Last updated: 2019-10-18

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html	982c8f1	noah-padgett	2019-05-18	roc analyses completed
Rmd	4a2f40d	noah-padgett	2019-05-10	fixed index page and updated roc file
Rmd	3cd3ef6	noah-padgett	2019-05-09	updated analyses

Purpose of this file:

Conduct ROC Analysies
Create ROC Curves
Create Summary tables of ROC analyses

Packages and Set-Up

##Chunk iptions
knitr::opts_chunk$set(out.width="225%")
#setwd('C:/Users/noahp/Dropbox/MCFA Thesis/Code Results')
## Packages
## General Packages
library(tidyverse)

-- Attaching packages -------------------------------------- tidyverse 1.2.1 --

v ggplot2 3.2.0     v purrr   0.3.2
v tibble  2.1.1     v dplyr   0.8.1
v tidyr   0.8.3     v stringr 1.4.0
v readr   1.3.1     v forcats 0.4.0

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

library(car)

Loading required package: carData


Attaching package: 'car'

The following object is masked from 'package:dplyr':

    recode

The following object is masked from 'package:purrr':

    some

library(psych)


Attaching package: 'psych'

The following object is masked from 'package:car':

    logit

The following objects are masked from 'package:ggplot2':

    %+%, alpha

# Formatting and Tables
library(kableExtra)


Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

    group_rows

library(xtable)
# For plotting
library(ggplot2)
theme_set(theme_bw() + theme(legend.position = 'bottom'))
# Data manipulating
library(dplyr)
# ROC Analysis
library(pROC)

Type 'citation("pROC")' for a citation.


Attaching package: 'pROC'

The following objects are masked from 'package:stats':

    cov, smooth, var

## One global parameter for printing figures
save.fig <- F
## Load up the functions needed for ANOVA and Assumption checking
source('code/r_functions.R')

Data Management

sim_results <- as_tibble(read.table('data/compiled_fit_results.txt', header=T,sep='\t'))
## Next, turn condition into a factor for plotting
sim_results$Condition <- as.factor(sim_results$Condition)
## Next, since TLI is non-normed, any value greater than 1 needs to be rescaled to 1.
sim_results$TLI <- ifelse(sim_results$TLI > 1, 1, sim_results$TLI)
sim_results$TLI <- ifelse(sim_results$TLI < 0, 0, sim_results$TLI)
## Next, summarize the results of the chi-square test of model fit. This is done simply by comparing the p-value to alpha (0.05) and indicating whether the model was flagged as fitting or not.
# Note: if  p < 0.05 then this variable is flagged as 0, and 1 otherwise
sim_results$Chi2_pvalue_decision <- ifelse(sim_results$chisqu_pvalue > 0.05, 1, 0)
# 0 = rejected that these data fit this model
# 1 = failed to reject that these data fit this model

Adding Labels to Conditions

Currently, each condition is kind of like a hidden id that we don’t know what the actual factor is. So, first thing isto create meaningful labels for us to use. Remember, the 72 conditions for the this study were

Level-1 sample size (5, 10, 30)
Level-2 sample size (30, 50, 100, 200)
Observed indicator ICC (.1, .3, .5)
Latent variable ICC (.1, .5)

## level-1 Sample size
ss_l1 <- c(5, 10, 30) ## 6 conditions each
ss_l2 <- c(30, 50, 100, 200) ## 18 condition each
icc_ov <- c(.1, .3, .5) ## 2 conditions each
icc_lv <- c(.1, .5) ## every other condition
nCon <- 72 # number of conditions
nRep <- 500 # number of replications per condition
nMod <- 12 ## numberof estimated models per conditions
## Total number of rows: 432,000
ss_l2 <- c(rep(ss_l2[1], 18*nRep*nMod), rep(ss_l2[2], 18*nRep*nMod), rep(ss_l2[3], 18*nRep*nMod), rep(ss_l2[4], 18*nRep*nMod))
ss_l1 <- rep(c(rep(ss_l1[1],6*nRep*nMod), rep(ss_l1[2],6*nRep*nMod), rep(ss_l1[3],6*nRep*nMod)), 4)
icc_ov <- rep(c(rep(icc_ov[1], 2*nRep*nMod), rep(icc_ov[2], 2*nRep*nMod), rep(icc_ov[3], 2*nRep*nMod)), 12)
icc_lv <- rep(c(rep(icc_lv[1], nRep*nMod), rep(icc_lv[2], nRep*nMod)), 36)
## Force these vectors to be column vectors
ss_l1 <- matrix(ss_l1, ncol=1)
ss_l2 <- matrix(ss_l2, ncol=1)
icc_ov <- matrix(icc_ov, ncol=1)
icc_lv <- matrix(icc_lv, ncol=1)
## Add the labels to the results data frame
sim_results <- sim_results[order(sim_results$Condition),]
sim_results <- cbind(sim_results, ss_l1, ss_l2, icc_ov, icc_lv)
## Force the conditions to be factors
sim_results$ss_l1 <- as.factor(sim_results$ss_l1)
sim_results$ss_l2 <- as.factor(sim_results$ss_l2)
sim_results$icc_ov <- as.factor(sim_results$icc_ov)
sim_results$icc_lv <- as.factor(sim_results$icc_lv)
sim_results$Model <- factor(sim_results$Model, levels = c('C','M1','M2','M12'), ordered = T)

ROC Analysis Labels

Make coding of variables/model specifications for the ROC analyses. The coding of the variables is in order to get the correct specifications labeled for the ROC analyses to come. The coding is as follows:

C = correct model specification versus any misspecification (Model C vs. M1, M2, M12)
CvM1 = when only the level-1 model is misspecified (Model C vs. M1)
CvM2 = when only the level-2 model is misspecified (Model C vs. M2)

## Need to make codes for the ROC analyses outcomes
# first, C vs. M1,M2,M12 - Perfect specification
sim_results$C <- ifelse(sim_results$Model == 'C', 1, 0)
table(sim_results$C)


     0      1 
324000 108000

# second, C vs. M1|M12- correct level 1 model
sim_results$CvM1 <- ifelse(sim_results$Model == 'C', 1, 0)
sim_results$CvM1[sim_results$Model == "M2" | sim_results$Model == "M12"] <- NA
table(sim_results$CvM1)


     0      1 
108000 108000

# third, C vs. M2|M12- correct level 2 model
sim_results$CvM2 <- ifelse(sim_results$Model == 'C', 1, 0)
sim_results$CvM2[sim_results$Model == "M1" | sim_results$Model == "M12"] <- NA
table(sim_results$CvM2)


     0      1 
108000 108000

Introduction to ROC Analysis

ROC stands for Receiver Operating Characteristic. ROC analysis aims to detect the presense of signals in data by looking at how the ability to classify an outcome (usually binary) based on a continuous or ordinal indicator. ROC analyses was orginally used in wartime to help detect the presence of radar signals. However, now ROC analysis is used in many areas including medical and psychology research. It is commonly used as a tool to help make decisions about what tools or methods help classify objects or individuals into specific groups.

In R, a package called pROC (Robin et al., 2011) was built that greatly enhancing the flexibility of using R for ROC analysis. For example, aside from just being able to conduct ROC analysis, one can compute confidences for this curve and conduct specific statistical tests comparing AUCs from the same data.

For the ROC analyses, we conducted them in pieces to build more and more fine grained information about the classification quality of fit indices for detecting a simple type of misspecification. For misspecification, we broke up the ROC analyses into three major chunks.

Detecting any type of misspecification (i.e., C vs. M1-M2-M12),
Detecting misspecified level-1 model (i.e., C vs. M1), and
Detecting misspecified level-2 model (i.e., C vs. M2).

Within each of these major chunks of analyses, we furhter investigated whether classification of correctly specified models was depended upon estimator, level-1 sample size, or level-2 sample size. There are intitally be MANY ROC curve figures and over 1200 ROC analyses.

Setting up the objects to store the individual results so that we can use them all for the figures. First, we run over each condition separately then go into the conditional ROC analyses.

fit_roc <- fit_roc_smooth <- list()
roc_summary <- as.data.frame(matrix(0,ncol=13, nrow=5*3*4*4*5))
colnames(roc_summary) <- c('Classification','Index', 'Estimator', 'Level-2 SS', 
                           'Level-1 SS', 'AUC',
                           'partial-AUC','Smoothed-AUC', 'Threshold',
                           'Specificity','Sensitivity', 'Num-C', 'Num-Mis')
roc_summary_gen <- as.data.frame(matrix(0,ncol=8, nrow=5)*3)
colnames(roc_summary_gen) <- c('Classification','Index', 'AUC',
                           'partial-AUC','Smoothed-AUC', 'Optimal-Threshold',
                           'Specificity','Sensitivity')
# Defining iterators
CLASS <- c('C', 'CvM1','CvM2')
INDEX <- c('CFI', 'TLI', 'RMSEA', 'SRMRW', 'SRMRB')
EST <- c('ALL','MLR', 'ULSMV', 'WLSMV')
SS_L1 <- c('ALL', 5, 10, 30)
SS_L2 <- c('ALL', 30, 50, 100, 200)
## Subset to the usable cases
sim_results <- filter(sim_results, Converge == 1 & Admissible == 1)

ROC Analyses

Detecting any type of misspecification

General overview over all conditions

ig <- 1 ## counter for roc_summary
j <- 1 ## Which class?
for(index in INDEX){
    ## Print out which iteration so we know what we am looking at
    cat('\n\nROC Analysis in')
    cat('\nIndex:\t', index)
    cat('\nClassification:\t', CLASS[j])
    ## Set up iteration key
    key <- paste0(index,'.',CLASS[j])
    ## Create formula
    model <- as.formula(paste0(CLASS[j], '~', index))
    ## Fit ROC curve
    fit_roc[[key]] <-  roc(model, data=sim_results, quiet=TRUE,
                           plot=TRUE, ci=TRUE, print.auc=TRUE)
    ## Create a plot of "smoothed" curve for plotting
    fit_roc_smooth[[key]] <-  smooth(roc(model, data=sim_results))
    ## Compute partial AUC for specificity .8-1
    p.auc <- auc(fit_roc[[key]], partial.auc = c(1,.8),
                 partial.auc.focus = 'sp', partial.auc.correct = T)
    ## get summary info
    roc_summary_gen[ig, 2] <- index
    roc_summary_gen[ig, 1] <- CLASS[j]
    roc_summary_gen[ig, 3] <- fit_roc[[key]]$auc ## total AUC
    roc_summary_gen[ig, 4] <- p.auc ## corrected partial AUC (.5 is no discrimination)
    roc_summary_gen[ig, 5] <- fit_roc_smooth[[key]]$auc ## smoothed AUC
    roc_summary_gen[ig, 6:8] <- coords(fit_roc[[key]], "best", 
                                   ret=c("threshold", "specificity", 'sensitivity'),
                                   transpose=TRUE)
    ## print summary
    cat('\n\nSummary of ROC:\n')
    print(roc_summary_gen[ig, ])
    ## add to summary iterator
    ig <- ig + 1
} ## End loop round index



ROC Analysis in
Index:   CFI
Classification:  C

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
1              C   CFI 0.8156696   0.6374748    0.8456461
  Optimal-Threshold Specificity Sensitivity
1          0.977017   0.7024613   0.8549251


ROC Analysis in
Index:   TLI
Classification:  C

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
2              C   TLI 0.8154291   0.6374691    0.8453772
  Optimal-Threshold Specificity Sensitivity
2         0.9724203   0.7022691   0.8549251


ROC Analysis in
Index:   RMSEA
Classification:  C

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
3              C RMSEA 0.8034875   0.6348255    0.8299624
  Optimal-Threshold Specificity Sensitivity
3        0.01504671   0.6849311    0.829003


ROC Analysis in
Index:   SRMRW
Classification:  C

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
4              C SRMRW 0.7420696   0.6079951    0.7165381
  Optimal-Threshold Specificity Sensitivity
4        0.03812817   0.7281555   0.7226555


ROC Analysis in
Index:   SRMRB
Classification:  C

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
5              C SRMRB 0.5979973   0.5720031    0.6039185
  Optimal-Threshold Specificity Sensitivity
5        0.06685217   0.8038081   0.3519244

kable(roc_summary_gen[1:5,], format = 'html', digits=3) %>%
  kable_styling(full_width = T)

Classification	Index	AUC	partial-AUC	Smoothed-AUC	Optimal-Threshold	Specificity	Sensitivity
C	CFI	0.816	0.637	0.846	0.977	0.702	0.855
C	TLI	0.815	0.637	0.845	0.972	0.702	0.855
C	RMSEA	0.803	0.635	0.830	0.015	0.685	0.829
C	SRMRW	0.742	0.608	0.717	0.038	0.728	0.723
C	SRMRB	0.598	0.572	0.604	0.067	0.804	0.352

print(xtable(roc_summary_gen[1:5,c(2:3,6:8)], digits = 3), booktabs=T,include.rownames = F)

% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Fri Oct 18 17:35:26 2019
\begin{table}[ht]
\centering
\begin{tabular}{lrrrr}
  \toprule
Index & AUC & Optimal-Threshold & Specificity & Sensitivity \\ 
  \midrule
CFI & 0.816 & 0.977 & 0.702 & 0.855 \\ 
  TLI & 0.815 & 0.972 & 0.702 & 0.855 \\ 
  RMSEA & 0.803 & 0.015 & 0.685 & 0.829 \\ 
  SRMRW & 0.742 & 0.038 & 0.728 & 0.723 \\ 
  SRMRB & 0.598 & 0.067 & 0.804 & 0.352 \\ 
   \bottomrule
\end{tabular}
\end{table}

