
[29] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 webshot_0.5.2 RColorBrewer_1.1-2
[5] httr_1.4.2 rprojroot_1.3-2 tools_4.0.2 backports_1.1.7
[9] R6_2.4.1 DBI_1.1.0 colorspace_1.4-1 withr_2.2.0
[13] tidyselect_1.1.0 mnormt_2.0.2 emmeans_1.4.8 compiler_4.0.2
[17] git2r_0.27.1 cli_2.0.2 rvest_0.3.6 xml2_1.3.2
[21] sandwich_2.5-1 scales_1.1.1 digest_0.6.25 pbivnorm_0.6.0
[25] rmarkdown_2.5 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[29] htmlwidgets_1.5.1 rlang_0.4.7 rstudioapi_0.11 visNetwork_2.0.9
[33] generics_0.0.2 zoo_1.8-8 jsonlite_1.7.0 magrittr_1.5
[37] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
[41] visdat_0.5.3 lifecycle_0.2.0 stringi_1.4.6 multcomp_1.4-13
[45] whisker_0.4 yaml_2.2.1 grid_4.0.2 blob_1.2.1
[49] parallel_4.0.2 promises_1.1.1 crayon_1.3.4 haven_2.3.1
[53] splines_4.0.2 hms_0.5.3 tmvnsim_1.0-2 knitr_1.29
[57] pillar_1.4.6 estimability_1.3 codetools_0.2-16 stats4_4.0.2
[61] reprex_0.3.0 glue_1.4.1 evaluate_0.14 modelr_0.1.8
[65] vctrs_0.3.2 httpuv_1.5.4 cellranger_1.1.0 gtable_0.3.0
[69] assertthat_0.2.1 xfun_0.19 broom_0.7.0 coda_0.19-3
[73] later_1.1.0.1 viridisLite_0.3.0 survival_3.2-3 DiagrammeR_1.0.6.1
[77] TH.data_1.0-10 ellipsis_0.3.1