More fine grained information within/across conditions

i <- 1 ## counter for roc_summary
j <- 1 ## Which class?
for(index in INDEX){
  for(est in EST){
    for(s2 in SS_L2){
      for(s1 in SS_L1){
    ## Print out which iteration so we know what we are looking at
    #cat('\n\nROC Analysis in')
    #cat('\nIndex:\t', index)
    #cat('\nClassification:\t', CLASS[j])
    #cat('\nEstimation Method:\t', est)
    #cat('\nLevel-2 Sample Size:\t', s2)
    #cat('\nLevel-1 Sample Size:\t', s1)
    ## Set up iteration key
    key <- paste0(index,'.',CLASS[j],'.',est,'.', s2,'.',s1)
    # Subset data as  needed
    if(est == 'ALL' & s2 == 'ALL' & s1 == 'ALL') mydata <- sim_results
    if(est != 'ALL' & s2 == 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, Estimator == est)
    }
    if(est == 'ALL' & s2 != 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, ss_l2 == s2)
    }
    if(est == 'ALL' & s2 == 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, ss_l1 == s1)
    }
    if(est != 'ALL' & s2 != 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l2 == s2)
    }
    if(est != 'ALL' & s2 == 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l1 == s1)
    }
    if(est == 'ALL' & s2 != 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, ss_l2 == s2, ss_l1 == s1)
    }
    if(est != 'ALL' & s2 != 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l2 == s2, ss_l1 == s1)
    }
    ## Create formula
    model <- as.formula(paste0(CLASS[j], '~', index))
    ## Fit ROC curve
    fit_roc[[key]] <-  roc(model, data=mydata, quiet=T,
                           plot =F, ci=TRUE, print.auc=TRUE)
    ## Create a plot of "smoothed" curve for plotting
    fit_roc_smooth[[key]] <-  smooth(roc(model, data=mydata))
    ## Compute partial AUC for specificity .8-1
    p.auc <- auc(fit_roc[[key]], partial.auc = c(1,.8),
                 partial.auc.focus = 'sp', partial.auc.correct = T)
    ## get summary info
    roc_summary[i, 2] <- index
    roc_summary[i, 1] <- CLASS[j]
    roc_summary[i, 3] <- est ##estimator
    roc_summary[i, 4] <- s2 ## level-2 sample size
    roc_summary[i, 5] <- s1 ## level-1 sample size
    roc_summary[i, 6] <- fit_roc[[key]]$auc ## total AUC
    roc_summary[i, 7] <- p.auc ## corrected partial AUC (.5 is no discrimination)
    roc_summary[i, 8] <- fit_roc_smooth[[key]]$auc ## smoothed AUC
    roc_summary[i, 9:11] <- coords(fit_roc[[key]], "best", 
                                   ret=c("threshold", "specificity", 'sensitivity'),
                                   transpose=TRUE)
    
    ## add number of C and number of miss models in analysis
    n.C <- nrow(mydata[ mydata[, CLASS[j]] == 1, ])
    n.M <- nrow(mydata[ mydata[, CLASS[j]] == 0, ])
    roc_summary[i, 12] <- n.C
    roc_summary[i, 13] <- n.M
    
    ## print summary
    #cat('\n\nSummary of ROC:\n')
    #print(roc_summary[i, ])
    ## add to summary iterator
    i <- i + 1
      } ## end loop around ss l1
    } ## End loop around ss l2
  } ## End loop around estimator
} ## End loop round index
kable(roc_summary[1:400, ], format = 'html', digits=3) %>%
  kable_styling(full_width = T)

Classification	Index	Estimator	Level-2 SS	Level-1 SS	AUC	partial-AUC	Smoothed-AUC	Threshold	Specificity	Sensitivity	Num-C	Num-Mis
C	CFI	ALL	ALL	ALL	0.816	0.637	0.846	0.977	0.702	0.855	83510	223868
C	CFI	ALL	ALL	5	0.784	0.649	0.799	0.971	0.749	0.752	23460	63082
C	CFI	ALL	ALL	10	0.842	0.672	0.860	0.972	0.718	0.873	28208	75749
C	CFI	ALL	ALL	30	0.829	0.612	0.809	0.986	0.643	0.944	31842	85037
C	CFI	ALL	30	ALL	0.679	0.544	0.713	0.968	0.587	0.739	16157	43767
C	CFI	ALL	30	5	0.636	0.546	0.640	0.899	0.490	0.719	3994	10825
C	CFI	ALL	30	10	0.708	0.560	0.721	0.959	0.597	0.728	5297	14595
C	CFI	ALL	30	30	0.706	0.535	0.639	0.971	0.467	0.943	6866	18347
C	CFI	ALL	50	ALL	0.774	0.597	0.802	0.970	0.642	0.826	19330	52168
C	CFI	ALL	50	5	0.720	0.585	0.727	0.956	0.654	0.680	5037	13894
C	CFI	ALL	50	10	0.818	0.642	0.830	0.965	0.683	0.843	6490	17559
C	CFI	ALL	50	30	0.795	0.580	0.763	0.983	0.589	0.969	7803	20715
C	CFI	ALL	100	ALL	0.864	0.684	0.884	0.977	0.723	0.905	22743	60844
C	CFI	ALL	100	5	0.850	0.687	0.859	0.967	0.723	0.845	6487	17448
C	CFI	ALL	100	10	0.889	0.725	0.901	0.974	0.741	0.946	7852	20843
C	CFI	ALL	100	30	0.865	0.659	0.841	0.991	0.701	0.971	8404	22553
C	CFI	ALL	200	ALL	0.912	0.767	0.922	0.980	0.764	0.965	25280	67089
C	CFI	ALL	200	5	0.904	0.747	0.914	0.977	0.787	0.940	7942	20915
C	CFI	ALL	200	10	0.916	0.772	0.913	0.983	0.789	0.963	8569	22752
C	CFI	ALL	200	30	0.931	0.811	0.923	0.994	0.797	0.965	8769	23422
C	CFI	MLR	ALL	ALL	0.841	0.710	0.840	0.967	0.746	0.834	30018	85647
C	CFI	MLR	ALL	5	0.773	0.678	0.777	0.971	0.814	0.663	8945	24572
C	CFI	MLR	ALL	10	0.854	0.717	0.856	0.967	0.772	0.816	10090	28785
C	CFI	MLR	ALL	30	0.897	0.742	0.891	0.977	0.733	0.933	10983	32290
C	CFI	MLR	30	ALL	0.747	0.639	0.739	0.940	0.726	0.698	6295	17854
C	CFI	MLR	30	5	0.666	0.569	0.664	0.888	0.673	0.589	1726	4739
C	CFI	MLR	30	10	0.774	0.622	0.773	0.918	0.612	0.815	2054	5832
C	CFI	MLR	30	30	0.873	0.686	0.876	0.957	0.693	0.966	2515	7283
C	CFI	MLR	50	ALL	0.828	0.674	0.827	0.956	0.713	0.835	7124	20417
C	CFI	MLR	50	5	0.747	0.608	0.748	0.929	0.612	0.763	2032	5724
C	CFI	MLR	50	10	0.859	0.690	0.859	0.956	0.734	0.867	2387	6790
C	CFI	MLR	50	30	0.894	0.719	0.889	0.979	0.739	0.969	2705	7903
C	CFI	MLR	100	ALL	0.893	0.735	0.898	0.974	0.750	0.918	8014	22901
C	CFI	MLR	100	5	0.866	0.705	0.867	0.959	0.700	0.897	2417	6637
C	CFI	MLR	100	10	0.906	0.748	0.901	0.974	0.768	0.967	2760	7823
C	CFI	MLR	100	30	0.918	0.775	0.912	0.991	0.792	0.970	2837	8441
C	CFI	MLR	200	ALL	0.917	0.778	0.911	0.981	0.767	0.973	8585	24475
C	CFI	MLR	200	5	0.911	0.761	0.906	0.976	0.787	0.958	2770	7472
C	CFI	MLR	200	10	0.923	0.788	0.915	0.986	0.809	0.975	2889	8340
C	CFI	MLR	200	30	0.938	0.827	0.932	0.995	0.824	0.986	2926	8663
C	CFI	ULSMV	ALL	ALL	0.780	0.587	0.841	0.973	0.628	0.888	27515	71584
C	CFI	ULSMV	ALL	5	0.775	0.618	0.807	0.967	0.697	0.794	7500	19987
C	CFI	ULSMV	ALL	10	0.818	0.625	0.854	0.970	0.687	0.888	9333	24300
C	CFI	ULSMV	ALL	30	0.762	0.561	0.725	0.987	0.531	0.975	10682	27297
C	CFI	ULSMV	30	ALL	0.628	0.524	0.720	0.982	0.431	0.821	5208	13635
C	CFI	ULSMV	30	5	0.617	0.531	0.638	0.981	0.633	0.575	1203	3200
C	CFI	ULSMV	30	10	0.665	0.535	0.726	0.978	0.498	0.795	1719	4632
C	CFI	ULSMV	30	30	0.614	0.516	0.481	0.988	0.268	0.975	2286	5803
C	CFI	ULSMV	50	ALL	0.712	0.554	0.770	0.973	0.552	0.826	6356	16505
C	CFI	ULSMV	50	5	0.686	0.564	0.699	0.967	0.647	0.639	1595	4319
C	CFI	ULSMV	50	10	0.774	0.603	0.801	0.970	0.659	0.804	2122	5551
C	CFI	ULSMV	50	30	0.694	0.535	0.643	0.989	0.417	0.974	2639	6635
C	CFI	ULSMV	100	ALL	0.827	0.634	0.855	0.971	0.667	0.906	7529	19571
C	CFI	ULSMV	100	5	0.829	0.671	0.840	0.965	0.709	0.823	2076	5570
C	CFI	ULSMV	100	10	0.874	0.711	0.886	0.970	0.742	0.908	2619	6741
C	CFI	ULSMV	100	30	0.798	0.584	0.740	0.989	0.583	0.967	2834	7260
C	CFI	ULSMV	200	ALL	0.911	0.764	0.919	0.975	0.785	0.957	8422	21873
C	CFI	ULSMV	200	5	0.898	0.738	0.908	0.967	0.766	0.950	2626	6898
C	CFI	ULSMV	200	10	0.912	0.761	0.907	0.970	0.796	0.971	2873	7376
C	CFI	ULSMV	200	30	0.929	0.805	0.921	0.987	0.794	0.975	2923	7599
C	CFI	WLSMV	ALL	ALL	0.836	0.649	0.871	0.982	0.682	0.896	25977	66637
C	CFI	WLSMV	ALL	5	0.818	0.656	0.835	0.972	0.704	0.838	7015	18523
C	CFI	WLSMV	ALL	10	0.866	0.686	0.880	0.982	0.724	0.902	8785	22664
C	CFI	WLSMV	ALL	30	0.841	0.625	0.797	0.995	0.669	0.951	10177	25450
C	CFI	WLSMV	30	ALL	0.705	0.552	0.758	0.983	0.546	0.811	4654	12278
C	CFI	WLSMV	30	5	0.675	0.558	0.680	0.936	0.455	0.807	1065	2886
C	CFI	WLSMV	30	10	0.763	0.588	0.776	0.981	0.624	0.804	1524	4131
C	CFI	WLSMV	30	30	0.700	0.537	0.564	0.987	0.397	0.977	2065	5261
C	CFI	WLSMV	50	ALL	0.802	0.625	0.830	0.980	0.647	0.848	5850	15246
C	CFI	WLSMV	50	5	0.747	0.596	0.751	0.964	0.618	0.769	1410	3851
C	CFI	WLSMV	50	10	0.843	0.663	0.841	0.976	0.688	0.880	1981	5218
C	CFI	WLSMV	50	30	0.828	0.612	0.771	0.992	0.630	0.966	2459	6177
C	CFI	WLSMV	100	ALL	0.876	0.703	0.891	0.979	0.708	0.936	7200	18372
C	CFI	WLSMV	100	5	0.859	0.690	0.862	0.972	0.711	0.880	1994	5241
C	CFI	WLSMV	100	10	0.893	0.721	0.882	0.983	0.746	0.947	2473	6279
C	CFI	WLSMV	100	30	0.889	0.706	0.863	0.994	0.735	0.977	2733	6852
C	CFI	WLSMV	200	ALL	0.910	0.761	0.898	0.986	0.756	0.960	8273	20741
C	CFI	WLSMV	200	5	0.904	0.742	0.897	0.979	0.769	0.962	2546	6545
C	CFI	WLSMV	200	10	0.917	0.772	0.907	0.988	0.783	0.981	2807	7036
C	CFI	WLSMV	200	30	0.930	0.806	0.919	0.997	0.808	0.969	2920	7160
C	TLI	ALL	ALL	ALL	0.815	0.637	0.845	0.972	0.702	0.855	83510	223868
C	TLI	ALL	ALL	5	0.784	0.649	0.799	0.965	0.749	0.752	23460	63082
C	TLI	ALL	ALL	10	0.841	0.672	0.859	0.966	0.717	0.873	28208	75749
C	TLI	ALL	ALL	30	0.828	0.612	0.810	0.983	0.643	0.945	31842	85037
C	TLI	ALL	30	ALL	0.678	0.544	0.713	0.961	0.587	0.739	16157	43767
C	TLI	ALL	30	5	0.635	0.546	0.639	0.878	0.487	0.720	3994	10825
C	TLI	ALL	30	10	0.707	0.560	0.721	0.951	0.597	0.728	5297	14595
C	TLI	ALL	30	30	0.706	0.535	0.640	0.965	0.467	0.943	6866	18347
C	TLI	ALL	50	ALL	0.773	0.597	0.802	0.964	0.641	0.826	19330	52168
C	TLI	ALL	50	5	0.719	0.585	0.727	0.948	0.653	0.681	5037	13894
C	TLI	ALL	50	10	0.818	0.642	0.830	0.958	0.682	0.843	6490	17559
C	TLI	ALL	50	30	0.795	0.580	0.764	0.980	0.588	0.969	7803	20715
C	TLI	ALL	100	ALL	0.864	0.684	0.884	0.972	0.722	0.905	22743	60844
C	TLI	ALL	100	5	0.849	0.687	0.859	0.961	0.723	0.845	6487	17448
C	TLI	ALL	100	10	0.889	0.724	0.901	0.968	0.739	0.948	7852	20843
C	TLI	ALL	100	30	0.865	0.659	0.842	0.989	0.701	0.971	8404	22553
C	TLI	ALL	200	ALL	0.912	0.767	0.922	0.976	0.763	0.965	25280	67089
C	TLI	ALL	200	5	0.904	0.747	0.914	0.972	0.786	0.941	7942	20915
C	TLI	ALL	200	10	0.916	0.772	0.913	0.980	0.789	0.963	8569	22752
C	TLI	ALL	200	30	0.931	0.811	0.923	0.993	0.797	0.965	8769	23422
C	TLI	MLR	ALL	ALL	0.840	0.710	0.840	0.961	0.750	0.830	30018	85647
C	TLI	MLR	ALL	5	0.772	0.678	0.776	0.965	0.814	0.663	8945	24572
C	TLI	MLR	ALL	10	0.853	0.717	0.855	0.960	0.772	0.816	10090	28785
C	TLI	MLR	ALL	30	0.897	0.742	0.891	0.972	0.733	0.933	10983	32290
C	TLI	MLR	30	ALL	0.746	0.639	0.738	0.928	0.725	0.698	6295	17854
C	TLI	MLR	30	5	0.664	0.569	0.663	0.867	0.678	0.582	1726	4739
C	TLI	MLR	30	10	0.773	0.622	0.772	0.902	0.610	0.815	2054	5832
C	TLI	MLR	30	30	0.873	0.686	0.876	0.949	0.693	0.966	2515	7283
C	TLI	MLR	50	ALL	0.827	0.674	0.827	0.947	0.713	0.835	7124	20417
C	TLI	MLR	50	5	0.746	0.607	0.747	0.915	0.610	0.763	2032	5724
C	TLI	MLR	50	10	0.859	0.690	0.859	0.947	0.734	0.867	2387	6790
C	TLI	MLR	50	30	0.894	0.719	0.889	0.975	0.739	0.969	2705	7903
C	TLI	MLR	100	ALL	0.893	0.735	0.898	0.968	0.750	0.918	8014	22901
C	TLI	MLR	100	5	0.865	0.705	0.867	0.951	0.699	0.897	2417	6637
C	TLI	MLR	100	10	0.906	0.748	0.901	0.969	0.768	0.967	2760	7823
C	TLI	MLR	100	30	0.918	0.775	0.912	0.989	0.792	0.970	2837	8441
C	TLI	MLR	200	ALL	0.917	0.778	0.911	0.977	0.767	0.973	8585	24475
C	TLI	MLR	200	5	0.911	0.761	0.906	0.972	0.790	0.955	2770	7472
C	TLI	MLR	200	10	0.923	0.788	0.915	0.984	0.809	0.975	2889	8340
C	TLI	MLR	200	30	0.938	0.827	0.932	0.994	0.824	0.986	2926	8663
C	TLI	ULSMV	ALL	ALL	0.780	0.587	0.841	0.968	0.627	0.888	27515	71584
C	TLI	ULSMV	ALL	5	0.775	0.618	0.806	0.960	0.696	0.794	7500	19987
C	TLI	ULSMV	ALL	10	0.818	0.625	0.854	0.964	0.686	0.888	9333	24300
C	TLI	ULSMV	ALL	30	0.762	0.561	0.727	0.985	0.531	0.975	10682	27297
C	TLI	ULSMV	30	ALL	0.628	0.524	0.720	0.979	0.430	0.821	5208	13635
C	TLI	ULSMV	30	5	0.616	0.531	0.638	0.977	0.632	0.575	1203	3200
C	TLI	ULSMV	30	10	0.665	0.535	0.725	0.973	0.498	0.795	1719	4632
C	TLI	ULSMV	30	30	0.614	0.516	0.482	0.985	0.268	0.975	2286	5803
C	TLI	ULSMV	50	ALL	0.712	0.554	0.770	0.968	0.552	0.826	6356	16505
C	TLI	ULSMV	50	5	0.685	0.564	0.699	0.959	0.638	0.647	1595	4319
C	TLI	ULSMV	50	10	0.774	0.603	0.801	0.966	0.663	0.799	2122	5551
C	TLI	ULSMV	50	30	0.694	0.535	0.645	0.986	0.417	0.974	2639	6635
C	TLI	ULSMV	100	ALL	0.827	0.634	0.855	0.965	0.666	0.906	7529	19571
C	TLI	ULSMV	100	5	0.829	0.670	0.840	0.958	0.708	0.823	2076	5570
C	TLI	ULSMV	100	10	0.874	0.711	0.886	0.965	0.743	0.907	2619	6741
C	TLI	ULSMV	100	30	0.798	0.584	0.740	0.987	0.583	0.967	2834	7260
C	TLI	ULSMV	200	ALL	0.911	0.764	0.919	0.970	0.785	0.956	8422	21873
C	TLI	ULSMV	200	5	0.898	0.738	0.908	0.960	0.766	0.950	2626	6898
C	TLI	ULSMV	200	10	0.912	0.761	0.907	0.965	0.797	0.969	2873	7376
C	TLI	ULSMV	200	30	0.929	0.805	0.921	0.985	0.794	0.975	2923	7599
C	TLI	WLSMV	ALL	ALL	0.836	0.649	0.871	0.979	0.682	0.896	25977	66637
C	TLI	WLSMV	ALL	5	0.818	0.656	0.835	0.966	0.703	0.838	7015	18523
C	TLI	WLSMV	ALL	10	0.866	0.686	0.880	0.979	0.724	0.902	8785	22664
C	TLI	WLSMV	ALL	30	0.841	0.625	0.797	0.994	0.669	0.951	10177	25450
C	TLI	WLSMV	30	ALL	0.705	0.552	0.758	0.979	0.547	0.809	4654	12278
C	TLI	WLSMV	30	5	0.674	0.558	0.680	0.924	0.453	0.807	1065	2886
C	TLI	WLSMV	30	10	0.762	0.588	0.776	0.978	0.627	0.801	1524	4131
C	TLI	WLSMV	30	30	0.700	0.537	0.565	0.984	0.397	0.977	2065	5261
C	TLI	WLSMV	50	ALL	0.801	0.625	0.830	0.976	0.648	0.846	5850	15246
C	TLI	WLSMV	50	5	0.747	0.596	0.751	0.956	0.617	0.769	1410	3851
C	TLI	WLSMV	50	10	0.843	0.663	0.841	0.971	0.687	0.880	1981	5218
C	TLI	WLSMV	50	30	0.828	0.612	0.771	0.991	0.629	0.967	2459	6177
C	TLI	WLSMV	100	ALL	0.876	0.703	0.892	0.975	0.708	0.936	7200	18372
C	TLI	WLSMV	100	5	0.858	0.690	0.862	0.966	0.711	0.880	1994	5241
C	TLI	WLSMV	100	10	0.893	0.721	0.882	0.980	0.746	0.947	2473	6279
C	TLI	WLSMV	100	30	0.889	0.706	0.863	0.993	0.735	0.977	2733	6852
C	TLI	WLSMV	200	ALL	0.910	0.761	0.898	0.984	0.756	0.960	8273	20741
C	TLI	WLSMV	200	5	0.904	0.742	0.897	0.974	0.769	0.962	2546	6545
C	TLI	WLSMV	200	10	0.917	0.772	0.907	0.986	0.783	0.981	2807	7036
C	TLI	WLSMV	200	30	0.930	0.806	0.919	0.997	0.808	0.969	2920	7160
C	RMSEA	ALL	ALL	ALL	0.803	0.635	0.830	0.015	0.685	0.829	83510	223868
C	RMSEA	ALL	ALL	5	0.776	0.644	0.790	0.019	0.722	0.757	23460	63082
C	RMSEA	ALL	ALL	10	0.832	0.668	0.847	0.016	0.725	0.836	28208	75749
C	RMSEA	ALL	ALL	30	0.816	0.612	0.798	0.009	0.665	0.869	31842	85037
C	RMSEA	ALL	30	ALL	0.664	0.544	0.688	0.020	0.549	0.734	16157	43767
C	RMSEA	ALL	30	5	0.614	0.546	0.614	0.021	0.695	0.477	3994	10825
C	RMSEA	ALL	30	10	0.681	0.560	0.680	0.018	0.623	0.657	5297	14595
C	RMSEA	ALL	30	30	0.691	0.535	0.615	0.022	0.398	0.958	6866	18347
C	RMSEA	ALL	50	ALL	0.758	0.596	0.778	0.016	0.636	0.776	19330	52168
C	RMSEA	ALL	50	5	0.702	0.582	0.704	0.023	0.635	0.670	5037	13894
C	RMSEA	ALL	50	10	0.801	0.635	0.802	0.020	0.653	0.819	6490	17559
C	RMSEA	ALL	50	30	0.781	0.580	0.734	0.013	0.551	0.933	7803	20715
C	RMSEA	ALL	100	ALL	0.854	0.678	0.862	0.016	0.698	0.892	22743	60844
C	RMSEA	ALL	100	5	0.842	0.680	0.843	0.021	0.676	0.870	6487	17448
C	RMSEA	ALL	100	10	0.886	0.716	0.877	0.017	0.720	0.943	7852	20843
C	RMSEA	ALL	100	30	0.859	0.656	0.825	0.010	0.687	0.961	8404	22553
C	RMSEA	ALL	200	ALL	0.905	0.758	0.897	0.014	0.736	0.955	25280	67089
C	RMSEA	ALL	200	5	0.902	0.742	0.896	0.017	0.764	0.953	7942	20915
C	RMSEA	ALL	200	10	0.918	0.776	0.909	0.013	0.790	0.971	8569	22752
C	RMSEA	ALL	200	30	0.929	0.808	0.922	0.007	0.806	0.951	8769	23422
C	RMSEA	MLR	ALL	ALL	0.830	0.706	0.829	0.024	0.735	0.843	30018	85647
C	RMSEA	MLR	ALL	5	0.758	0.671	0.762	0.023	0.782	0.684	8945	24572
C	RMSEA	MLR	ALL	10	0.843	0.711	0.844	0.025	0.745	0.840	10090	28785
C	RMSEA	MLR	ALL	30	0.896	0.738	0.894	0.019	0.729	0.944	10983	32290
C	RMSEA	MLR	30	ALL	0.725	0.634	0.720	0.029	0.741	0.660	6295	17854
C	RMSEA	MLR	30	5	0.653	0.562	0.655	0.051	0.540	0.678	1726	4739
C	RMSEA	MLR	30	10	0.755	0.612	0.756	0.035	0.642	0.744	2054	5832
C	RMSEA	MLR	30	30	0.870	0.679	0.873	0.026	0.700	0.961	2515	7283
C	RMSEA	MLR	50	ALL	0.814	0.669	0.813	0.026	0.716	0.814	7124	20417
C	RMSEA	MLR	50	5	0.730	0.599	0.731	0.034	0.593	0.752	2032	5724
C	RMSEA	MLR	50	10	0.850	0.680	0.851	0.026	0.725	0.849	2387	6790
C	RMSEA	MLR	50	30	0.892	0.715	0.887	0.018	0.736	0.979	2705	7903
C	RMSEA	MLR	100	ALL	0.889	0.729	0.892	0.020	0.747	0.916	8014	22901
C	RMSEA	MLR	100	5	0.859	0.695	0.859	0.024	0.717	0.871	2417	6637
C	RMSEA	MLR	100	10	0.903	0.740	0.897	0.020	0.762	0.968	2760	7823
C	RMSEA	MLR	100	30	0.917	0.772	0.911	0.012	0.779	0.982	2837	8441
C	RMSEA	MLR	200	ALL	0.915	0.772	0.908	0.017	0.759	0.979	8585	24475
C	RMSEA	MLR	200	5	0.908	0.751	0.900	0.019	0.782	0.964	2770	7472
C	RMSEA	MLR	200	10	0.921	0.781	0.912	0.014	0.801	0.981	2889	8340
C	RMSEA	MLR	200	30	0.937	0.825	0.930	0.008	0.832	0.975	2926	8663
C	RMSEA	ULSMV	ALL	ALL	0.770	0.587	0.807	0.012	0.596	0.855	27515	71584
C	RMSEA	ULSMV	ALL	5	0.784	0.617	0.807	0.017	0.656	0.815	7500	19987
C	RMSEA	ULSMV	ALL	10	0.817	0.624	0.828	0.012	0.692	0.843	9333	24300
C	RMSEA	ULSMV	ALL	30	0.754	0.561	0.708	0.006	0.552	0.905	10682	27297
C	RMSEA	ULSMV	30	ALL	0.628	0.524	0.675	0.008	0.448	0.771	5208	13635
C	RMSEA	ULSMV	30	5	0.621	0.531	0.635	0.011	0.619	0.587	1203	3200
C	RMSEA	ULSMV	30	10	0.663	0.535	0.666	0.014	0.428	0.838	1719	4632
C	RMSEA	ULSMV	30	30	0.391	NA	0.564	-Inf	0.000	1.000	2286	5803
C	RMSEA	ULSMV	50	ALL	0.706	0.554	0.728	0.009	0.590	0.749	6356	16505
C	RMSEA	ULSMV	50	5	0.686	0.564	0.690	0.016	0.614	0.661	1595	4319
C	RMSEA	ULSMV	50	10	0.770	0.603	0.761	0.014	0.614	0.802	2122	5551
C	RMSEA	ULSMV	50	30	0.688	0.535	0.587	0.007	0.430	0.907	2639	6635
C	RMSEA	ULSMV	100	ALL	0.813	0.632	0.810	0.012	0.671	0.839	7529	19571
C	RMSEA	ULSMV	100	5	0.829	0.667	0.830	0.017	0.700	0.826	2076	5570
C	RMSEA	ULSMV	100	10	0.870	0.706	0.863	0.014	0.707	0.900	2619	6741
C	RMSEA	ULSMV	100	30	0.789	0.584	0.715	0.006	0.574	0.924	2834	7260
C	RMSEA	ULSMV	200	ALL	0.893	0.752	0.889	0.013	0.689	0.947	8422	21873
C	RMSEA	ULSMV	200	5	0.896	0.737	0.895	0.015	0.784	0.907	2626	6898
C	RMSEA	ULSMV	200	10	0.916	0.774	0.909	0.012	0.800	0.944	2873	7376
C	RMSEA	ULSMV	200	30	0.926	0.807	0.925	0.006	0.805	0.929	2923	7599
C	RMSEA	WLSMV	ALL	ALL	0.832	0.648	0.864	0.014	0.689	0.872	25977	66637
C	RMSEA	WLSMV	ALL	5	0.820	0.655	0.837	0.019	0.719	0.819	7015	18523
C	RMSEA	WLSMV	ALL	10	0.866	0.685	0.876	0.016	0.713	0.915	8785	22664
C	RMSEA	WLSMV	ALL	30	0.839	0.625	0.804	0.008	0.672	0.936	10177	25450
C	RMSEA	WLSMV	30	ALL	0.700	0.552	0.743	0.013	0.545	0.786	4654	12278
C	RMSEA	WLSMV	30	5	0.674	0.558	0.679	0.021	0.614	0.652	1065	2886
C	RMSEA	WLSMV	30	10	0.759	0.588	0.767	0.018	0.552	0.863	1524	4131
C	RMSEA	WLSMV	30	30	0.694	0.537	0.573	0.008	0.446	0.895	2065	5261
C	RMSEA	WLSMV	50	ALL	0.790	0.625	0.811	0.011	0.718	0.736	5850	15246
C	RMSEA	WLSMV	50	5	0.743	0.595	0.747	0.020	0.644	0.727	1410	3851
C	RMSEA	WLSMV	50	10	0.842	0.663	0.838	0.018	0.664	0.897	1981	5218
C	RMSEA	WLSMV	50	30	0.821	0.612	0.782	0.009	0.617	0.934	2459	6177
C	RMSEA	WLSMV	100	ALL	0.873	0.703	0.886	0.015	0.723	0.910	7200	18372
C	RMSEA	WLSMV	100	5	0.858	0.690	0.863	0.021	0.673	0.914	1994	5241
C	RMSEA	WLSMV	100	10	0.893	0.722	0.882	0.017	0.728	0.965	2473	6279
C	RMSEA	WLSMV	100	30	0.889	0.706	0.859	0.009	0.737	0.972	2733	6852
C	RMSEA	WLSMV	200	ALL	0.910	0.761	0.898	0.014	0.752	0.963	8273	20741
C	RMSEA	WLSMV	200	5	0.904	0.742	0.895	0.017	0.771	0.961	2546	6545
C	RMSEA	WLSMV	200	10	0.918	0.775	0.908	0.013	0.786	0.979	2807	7036
C	RMSEA	WLSMV	200	30	0.930	0.806	0.919	0.006	0.811	0.965	2920	7160
C	SRMRW	ALL	ALL	ALL	0.742	0.608	0.717	0.038	0.728	0.723	83510	223868
C	SRMRW	ALL	ALL	5	0.685	0.606	0.673	0.046	0.741	0.581	23460	63082
C	SRMRW	ALL	ALL	10	0.762	0.610	0.741	0.041	0.696	0.754	28208	75749
C	SRMRW	ALL	ALL	30	0.812	0.605	0.792	0.035	0.661	0.940	31842	85037
C	SRMRW	ALL	30	ALL	0.656	0.590	0.647	0.044	0.770	0.498	16157	43767
C	SRMRW	ALL	30	5	0.620	0.554	0.617	0.072	0.702	0.479	3994	10825
C	SRMRW	ALL	30	10	0.706	0.585	0.693	0.055	0.628	0.701	5297	14595
C	SRMRW	ALL	30	30	0.779	0.593	0.754	0.039	0.661	0.872	6866	18347
C	SRMRW	ALL	50	ALL	0.715	0.601	0.700	0.042	0.725	0.639	19330	52168
C	SRMRW	ALL	50	5	0.685	0.578	0.681	0.062	0.642	0.635	5037	13894
C	SRMRW	ALL	50	10	0.770	0.599	0.753	0.047	0.633	0.834	6490	17559
C	SRMRW	ALL	50	30	0.805	0.596	0.790	0.035	0.668	0.926	7803	20715
C	SRMRW	ALL	100	ALL	0.791	0.605	0.777	0.038	0.698	0.806	22743	60844
C	SRMRW	ALL	100	5	0.782	0.608	0.775	0.048	0.655	0.809	6487	17448
C	SRMRW	ALL	100	10	0.817	0.604	0.816	0.038	0.662	0.932	7852	20843
C	SRMRW	ALL	100	30	0.822	0.601	0.832	0.032	0.664	0.973	8404	22553
C	SRMRW	ALL	200	ALL	0.828	0.610	0.835	0.036	0.654	0.963	25280	67089
C	SRMRW	ALL	200	5	0.830	0.620	0.841	0.040	0.649	0.950	7942	20915
C	SRMRW	ALL	200	10	0.833	0.612	0.856	0.033	0.663	0.986	8569	22752
C	SRMRW	ALL	200	30	0.830	0.608	0.849	0.024	0.679	0.982	8769	23422
C	SRMRW	MLR	ALL	ALL	0.754	0.607	0.728	0.036	0.720	0.773	30018	85647
C	SRMRW	MLR	ALL	5	0.698	0.613	0.694	0.040	0.779	0.564	8945	24572
C	SRMRW	MLR	ALL	10	0.795	0.611	0.789	0.038	0.687	0.822	10090	28785
C	SRMRW	MLR	ALL	30	0.832	0.611	0.840	0.031	0.661	0.998	10983	32290
C	SRMRW	MLR	30	ALL	0.674	0.596	0.671	0.042	0.738	0.568	6295	17854
C	SRMRW	MLR	30	5	0.704	0.569	0.707	0.071	0.486	0.827	1726	4739
C	SRMRW	MLR	30	10	0.795	0.597	0.799	0.048	0.634	0.870	2054	5832
C	SRMRW	MLR	30	30	0.826	0.604	0.834	0.033	0.652	0.999	2515	7283
C	SRMRW	MLR	50	ALL	0.737	0.606	0.730	0.037	0.744	0.682	7124	20417
C	SRMRW	MLR	50	5	0.768	0.597	0.774	0.056	0.571	0.855	2032	5724
C	SRMRW	MLR	50	10	0.825	0.610	0.844	0.041	0.629	0.965	2387	6790
C	SRMRW	MLR	50	30	0.831	0.609	0.833	0.029	0.660	1.000	2705	7903
C	SRMRW	MLR	100	ALL	0.816	0.606	0.821	0.037	0.672	0.903	8014	22901
C	SRMRW	MLR	100	5	0.831	0.625	0.846	0.043	0.632	0.956	2417	6637
C	SRMRW	MLR	100	10	0.834	0.616	0.839	0.032	0.651	1.000	2760	7823
C	SRMRW	MLR	100	30	0.835	0.615	0.837	0.025	0.664	1.000	2837	8441
C	SRMRW	MLR	200	ALL	0.832	0.610	0.861	0.032	0.661	0.993	8585	24475
C	SRMRW	MLR	200	5	0.850	0.640	0.868	0.032	0.667	0.980	2770	7472
C	SRMRW	MLR	200	10	0.844	0.631	0.848	0.024	0.655	1.000	2889	8340
C	SRMRW	MLR	200	30	0.840	0.623	0.842	0.013	0.664	1.000	2926	8663
C	SRMRW	ULSMV	ALL	ALL	0.740	0.633	0.715	0.045	0.741	0.715	27515	71584
C	SRMRW	ULSMV	ALL	5	0.672	0.615	0.660	0.050	0.788	0.539	7500	19987
C	SRMRW	ULSMV	ALL	10	0.752	0.634	0.729	0.045	0.737	0.710	9333	24300
C	SRMRW	ULSMV	ALL	30	0.819	0.644	0.789	0.042	0.675	0.904	10682	27297
C	SRMRW	ULSMV	30	ALL	0.642	0.590	0.636	0.052	0.789	0.459	5208	13635
C	SRMRW	ULSMV	30	5	0.607	0.537	0.604	0.096	0.586	0.578	1203	3200
C	SRMRW	ULSMV	30	10	0.691	0.581	0.681	0.069	0.593	0.713	1719	4632
C	SRMRW	ULSMV	30	30	0.758	0.595	0.734	0.047	0.697	0.773	2286	5803
C	SRMRW	ULSMV	50	ALL	0.709	0.614	0.697	0.048	0.770	0.595	6356	16505
C	SRMRW	ULSMV	50	5	0.690	0.573	0.686	0.075	0.620	0.666	1595	4319
C	SRMRW	ULSMV	50	10	0.764	0.604	0.746	0.054	0.682	0.770	2122	5551
C	SRMRW	ULSMV	50	30	0.812	0.606	0.791	0.042	0.702	0.879	2639	6635
C	SRMRW	ULSMV	100	ALL	0.803	0.633	0.785	0.046	0.689	0.835	7529	19571
C	SRMRW	ULSMV	100	5	0.791	0.609	0.783	0.056	0.666	0.818	2076	5570
C	SRMRW	ULSMV	100	10	0.834	0.616	0.828	0.043	0.699	0.913	2619	6741
C	SRMRW	ULSMV	100	30	0.846	0.614	0.849	0.035	0.706	0.942	2834	7260
C	SRMRW	ULSMV	200	ALL	0.855	0.655	0.857	0.042	0.664	0.971	8422	21873
C	SRMRW	ULSMV	200	5	0.843	0.619	0.852	0.043	0.709	0.935	2626	6898
C	SRMRW	ULSMV	200	10	0.856	0.622	0.869	0.033	0.738	0.957	2873	7376
C	SRMRW	ULSMV	200	30	0.860	0.622	0.877	0.024	0.778	0.947	2923	7599
C	SRMRW	WLSMV	ALL	ALL	0.750	0.594	0.727	0.044	0.671	0.813	25977	66637
C	SRMRW	WLSMV	ALL	5	0.696	0.604	0.687	0.048	0.747	0.610	7015	18523
C	SRMRW	WLSMV	ALL	10	0.776	0.604	0.771	0.045	0.664	0.816	8785	22664
C	SRMRW	WLSMV	ALL	30	0.806	0.598	0.821	0.039	0.600	0.997	10177	25450
C	SRMRW	WLSMV	30	ALL	0.682	0.590	0.673	0.050	0.738	0.585	4654	12278
C	SRMRW	WLSMV	30	5	0.679	0.556	0.680	0.085	0.562	0.714	1065	2886
C	SRMRW	WLSMV	30	10	0.777	0.585	0.781	0.060	0.598	0.871	1524	4131
C	SRMRW	WLSMV	30	30	0.805	0.590	0.814	0.042	0.597	0.999	2065	5261
C	SRMRW	WLSMV	50	ALL	0.733	0.594	0.723	0.047	0.689	0.738	5850	15246
C	SRMRW	WLSMV	50	5	0.745	0.581	0.748	0.069	0.557	0.834	1410	3851
C	SRMRW	WLSMV	50	10	0.808	0.597	0.828	0.049	0.609	0.954	1981	5218
C	SRMRW	WLSMV	50	30	0.811	0.597	0.814	0.037	0.603	1.000	2459	6177
C	SRMRW	WLSMV	100	ALL	0.797	0.594	0.802	0.045	0.627	0.910	7200	18372
C	SRMRW	WLSMV	100	5	0.812	0.606	0.824	0.052	0.590	0.951	1994	5241
C	SRMRW	WLSMV	100	10	0.818	0.607	0.825	0.039	0.599	0.998	2473	6279
C	SRMRW	WLSMV	100	30	0.818	0.608	0.821	0.024	0.600	1.000	2733	6852
C	SRMRW	WLSMV	200	ALL	0.813	0.601	0.844	0.038	0.607	0.992	8273	20741
C	SRMRW	WLSMV	200	5	0.833	0.621	0.851	0.038	0.635	0.974	2546	6545
C	SRMRW	WLSMV	200	10	0.838	0.630	0.846	0.025	0.628	0.980	2807	7036
C	SRMRW	WLSMV	200	30	0.833	0.628	0.838	0.015	0.604	0.992	2920	7160
C	SRMRB	ALL	ALL	ALL	0.598	0.572	0.604	0.067	0.804	0.352	83510	223868
C	SRMRB	ALL	ALL	5	0.567	0.550	0.570	0.074	0.808	0.307	23460	63082
C	SRMRB	ALL	ALL	10	0.596	0.571	0.603	0.069	0.792	0.358	28208	75749
C	SRMRB	ALL	ALL	30	0.630	0.592	0.640	0.063	0.793	0.404	31842	85037
C	SRMRB	ALL	30	ALL	0.565	0.542	0.567	0.119	0.773	0.326	16157	43767
C	SRMRB	ALL	30	5	0.541	0.516	0.538	0.151	0.644	0.426	3994	10825
C	SRMRB	ALL	30	10	0.558	0.532	0.557	0.127	0.709	0.388	5297	14595
C	SRMRB	ALL	30	30	0.598	0.564	0.603	0.110	0.798	0.353	6866	18347
C	SRMRB	ALL	50	ALL	0.597	0.563	0.600	0.096	0.761	0.383	19330	52168
C	SRMRB	ALL	50	5	0.562	0.526	0.559	0.123	0.589	0.511	5037	13894
C	SRMRB	ALL	50	10	0.588	0.556	0.588	0.098	0.763	0.383	6490	17559
C	SRMRB	ALL	50	30	0.645	0.595	0.654	0.086	0.799	0.406	7803	20715
C	SRMRB	ALL	100	ALL	0.648	0.598	0.651	0.074	0.721	0.497	22743	60844
C	SRMRB	ALL	100	5	0.596	0.556	0.592	0.088	0.663	0.494	6487	17448
C	SRMRB	ALL	100	10	0.648	0.603	0.651	0.074	0.744	0.491	7852	20843
C	SRMRB	ALL	100	30	0.715	0.639	0.734	0.067	0.757	0.530	8404	22553
C	SRMRB	ALL	200	ALL	0.701	0.630	0.704	0.057	0.692	0.603	25280	67089
C	SRMRB	ALL	200	5	0.644	0.590	0.640	0.066	0.693	0.546	7942	20915
C	SRMRB	ALL	200	10	0.711	0.645	0.723	0.054	0.782	0.530	8569	22752
C	SRMRB	ALL	200	30	0.778	0.676	0.805	0.054	0.634	0.742	8769	23422
C	SRMRB	MLR	ALL	ALL	0.600	0.577	0.605	0.083	0.800	0.366	30018	85647
C	SRMRB	MLR	ALL	5	0.573	0.563	0.579	0.093	0.808	0.325	8945	24572
C	SRMRB	MLR	ALL	10	0.600	0.576	0.606	0.083	0.799	0.363	10090	28785
C	SRMRB	MLR	ALL	30	0.636	0.597	0.645	0.082	0.746	0.460	10983	32290
C	SRMRB	MLR	30	ALL	0.565	0.550	0.567	0.143	0.793	0.325	6295	17854
C	SRMRB	MLR	30	5	0.541	0.526	0.540	0.171	0.717	0.371	1726	4739
C	SRMRB	MLR	30	10	0.564	0.545	0.564	0.149	0.753	0.365	2054	5832
C	SRMRB	MLR	30	30	0.608	0.579	0.612	0.135	0.799	0.378	2515	7283
C	SRMRB	MLR	50	ALL	0.610	0.575	0.610	0.119	0.744	0.435	7124	20417
C	SRMRB	MLR	50	5	0.579	0.548	0.577	0.140	0.678	0.464	2032	5724
C	SRMRB	MLR	50	10	0.611	0.581	0.610	0.121	0.747	0.454	2387	6790
C	SRMRB	MLR	50	30	0.664	0.608	0.669	0.110	0.757	0.487	2705	7903
C	SRMRB	MLR	100	ALL	0.673	0.617	0.671	0.093	0.681	0.583	8014	22901
C	SRMRB	MLR	100	5	0.633	0.594	0.629	0.102	0.726	0.523	2417	6637
C	SRMRB	MLR	100	10	0.678	0.631	0.675	0.086	0.806	0.491	2760	7823
C	SRMRB	MLR	100	30	0.746	0.656	0.766	0.084	0.723	0.612	2837	8441
C	SRMRB	MLR	200	ALL	0.732	0.657	0.732	0.069	0.708	0.646	8585	24475
C	SRMRB	MLR	200	5	0.686	0.643	0.682	0.074	0.779	0.543	2770	7472
C	SRMRB	MLR	200	10	0.741	0.675	0.755	0.066	0.782	0.580	2889	8340
C	SRMRB	MLR	200	30	0.819	0.709	0.841	0.064	0.678	0.762	2926	8663
C	SRMRB	ULSMV	ALL	ALL	0.595	0.580	0.606	0.056	0.838	0.324	27515	71584
C	SRMRB	ULSMV	ALL	5	0.563	0.555	0.570	0.064	0.817	0.300	7500	19987
C	SRMRB	ULSMV	ALL	10	0.594	0.580	0.605	0.055	0.852	0.309	9333	24300
C	SRMRB	ULSMV	ALL	30	0.626	0.601	0.637	0.054	0.800	0.403	10682	27297
C	SRMRB	ULSMV	30	ALL	0.567	0.549	0.573	0.106	0.734	0.381	5208	13635
C	SRMRB	ULSMV	30	5	0.545	0.517	0.544	0.141	0.474	0.603	1203	3200
C	SRMRB	ULSMV	30	10	0.562	0.535	0.561	0.108	0.716	0.403	1719	4632
C	SRMRB	ULSMV	30	30	0.593	0.578	0.601	0.096	0.772	0.400	2286	5803
C	SRMRB	ULSMV	50	ALL	0.596	0.576	0.604	0.081	0.795	0.366	6356	16505
C	SRMRB	ULSMV	50	5	0.557	0.531	0.555	0.104	0.593	0.507	1595	4319
C	SRMRB	ULSMV	50	10	0.591	0.566	0.593	0.086	0.747	0.425	2122	5551
C	SRMRB	ULSMV	50	30	0.640	0.620	0.653	0.072	0.848	0.394	2639	6635
C	SRMRB	ULSMV	100	ALL	0.649	0.620	0.655	0.060	0.843	0.398	7529	19571
C	SRMRB	ULSMV	100	5	0.597	0.569	0.595	0.074	0.715	0.464	2076	5570
C	SRMRB	ULSMV	100	10	0.652	0.634	0.656	0.063	0.796	0.489	2619	6741
C	SRMRB	ULSMV	100	30	0.710	0.673	0.731	0.053	0.895	0.441	2834	7260
C	SRMRB	ULSMV	200	ALL	0.701	0.656	0.707	0.047	0.793	0.516	8422	21873
C	SRMRB	ULSMV	200	5	0.646	0.614	0.644	0.053	0.797	0.462	2626	6898
C	SRMRB	ULSMV	200	10	0.714	0.685	0.727	0.044	0.876	0.492	2873	7376
C	SRMRB	ULSMV	200	30	0.779	0.713	0.808	0.038	0.935	0.467	2923	7599
C	SRMRB	WLSMV	ALL	ALL	0.601	0.578	0.609	0.062	0.822	0.345	25977	66637
C	SRMRB	WLSMV	ALL	5	0.570	0.558	0.576	0.069	0.823	0.302	7015	18523
C	SRMRB	WLSMV	ALL	10	0.598	0.577	0.606	0.063	0.818	0.342	8785	22664
C	SRMRB	WLSMV	ALL	30	0.631	0.596	0.640	0.060	0.798	0.407	10177	25450
C	SRMRB	WLSMV	30	ALL	0.576	0.551	0.579	0.117	0.793	0.325	4654	12278
C	SRMRB	WLSMV	30	5	0.551	0.518	0.548	0.152	0.538	0.555	1065	2886
C	SRMRB	WLSMV	30	10	0.573	0.541	0.570	0.122	0.736	0.391	1524	4131
C	SRMRB	WLSMV	30	30	0.608	0.577	0.614	0.109	0.822	0.356	2065	5261
C	SRMRB	WLSMV	50	ALL	0.613	0.579	0.618	0.096	0.730	0.450	5850	15246
C	SRMRB	WLSMV	50	5	0.576	0.534	0.573	0.112	0.642	0.483	1410	3851
C	SRMRB	WLSMV	50	10	0.598	0.567	0.598	0.096	0.774	0.409	1981	5218
C	SRMRB	WLSMV	50	30	0.671	0.620	0.681	0.086	0.819	0.438	2459	6177
C	SRMRB	WLSMV	100	ALL	0.670	0.621	0.670	0.071	0.728	0.534	7200	18372
C	SRMRB	WLSMV	100	5	0.608	0.573	0.603	0.080	0.736	0.462	1994	5241
C	SRMRB	WLSMV	100	10	0.668	0.627	0.667	0.072	0.755	0.530	2473	6279
C	SRMRB	WLSMV	100	30	0.754	0.674	0.774	0.064	0.795	0.558	2733	6852
C	SRMRB	WLSMV	200	ALL	0.723	0.651	0.722	0.054	0.699	0.643	8273	20741
C	SRMRB	WLSMV	200	5	0.661	0.616	0.657	0.061	0.727	0.548	2546	6545
C	SRMRB	WLSMV	200	10	0.736	0.673	0.745	0.052	0.795	0.577	2807	7036
C	SRMRB	WLSMV	200	30	0.814	0.707	0.838	0.048	0.735	0.698	2920	7160

Detecting Misspecification at Level-1

General overview over all conditions

j <- 2 ## Which class?
for(index in INDEX){
    ## Print out which iteration so we know what we am looking at
    cat('\n\nROC Analysis in')
    cat('\nIndex:\t', index)
    cat('\nClassification:\t', CLASS[j])
    ## Set up iteration key
    key <- paste0(index,'.',CLASS[j])
    ## Create formula
    model <- as.formula(paste0(CLASS[j], '~', index))
    ## Fit ROC curve
    fit_roc[[key]] <-  roc(model, data=sim_results, quiet=T,
                           plot =TRUE, ci=TRUE, print.auc=TRUE)
    ## Create a plot of "smoothed" curve for plotting
    fit_roc_smooth[[key]] <-  smooth(roc(model, data=sim_results))
    ## Compute partial AUC for specificity .8-1
    p.auc <- auc(fit_roc[[key]], partial.auc = c(1,.8),
                 partial.auc.focus = 'sp', partial.auc.correct = T)
    ## get summary info
    roc_summary_gen[ig, 2] <- index
    roc_summary_gen[ig, 1] <- CLASS[j]
    roc_summary_gen[ig, 3] <- fit_roc[[key]]$auc ## total AUC
    roc_summary_gen[ig, 4] <- p.auc ## corrected partial AUC (.5 is no discrimination)
    roc_summary_gen[ig, 5] <- fit_roc_smooth[[key]]$auc ## smoothed AUC
    roc_summary_gen[ig, 6:8] <- coords(fit_roc[[key]], "best", 
                                   ret=c("threshold", "specificity", 'sensitivity'),
                                   transpose=TRUE)
    ## print summary
    cat('\n\nSummary of ROC:\n')
    print(roc_summary_gen[ig, ])
    ## add to summary iterator
    ig <- ig + 1
} ## End loop round index



ROC Analysis in
Index:   CFI
Classification:  CvM1

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
6           CvM1   CFI 0.8942411   0.7896568    0.9008448
  Optimal-Threshold Specificity Sensitivity
6         0.9707385   0.8682327    0.883183


ROC Analysis in
Index:   TLI
Classification:  CvM1

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
7           CvM1   TLI 0.8942411   0.7896568    0.9008143
  Optimal-Threshold Specificity Sensitivity
7         0.9648862   0.8682327    0.883183


ROC Analysis in
Index:   RMSEA
Classification:  CvM1

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
8           CvM1 RMSEA 0.8776767   0.7534801    0.8844573
  Optimal-Threshold Specificity Sensitivity
8         0.0169548   0.8114027   0.8589859


ROC Analysis in
Index:   SRMRW
Classification:  CvM1

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
  Classification Index       AUC partial-AUC Smoothed-AUC
9           CvM1 SRMRW 0.8725866    0.869644    0.8879885
  Optimal-Threshold Specificity Sensitivity
9        0.03824319   0.9745978   0.7240689


ROC Analysis in
Index:   SRMRB
Classification:  CvM1

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
   Classification Index       AUC partial-AUC Smoothed-AUC
10           CvM1 SRMRB 0.5373652   0.5405211    0.5473238
   Optimal-Threshold Specificity Sensitivity
10        0.05688385   0.8393742   0.2433847

kable(roc_summary_gen[1:5,], format = 'html', digits=3) %>%
  kable_styling(full_width = T)

Classification	Index	AUC	partial-AUC	Smoothed-AUC	Optimal-Threshold	Specificity	Sensitivity
C	CFI	0.816	0.637	0.846	0.977	0.702	0.855
C	TLI	0.815	0.637	0.845	0.972	0.702	0.855
C	RMSEA	0.803	0.635	0.830	0.015	0.685	0.829
C	SRMRW	0.742	0.608	0.717	0.038	0.728	0.723
C	SRMRB	0.598	0.572	0.604	0.067	0.804	0.352

print(xtable(roc_summary_gen[1:5,c(2:3,6:8)], digits = 3), booktabs=T,include.rownames = F)

% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Fri Oct 18 17:39:48 2019
\begin{table}[ht]
\centering
\begin{tabular}{lrrrr}
  \toprule
Index & AUC & Optimal-Threshold & Specificity & Sensitivity \\ 
  \midrule
CFI & 0.816 & 0.977 & 0.702 & 0.855 \\ 
  TLI & 0.815 & 0.972 & 0.702 & 0.855 \\ 
  RMSEA & 0.803 & 0.015 & 0.685 & 0.829 \\ 
  SRMRW & 0.742 & 0.038 & 0.728 & 0.723 \\ 
  SRMRB & 0.598 & 0.067 & 0.804 & 0.352 \\ 
   \bottomrule
\end{tabular}
\end{table}

More fine grained information within/across conditions

i <- 401
j <- 2 ## Which class?
for(index in INDEX){
  for(est in EST){
    for(s2 in SS_L2){
      for(s1 in SS_L1){
    ## Print out which iteration so we know what we are looking at
    #cat('\n\nROC Analysis in')
    #cat('\nIndex:\t', index)
    #cat('\nClassification:\t', CLASS[j])
    #cat('\nEstimation Method:\t', est)
    #cat('\nLevel-2 Sample Size:\t', s2)
    #cat('\nLevel-1 Sample Size:\t', s1)
    ## Set up iteration key
    key <- paste0(index,'.',CLASS[j],'.',est,'.', s2,'.',s1)
    # Subset data as  needed
    if(est == 'ALL' & s2 == 'ALL' & s1 == 'ALL') mydata <- sim_results
    if(est != 'ALL' & s2 == 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, Estimator == est)
    }
    if(est == 'ALL' & s2 != 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, ss_l2 == s2)
    }
    if(est == 'ALL' & s2 == 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, ss_l1 == s1)
    }
    if(est != 'ALL' & s2 != 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l2 == s2)
    }
    if(est != 'ALL' & s2 == 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l1 == s1)
    }
    if(est == 'ALL' & s2 != 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, ss_l2 == s2, ss_l1 == s1)
    }
    if(est != 'ALL' & s2 != 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l2 == s2, ss_l1 == s1)
    }
    ## Create formula
    model <- as.formula(paste0(CLASS[j], '~', index))
    ## Fit ROC curve
    fit_roc[[key]] <-  roc(model, data=mydata,quiet=T,
                           plot =F, ci=TRUE, print.auc=TRUE)
    ## Create a plot of "smoothed" curve for plotting
    fit_roc_smooth[[key]] <-  tryCatch(smooth(roc(model, data=mydata)),
                                       error = function(e) NA)
                                      
    ## Compute partial AUC for specificity .8-1
    p.auc <- auc(fit_roc[[key]], partial.auc = c(1,.8),
                 partial.auc.focus = 'sp', partial.auc.correct = T)
    ## get summary info
    roc_summary[i, 2] <- index
    roc_summary[i, 1] <- CLASS[j]
    roc_summary[i, 3] <- est ##estimator
    roc_summary[i, 4] <- s2 ## level-2 sample size
    roc_summary[i, 5] <- s1 ## level-1 sample size
    roc_summary[i, 6] <- fit_roc[[key]]$auc ## total AUC
    roc_summary[i, 7] <- p.auc ## corrected partial AUC (.5 is no discrimination)
    if(is.na(fit_roc_smooth[[key]]) == T){
      roc_summary[i, 8] <- NA
    } else {
      roc_summary[i, 8] <- fit_roc_smooth[[key]]$auc ## smoothed AUC
    }
    roc_summary[i, 9:11] <- coords(fit_roc[[key]], "best", 
                                   ret=c("threshold", "specificity", 'sensitivity'),
                                   transpose=TRUE)
    
    ## add number of C and number of miss models in analysis
    n.C <- nrow(mydata[ mydata[, CLASS[j]] == 1, ])
    n.M <- nrow(mydata[ mydata[, CLASS[j]] == 0, ])
    roc_summary[i, 12] <- n.C
    roc_summary[i, 13] <- n.M
    
    ## print summary
    #cat('\n\nSummary of ROC:\n')
    #print(roc_summary[i, ])
    ## add to summary iterator
    i <- i + 1
      } ## end loop around ss l1
    } ## End loop around ss l2
  } ## End loop around estimator
} ## End loop round index
kable(roc_summary[401:800, ], format = 'html', digits=3) %>%
  kable_styling(full_width = T)

	Classification	Index	Estimator	Level-2 SS	Level-1 SS	AUC	partial-AUC	Smoothed-AUC	Threshold	Specificity	Sensitivity	Num-C	Num-Mis
401	CvM1	CFI	ALL	ALL	ALL	0.894	0.790	0.901	0.971	0.868	0.883	241960	223868
402	CvM1	CFI	ALL	ALL	5	0.846	0.769	0.846	0.968	0.868	0.766	68483	63082
403	CvM1	CFI	ALL	ALL	10	0.927	0.855	0.922	0.968	0.902	0.889	81868	75749
404	CvM1	CFI	ALL	ALL	30	0.908	0.745	0.912	0.976	0.836	0.976	91609	85037
405	CvM1	CFI	ALL	30	ALL	0.739	0.572	0.770	0.963	0.692	0.761	47053	43767
406	CvM1	CFI	ALL	30	5	0.661	0.561	0.666	0.921	0.619	0.633	11584	10825
407	CvM1	CFI	ALL	30	10	0.773	0.612	0.787	0.952	0.675	0.769	15551	14595
408	CvM1	CFI	ALL	30	30	0.783	0.558	0.742	0.965	0.627	0.969	19918	18347
409	CvM1	CFI	ALL	50	ALL	0.851	0.702	0.867	0.970	0.808	0.826	56136	52168
410	CvM1	CFI	ALL	50	5	0.770	0.636	0.775	0.961	0.763	0.654	14818	13894
411	CvM1	CFI	ALL	50	10	0.905	0.795	0.903	0.959	0.817	0.887	18954	17559
412	CvM1	CFI	ALL	50	30	0.878	0.663	0.874	0.980	0.784	0.981	22364	20715
413	CvM1	CFI	ALL	100	ALL	0.950	0.878	0.950	0.971	0.909	0.933	65882	60844
414	CvM1	CFI	ALL	100	5	0.926	0.842	0.923	0.967	0.867	0.847	18983	17448
415	CvM1	CFI	ALL	100	10	0.982	0.961	0.973	0.971	0.956	0.959	22711	20843
416	CvM1	CFI	ALL	100	30	0.938	0.827	0.930	0.982	0.894	0.995	24188	22553
417	CvM1	CFI	ALL	200	ALL	0.997	0.993	0.994	0.975	0.982	0.979	72889	67089
418	CvM1	CFI	ALL	200	5	0.993	0.985	0.989	0.971	0.970	0.967	23098	20915
419	CvM1	CFI	ALL	200	10	0.999	0.998	0.999	0.970	0.992	0.990	24652	22752
420	CvM1	CFI	ALL	200	30	0.998	0.995	0.998	0.987	0.991	0.992	25139	23422
421	CvM1	CFI	MLR	ALL	ALL	0.932	0.922	0.919	0.956	0.948	0.873	89822	85647
422	CvM1	CFI	MLR	ALL	5	0.832	0.799	0.832	0.963	0.907	0.704	26722	24572
423	CvM1	CFI	MLR	ALL	10	0.947	0.929	0.940	0.956	0.955	0.866	30195	28785
424	CvM1	CFI	MLR	ALL	30	1.000	0.999	1.000	0.956	0.995	0.993	32905	32290
425	CvM1	CFI	MLR	30	ALL	0.829	0.798	0.822	0.944	0.918	0.684	18778	17854
426	CvM1	CFI	MLR	30	5	0.699	0.597	0.698	0.889	0.722	0.582	5086	4739
427	CvM1	CFI	MLR	30	10	0.860	0.751	0.858	0.919	0.751	0.811	6138	5832
428	CvM1	CFI	MLR	30	30	0.998	0.995	0.998	0.949	0.973	0.987	7554	7283
429	CvM1	CFI	MLR	50	ALL	0.926	0.882	0.914	0.955	0.921	0.842	21335	20417
430	CvM1	CFI	MLR	50	5	0.795	0.669	0.795	0.929	0.681	0.762	6083	5724
431	CvM1	CFI	MLR	50	10	0.961	0.910	0.959	0.953	0.907	0.886	7170	6790
432	CvM1	CFI	MLR	50	30	1.000	1.000	1.000	0.966	0.998	1.000	8082	7903
433	CvM1	CFI	MLR	100	ALL	0.990	0.977	0.984	0.962	0.959	0.960	24013	22901
434	CvM1	CFI	MLR	100	5	0.946	0.877	0.946	0.959	0.847	0.903	7258	6637
435	CvM1	CFI	MLR	100	10	0.999	0.998	0.999	0.966	0.988	0.991	8237	7823
436	CvM1	CFI	MLR	100	30	1.000	1.000	NA	0.967	1.000	1.000	8518	8441
437	CvM1	CFI	MLR	200	ALL	1.000	0.999	1.000	0.969	0.994	0.995	25696	24475
438	CvM1	CFI	MLR	200	5	0.998	0.995	0.998	0.971	0.985	0.981	8295	7472
439	CvM1	CFI	MLR	200	10	1.000	1.000	NA	0.968	1.000	1.000	8650	8340
440	CvM1	CFI	MLR	200	30	1.000	1.000	NA	0.969	1.000	1.000	8751	8663
441	CvM1	CFI	ULSMV	ALL	ALL	0.822	0.620	0.871	0.977	0.750	0.874	77713	71584
442	CvM1	CFI	ULSMV	ALL	5	0.819	0.697	0.835	0.967	0.789	0.793	21448	19987
443	CvM1	CFI	ULSMV	ALL	10	0.873	0.723	0.892	0.970	0.810	0.888	26416	24300
444	CvM1	CFI	ULSMV	ALL	30	0.792	0.572	0.793	0.987	0.643	0.975	29849	27297
445	CvM1	CFI	ULSMV	30	ALL	0.649	0.529	0.751	0.979	0.482	0.830	14727	13635
446	CvM1	CFI	ULSMV	30	5	0.627	0.536	0.649	0.967	0.606	0.622	3399	3200
447	CvM1	CFI	ULSMV	30	10	0.691	0.543	0.760	0.978	0.559	0.795	4926	4632
448	CvM1	CFI	ULSMV	30	30	0.631	0.519	0.515	0.987	0.337	0.976	6402	5803
449	CvM1	CFI	ULSMV	50	ALL	0.735	0.562	0.799	0.972	0.613	0.834	17918	16505
450	CvM1	CFI	ULSMV	50	5	0.713	0.586	0.727	0.962	0.667	0.670	4573	4319
451	CvM1	CFI	ULSMV	50	10	0.813	0.644	0.837	0.971	0.748	0.803	6038	5551
452	CvM1	CFI	ULSMV	50	30	0.701	0.535	0.674	0.988	0.482	0.977	7307	6635
453	CvM1	CFI	ULSMV	100	ALL	0.875	0.711	0.901	0.971	0.781	0.905	21277	19571
454	CvM1	CFI	ULSMV	100	5	0.880	0.768	0.884	0.967	0.809	0.813	5969	5570
455	CvM1	CFI	ULSMV	100	10	0.946	0.885	0.943	0.970	0.885	0.908	7390	6741
456	CvM1	CFI	ULSMV	100	30	0.823	0.596	0.789	0.984	0.674	0.982	7918	7260
457	CvM1	CFI	ULSMV	200	ALL	0.989	0.973	0.986	0.975	0.954	0.956	23791	21873
458	CvM1	CFI	ULSMV	200	5	0.983	0.961	0.979	0.968	0.934	0.944	7507	6898
459	CvM1	CFI	ULSMV	200	10	0.996	0.991	0.996	0.970	0.977	0.971	8062	7376
460	CvM1	CFI	ULSMV	200	30	0.993	0.981	0.993	0.987	0.972	0.975	8222	7599
461	CvM1	CFI	WLSMV	ALL	ALL	0.945	0.877	0.947	0.977	0.906	0.923	74425	66637
462	CvM1	CFI	WLSMV	ALL	5	0.910	0.831	0.906	0.970	0.882	0.850	20313	18523
463	CvM1	CFI	WLSMV	ALL	10	0.977	0.943	0.970	0.973	0.934	0.953	25257	22664
464	CvM1	CFI	WLSMV	ALL	30	0.950	0.862	0.939	0.988	0.898	0.992	28855	25450
465	CvM1	CFI	WLSMV	30	ALL	0.804	0.632	0.844	0.983	0.732	0.811	13548	12278
466	CvM1	CFI	WLSMV	30	5	0.734	0.600	0.738	0.930	0.513	0.839	3099	2886
467	CvM1	CFI	WLSMV	30	10	0.886	0.765	0.894	0.981	0.835	0.804	4487	4131
468	CvM1	CFI	WLSMV	30	30	0.801	0.584	0.723	0.986	0.612	0.979	5962	5261
469	CvM1	CFI	WLSMV	50	ALL	0.926	0.851	0.930	0.980	0.884	0.849	16883	15246
470	CvM1	CFI	WLSMV	50	5	0.836	0.698	0.840	0.964	0.767	0.769	4162	3851
471	CvM1	CFI	WLSMV	50	10	0.972	0.930	0.971	0.972	0.914	0.916	5746	5218
472	CvM1	CFI	WLSMV	50	30	0.972	0.922	0.969	0.991	0.920	0.977	6975	6177
473	CvM1	CFI	WLSMV	100	ALL	0.992	0.981	0.988	0.972	0.965	0.965	20592	18372
474	CvM1	CFI	WLSMV	100	5	0.960	0.905	0.960	0.969	0.890	0.904	5756	5241
475	CvM1	CFI	WLSMV	100	10	0.999	0.998	0.999	0.973	0.987	0.994	7084	6279
476	CvM1	CFI	WLSMV	100	30	1.000	1.000	NA	0.984	1.000	1.000	7752	6852
477	CvM1	CFI	WLSMV	200	ALL	1.000	1.000	1.000	0.971	0.994	0.996	23402	20741
478	CvM1	CFI	WLSMV	200	5	0.999	0.997	0.999	0.971	0.983	0.988	7296	6545
479	CvM1	CFI	WLSMV	200	10	1.000	1.000	NA	0.977	1.000	1.000	7940	7036
480	CvM1	CFI	WLSMV	200	30	1.000	1.000	NA	0.979	1.000	1.000	8166	7160
481	CvM1	TLI	ALL	ALL	ALL	0.894	0.790	0.901	0.965	0.868	0.883	241960	223868
482	CvM1	TLI	ALL	ALL	5	0.846	0.769	0.846	0.962	0.868	0.766	68483	63082
483	CvM1	TLI	ALL	ALL	10	0.927	0.855	0.922	0.961	0.902	0.889	81868	75749
484	CvM1	TLI	ALL	ALL	30	0.908	0.745	0.912	0.972	0.836	0.976	91609	85037
485	CvM1	TLI	ALL	30	ALL	0.739	0.572	0.770	0.955	0.692	0.761	47053	43767
486	CvM1	TLI	ALL	30	5	0.661	0.561	0.666	0.906	0.619	0.633	11584	10825
487	CvM1	TLI	ALL	30	10	0.773	0.612	0.787	0.942	0.675	0.769	15551	14595
488	CvM1	TLI	ALL	30	30	0.783	0.558	0.742	0.958	0.627	0.969	19918	18347
489	CvM1	TLI	ALL	50	ALL	0.851	0.702	0.867	0.964	0.808	0.826	56136	52168
490	CvM1	TLI	ALL	50	5	0.770	0.636	0.775	0.953	0.763	0.654	14818	13894
491	CvM1	TLI	ALL	50	10	0.905	0.795	0.903	0.951	0.817	0.887	18954	17559
492	CvM1	TLI	ALL	50	30	0.878	0.663	0.874	0.976	0.784	0.981	22364	20715
493	CvM1	TLI	ALL	100	ALL	0.950	0.878	0.950	0.965	0.909	0.933	65882	60844
494	CvM1	TLI	ALL	100	5	0.926	0.842	0.923	0.960	0.867	0.847	18983	17448
495	CvM1	TLI	ALL	100	10	0.982	0.961	0.973	0.965	0.956	0.959	22711	20843
496	CvM1	TLI	ALL	100	30	0.938	0.827	0.930	0.978	0.894	0.995	24188	22553
497	CvM1	TLI	ALL	200	ALL	0.997	0.993	0.994	0.970	0.982	0.979	72889	67089
498	CvM1	TLI	ALL	200	5	0.993	0.985	0.989	0.965	0.970	0.967	23098	20915
499	CvM1	TLI	ALL	200	10	0.999	0.998	0.999	0.964	0.992	0.990	24652	22752
500	CvM1	TLI	ALL	200	30	0.998	0.995	0.998	0.985	0.991	0.992	25139	23422
501	CvM1	TLI	MLR	ALL	ALL	0.932	0.922	0.919	0.948	0.948	0.873	89822	85647
502	CvM1	TLI	MLR	ALL	5	0.832	0.799	0.832	0.955	0.907	0.704	26722	24572
503	CvM1	TLI	MLR	ALL	10	0.947	0.929	0.940	0.947	0.955	0.866	30195	28785
504	CvM1	TLI	MLR	ALL	30	1.000	0.999	1.000	0.947	0.995	0.993	32905	32290
505	CvM1	TLI	MLR	30	ALL	0.829	0.798	0.822	0.932	0.918	0.684	18778	17854
506	CvM1	TLI	MLR	30	5	0.699	0.597	0.698	0.867	0.722	0.582	5086	4739
507	CvM1	TLI	MLR	30	10	0.860	0.751	0.858	0.903	0.751	0.811	6138	5832
508	CvM1	TLI	MLR	30	30	0.998	0.995	0.998	0.939	0.973	0.987	7554	7283
509	CvM1	TLI	MLR	50	ALL	0.926	0.882	0.914	0.945	0.921	0.842	21335	20417
510	CvM1	TLI	MLR	50	5	0.795	0.669	0.795	0.915	0.681	0.762	6083	5724
511	CvM1	TLI	MLR	50	10	0.961	0.910	0.959	0.944	0.907	0.886	7170	6790
512	CvM1	TLI	MLR	50	30	1.000	1.000	1.000	0.959	0.998	1.000	8082	7903
513	CvM1	TLI	MLR	100	ALL	0.990	0.977	0.984	0.954	0.959	0.960	24013	22901
514	CvM1	TLI	MLR	100	5	0.946	0.877	0.946	0.951	0.847	0.903	7258	6637
515	CvM1	TLI	MLR	100	10	0.999	0.998	0.999	0.959	0.988	0.991	8237	7823
516	CvM1	TLI	MLR	100	30	1.000	1.000	NA	0.961	1.000	1.000	8518	8441
517	CvM1	TLI	MLR	200	ALL	1.000	0.999	1.000	0.962	0.994	0.995	25696	24475
518	CvM1	TLI	MLR	200	5	0.998	0.995	0.998	0.965	0.985	0.981	8295	7472
519	CvM1	TLI	MLR	200	10	1.000	1.000	NA	0.962	1.000	1.000	8650	8340
520	CvM1	TLI	MLR	200	30	1.000	1.000	NA	0.963	1.000	1.000	8751	8663
521	CvM1	TLI	ULSMV	ALL	ALL	0.822	0.620	0.871	0.973	0.750	0.874	77713	71584
522	CvM1	TLI	ULSMV	ALL	5	0.819	0.697	0.835	0.960	0.789	0.793	21448	19987
523	CvM1	TLI	ULSMV	ALL	10	0.873	0.723	0.892	0.964	0.810	0.888	26416	24300
524	CvM1	TLI	ULSMV	ALL	30	0.792	0.572	0.793	0.985	0.643	0.975	29849	27297
525	CvM1	TLI	ULSMV	30	ALL	0.649	0.529	0.751	0.975	0.482	0.830	14727	13635
526	CvM1	TLI	ULSMV	30	5	0.627	0.536	0.649	0.960	0.606	0.622	3399	3200
527	CvM1	TLI	ULSMV	30	10	0.691	0.543	0.760	0.973	0.559	0.795	4926	4632
528	CvM1	TLI	ULSMV	30	30	0.631	0.519	0.515	0.985	0.337	0.976	6402	5803
529	CvM1	TLI	ULSMV	50	ALL	0.735	0.562	0.799	0.966	0.613	0.834	17918	16505
530	CvM1	TLI	ULSMV	50	5	0.713	0.586	0.727	0.954	0.667	0.670	4573	4319
531	CvM1	TLI	ULSMV	50	10	0.813	0.644	0.837	0.965	0.748	0.803	6038	5551
532	CvM1	TLI	ULSMV	50	30	0.701	0.535	0.674	0.986	0.482	0.977	7307	6635
533	CvM1	TLI	ULSMV	100	ALL	0.875	0.711	0.901	0.965	0.781	0.905	21277	19571
534	CvM1	TLI	ULSMV	100	5	0.880	0.768	0.884	0.960	0.809	0.813	5969	5570
535	CvM1	TLI	ULSMV	100	10	0.946	0.885	0.943	0.964	0.885	0.908	7390	6741
536	CvM1	TLI	ULSMV	100	30	0.823	0.596	0.789	0.980	0.674	0.982	7918	7260
537	CvM1	TLI	ULSMV	200	ALL	0.989	0.973	0.986	0.970	0.954	0.956	23791	21873
538	CvM1	TLI	ULSMV	200	5	0.983	0.961	0.979	0.962	0.934	0.944	7507	6898
539	CvM1	TLI	ULSMV	200	10	0.996	0.991	0.996	0.964	0.977	0.971	8062	7376
540	CvM1	TLI	ULSMV	200	30	0.993	0.981	0.993	0.985	0.972	0.975	8222	7599
541	CvM1	TLI	WLSMV	ALL	ALL	0.945	0.877	0.947	0.972	0.906	0.923	74425	66637
542	CvM1	TLI	WLSMV	ALL	5	0.910	0.831	0.906	0.964	0.882	0.850	20313	18523
543	CvM1	TLI	WLSMV	ALL	10	0.977	0.943	0.970	0.968	0.934	0.953	25257	22664
544	CvM1	TLI	WLSMV	ALL	30	0.950	0.862	0.939	0.985	0.898	0.992	28855	25450
545	CvM1	TLI	WLSMV	30	ALL	0.804	0.632	0.844	0.979	0.732	0.811	13548	12278
546	CvM1	TLI	WLSMV	30	5	0.734	0.600	0.738	0.916	0.513	0.839	3099	2886
547	CvM1	TLI	WLSMV	30	10	0.886	0.765	0.894	0.977	0.835	0.804	4487	4131
548	CvM1	TLI	WLSMV	30	30	0.801	0.584	0.723	0.984	0.612	0.979	5962	5261
549	CvM1	TLI	WLSMV	50	ALL	0.926	0.851	0.930	0.976	0.884	0.849	16883	15246
550	CvM1	TLI	WLSMV	50	5	0.836	0.698	0.840	0.956	0.767	0.769	4162	3851
551	CvM1	TLI	WLSMV	50	10	0.972	0.930	0.971	0.966	0.914	0.916	5746	5218
552	CvM1	TLI	WLSMV	50	30	0.972	0.922	0.969	0.989	0.920	0.977	6975	6177
553	CvM1	TLI	WLSMV	100	ALL	0.992	0.981	0.988	0.966	0.965	0.965	20592	18372
554	CvM1	TLI	WLSMV	100	5	0.960	0.905	0.960	0.962	0.890	0.904	5756	5241
555	CvM1	TLI	WLSMV	100	10	0.999	0.998	0.999	0.967	0.987	0.994	7084	6279
556	CvM1	TLI	WLSMV	100	30	1.000	1.000	NA	0.981	1.000	1.000	7752	6852
557	CvM1	TLI	WLSMV	200	ALL	1.000	1.000	1.000	0.966	0.994	0.996	23402	20741
558	CvM1	TLI	WLSMV	200	5	0.999	0.997	0.999	0.966	0.983	0.988	7296	6545
559	CvM1	TLI	WLSMV	200	10	1.000	1.000	NA	0.972	1.000	1.000	7940	7036
560	CvM1	TLI	WLSMV	200	30	1.000	1.000	NA	0.975	1.000	1.000	8166	7160
561	CvM1	RMSEA	ALL	ALL	ALL	0.878	0.753	0.884	0.017	0.811	0.859	241960	223868
562	CvM1	RMSEA	ALL	ALL	5	0.833	0.749	0.833	0.020	0.824	0.767	68483	63082
563	CvM1	RMSEA	ALL	ALL	10	0.910	0.820	0.907	0.017	0.855	0.861	81868	75749
564	CvM1	RMSEA	ALL	ALL	30	0.891	0.714	0.893	0.014	0.771	0.943	91609	85037
565	CvM1	RMSEA	ALL	30	ALL	0.720	0.572	0.745	0.023	0.632	0.768	47053	43767
566	CvM1	RMSEA	ALL	30	5	0.632	0.559	0.635	0.023	0.703	0.501	11584	10825
567	CvM1	RMSEA	ALL	30	10	0.740	0.605	0.745	0.020	0.695	0.682	15551	14595
568	CvM1	RMSEA	ALL	30	30	0.765	0.558	0.714	0.023	0.571	0.963	19918	18347
569	CvM1	RMSEA	ALL	50	ALL	0.832	0.675	0.845	0.020	0.718	0.839	56136	52168
570	CvM1	RMSEA	ALL	50	5	0.746	0.625	0.749	0.023	0.695	0.681	14818	13894
571	CvM1	RMSEA	ALL	50	10	0.882	0.746	0.883	0.020	0.795	0.828	18954	17559
572	CvM1	RMSEA	ALL	50	30	0.860	0.650	0.835	0.015	0.700	0.959	22364	20715
573	CvM1	RMSEA	ALL	100	ALL	0.936	0.838	0.939	0.017	0.855	0.911	65882	60844
574	CvM1	RMSEA	ALL	100	5	0.913	0.813	0.915	0.021	0.805	0.870	18983	17448
575	CvM1	RMSEA	ALL	100	10	0.972	0.923	0.973	0.017	0.898	0.950	22711	20843
576	CvM1	RMSEA	ALL	100	30	0.928	0.801	0.906	0.012	0.850	0.991	24188	22553
577	CvM1	RMSEA	ALL	200	ALL	0.984	0.955	0.986	0.015	0.927	0.960	72889	67089
578	CvM1	RMSEA	ALL	200	5	0.989	0.969	0.990	0.018	0.938	0.958	23098	20915
579	CvM1	RMSEA	ALL	200	10	0.997	0.992	0.998	0.013	0.970	0.976	24652	22752
580	CvM1	RMSEA	ALL	200	30	0.990	0.973	0.991	0.007	0.930	0.976	25139	23422
581	CvM1	RMSEA	MLR	ALL	ALL	0.918	0.913	0.905	0.025	0.950	0.856	89822	85647
582	CvM1	RMSEA	MLR	ALL	5	0.809	0.788	0.812	0.024	0.895	0.695	26722	24572
583	CvM1	RMSEA	MLR	ALL	10	0.932	0.917	0.928	0.025	0.955	0.843	30195	28785
584	CvM1	RMSEA	MLR	ALL	30	0.999	0.999	0.999	0.025	0.994	0.989	32905	32290
585	CvM1	RMSEA	MLR	30	ALL	0.799	0.779	0.797	0.030	0.903	0.663	18778	17854
586	CvM1	RMSEA	MLR	30	5	0.678	0.585	0.680	0.048	0.652	0.605	5086	4739
587	CvM1	RMSEA	MLR	30	10	0.834	0.723	0.836	0.034	0.799	0.704	6138	5832
588	CvM1	RMSEA	MLR	30	30	0.997	0.991	0.996	0.027	0.967	0.981	7554	7283
589	CvM1	RMSEA	MLR	50	ALL	0.908	0.869	0.897	0.026	0.912	0.823	21335	20417
590	CvM1	RMSEA	MLR	50	5	0.771	0.655	0.772	0.034	0.657	0.752	6083	5724
591	CvM1	RMSEA	MLR	50	10	0.949	0.889	0.949	0.027	0.877	0.877	7170	6790
592	CvM1	RMSEA	MLR	50	30	1.000	1.000	1.000	0.022	0.997	1.000	8082	7903
593	CvM1	RMSEA	MLR	100	ALL	0.988	0.973	0.982	0.024	0.953	0.958	24013	22901
594	CvM1	RMSEA	MLR	100	5	0.937	0.861	0.938	0.024	0.857	0.871	7258	6637
595	CvM1	RMSEA	MLR	100	10	0.999	0.998	0.999	0.022	0.982	0.991	8237	7823
596	CvM1	RMSEA	MLR	100	30	1.000	1.000	NA	0.020	1.000	1.000	8518	8441
597	CvM1	RMSEA	MLR	200	ALL	1.000	0.999	1.000	0.020	0.996	0.993	25696	24475
598	CvM1	RMSEA	MLR	200	5	0.998	0.994	0.998	0.020	0.985	0.979	8295	7472
599	CvM1	RMSEA	MLR	200	10	1.000	1.000	NA	0.020	1.000	1.000	8650	8340
600	CvM1	RMSEA	MLR	200	30	1.000	1.000	NA	0.018	1.000	1.000	8751	8663
601	CvM1	RMSEA	ULSMV	ALL	ALL	0.809	0.619	0.841	0.012	0.677	0.857	77713	71584
602	CvM1	RMSEA	ULSMV	ALL	5	0.827	0.685	0.841	0.015	0.770	0.778	21448	19987
603	CvM1	RMSEA	ULSMV	ALL	10	0.867	0.702	0.878	0.013	0.759	0.870	26416	24300
604	CvM1	RMSEA	ULSMV	ALL	30	0.778	0.572	0.745	0.006	0.587	0.924	29849	27297
605	CvM1	RMSEA	ULSMV	30	ALL	0.651	0.529	0.705	0.010	0.460	0.803	14727	13635
606	CvM1	RMSEA	ULSMV	30	5	0.633	0.536	0.647	0.012	0.629	0.596	3399	3200
607	CvM1	RMSEA	ULSMV	30	10	0.688	0.543	0.693	0.014	0.469	0.838	4926	4632
608	CvM1	RMSEA	ULSMV	30	30	0.375	NA	0.564	-Inf	0.000	1.000	6402	5803
609	CvM1	RMSEA	ULSMV	50	ALL	0.729	0.562	0.755	0.012	0.589	0.800	17918	16505
610	CvM1	RMSEA	ULSMV	50	5	0.712	0.586	0.718	0.016	0.650	0.666	4573	4319
611	CvM1	RMSEA	ULSMV	50	10	0.803	0.635	0.799	0.014	0.668	0.803	6038	5551
612	CvM1	RMSEA	ULSMV	50	30	0.693	0.535	0.584	0.009	0.422	0.957	7307	6635
613	CvM1	RMSEA	ULSMV	100	ALL	0.854	0.687	0.858	0.011	0.765	0.827	21277	19571
614	CvM1	RMSEA	ULSMV	100	5	0.876	0.750	0.878	0.017	0.772	0.826	5969	5570
615	CvM1	RMSEA	ULSMV	100	10	0.933	0.838	0.934	0.014	0.818	0.892	7390	6741
616	CvM1	RMSEA	ULSMV	100	30	0.809	0.596	0.738	0.009	0.594	0.984	7918	7260
617	CvM1	RMSEA	ULSMV	200	ALL	0.958	0.895	0.962	0.013	0.821	0.947	23791	21873
618	CvM1	RMSEA	ULSMV	200	5	0.976	0.939	0.978	0.015	0.926	0.907	7507	6898
619	CvM1	RMSEA	ULSMV	200	10	0.993	0.980	0.994	0.011	0.973	0.935	8062	7376
620	CvM1	RMSEA	ULSMV	200	30	0.976	0.937	0.978	0.006	0.903	0.929	8222	7599
621	CvM1	RMSEA	WLSMV	ALL	ALL	0.940	0.861	0.944	0.017	0.879	0.914	74425	66637
622	CvM1	RMSEA	WLSMV	ALL	5	0.912	0.829	0.909	0.021	0.858	0.870	20313	18523
623	CvM1	RMSEA	WLSMV	ALL	10	0.977	0.940	0.973	0.020	0.921	0.961	25257	22664
624	CvM1	RMSEA	WLSMV	ALL	30	0.947	0.853	0.945	0.011	0.873	0.979	28855	25450
625	CvM1	RMSEA	WLSMV	30	ALL	0.796	0.631	0.831	0.014	0.707	0.794	13548	12278
626	CvM1	RMSEA	WLSMV	30	5	0.732	0.601	0.737	0.021	0.700	0.652	3099	2886
627	CvM1	RMSEA	WLSMV	30	10	0.880	0.757	0.887	0.018	0.754	0.863	4487	4131
628	CvM1	RMSEA	WLSMV	30	30	0.793	0.584	0.728	0.008	0.617	0.904	5962	5261
629	CvM1	RMSEA	WLSMV	50	ALL	0.908	0.817	0.912	0.014	0.850	0.806	16883	15246
630	CvM1	RMSEA	WLSMV	50	5	0.831	0.694	0.835	0.020	0.789	0.724	4162	3851
631	CvM1	RMSEA	WLSMV	50	10	0.970	0.926	0.971	0.020	0.904	0.925	5746	5218
632	CvM1	RMSEA	WLSMV	50	30	0.961	0.894	0.960	0.009	0.878	0.935	6975	6177
633	CvM1	RMSEA	WLSMV	100	ALL	0.988	0.970	0.987	0.017	0.967	0.931	20592	18372
634	CvM1	RMSEA	WLSMV	100	5	0.960	0.903	0.959	0.021	0.880	0.916	5756	5241
635	CvM1	RMSEA	WLSMV	100	10	0.999	0.999	0.999	0.020	0.986	0.994	7084	6279
636	CvM1	RMSEA	WLSMV	100	30	1.000	1.000	1.000	0.012	0.999	0.999	7752	6852
637	CvM1	RMSEA	WLSMV	200	ALL	1.000	1.000	1.000	0.020	0.995	0.996	23402	20741
638	CvM1	RMSEA	WLSMV	200	5	0.999	0.997	0.999	0.020	0.984	0.985	7296	6545
639	CvM1	RMSEA	WLSMV	200	10	1.000	1.000	NA	0.019	1.000	1.000	7940	7036
640	CvM1	RMSEA	WLSMV	200	30	1.000	1.000	NA	0.014	1.000	1.000	8166	7160
641	CvM1	SRMRW	ALL	ALL	ALL	0.873	0.870	0.888	0.038	0.975	0.724	241960	223868
642	CvM1	SRMRW	ALL	ALL	5	0.789	0.771	0.810	0.047	0.920	0.588	68483	63082
643	CvM1	SRMRW	ALL	ALL	10	0.904	0.881	0.912	0.042	0.936	0.770	81868	75749
644	CvM1	SRMRW	ALL	ALL	30	0.978	0.977	0.978	0.035	0.993	0.941	91609	85037
645	CvM1	SRMRW	ALL	30	ALL	0.748	0.726	0.778	0.044	0.921	0.500	47053	43767
646	CvM1	SRMRW	ALL	30	5	0.690	0.621	0.689	0.072	0.803	0.483	11584	10825
647	CvM1	SRMRW	ALL	30	10	0.828	0.757	0.822	0.056	0.802	0.722	15551	14595
648	CvM1	SRMRW	ALL	30	30	0.943	0.938	0.943	0.039	0.967	0.872	19918	18347
649	CvM1	SRMRW	ALL	50	ALL	0.835	0.805	0.853	0.042	0.926	0.639	56136	52168
650	CvM1	SRMRW	ALL	50	5	0.791	0.700	0.792	0.063	0.767	0.671	14818	13894
651	CvM1	SRMRW	ALL	50	10	0.923	0.883	0.916	0.047	0.897	0.834	18954	17559
652	CvM1	SRMRW	ALL	50	30	0.977	0.972	0.978	0.035	0.991	0.927	22364	20715
653	CvM1	SRMRW	ALL	100	ALL	0.948	0.918	0.955	0.039	0.958	0.819	65882	60844
654	CvM1	SRMRW	ALL	100	5	0.932	0.875	0.930	0.050	0.863	0.859	18983	17448
655	CvM1	SRMRW	ALL	100	10	0.988	0.978	0.989	0.038	0.981	0.936	22711	20843
656	CvM1	SRMRW	ALL	100	30	0.997	0.995	0.997	0.035	0.996	0.981	24188	22553
657	CvM1	SRMRW	ALL	200	ALL	0.996	0.991	0.996	0.036	0.991	0.964	72889	67089
658	CvM1	SRMRW	ALL	200	5	0.994	0.985	0.994	0.040	0.978	0.950	23098	20915
659	CvM1	SRMRW	ALL	200	10	1.000	0.999	1.000	0.034	0.997	0.989	24652	22752
660	CvM1	SRMRW	ALL	200	30	1.000	1.000	1.000	0.034	1.000	1.000	25139	23422
661	CvM1	SRMRW	MLR	ALL	ALL	0.887	0.892	0.902	0.036	0.979	0.776	89822	85647
662	CvM1	SRMRW	MLR	ALL	5	0.808	0.784	0.838	0.042	0.935	0.599	26722	24572
663	CvM1	SRMRW	MLR	ALL	10	0.951	0.919	0.960	0.038	0.955	0.829	30195	28785
664	CvM1	SRMRW	MLR	ALL	30	1.000	1.000	1.000	0.031	1.000	0.998	32905	32290
665	CvM1	SRMRW	MLR	30	ALL	0.769	0.755	0.798	0.042	0.907	0.567	18778	17854
666	CvM1	SRMRW	MLR	30	5	0.813	0.681	0.812	0.071	0.660	0.820	5086	4739
667	CvM1	SRMRW	MLR	30	10	0.955	0.893	0.949	0.048	0.903	0.870	6138	5832
668	CvM1	SRMRW	MLR	30	30	1.000	1.000	1.000	0.033	0.996	0.999	7554	7283
669	CvM1	SRMRW	MLR	50	ALL	0.861	0.840	0.889	0.038	0.960	0.692	21335	20417
670	CvM1	SRMRW	MLR	50	5	0.905	0.802	0.907	0.056	0.796	0.855	6083	5724
671	CvM1	SRMRW	MLR	50	10	0.993	0.981	0.992	0.041	0.952	0.965	7170	6790
672	CvM1	SRMRW	MLR	50	30	1.000	1.000	NA	0.029	1.000	1.000	8082	7903
673	CvM1	SRMRW	MLR	100	ALL	0.981	0.961	0.984	0.037	0.967	0.905	24013	22901
674	CvM1	SRMRW	MLR	100	5	0.987	0.966	0.987	0.043	0.939	0.956	7258	6637
675	CvM1	SRMRW	MLR	100	10	1.000	1.000	1.000	0.032	0.999	1.000	8237	7823
676	CvM1	SRMRW	MLR	100	30	1.000	1.000	NA	0.025	1.000	1.000	8518	8441
677	CvM1	SRMRW	MLR	200	ALL	1.000	1.000	1.000	0.033	0.998	0.998	25696	24475
678	CvM1	SRMRW	MLR	200	5	1.000	1.000	1.000	0.035	0.994	0.999	8295	7472
679	CvM1	SRMRW	MLR	200	10	1.000	1.000	NA	0.027	1.000	1.000	8650	8340
680	CvM1	SRMRW	MLR	200	30	1.000	1.000	NA	0.022	1.000	1.000	8751	8663
681	CvM1	SRMRW	ULSMV	ALL	ALL	0.854	0.864	0.871	0.045	0.976	0.723	77713	71584
682	CvM1	SRMRW	ULSMV	ALL	5	0.760	0.760	0.787	0.050	0.951	0.542	21448	19987
683	CvM1	SRMRW	ULSMV	ALL	10	0.873	0.864	0.887	0.047	0.960	0.729	26416	24300
684	CvM1	SRMRW	ULSMV	ALL	30	0.960	0.961	0.962	0.042	0.995	0.904	29849	27297
685	CvM1	SRMRW	ULSMV	30	ALL	0.722	0.706	0.748	0.053	0.922	0.464	14727	13635
686	CvM1	SRMRW	ULSMV	30	5	0.661	0.582	0.659	0.098	0.619	0.619	3399	3200
687	CvM1	SRMRW	ULSMV	30	10	0.797	0.730	0.801	0.069	0.752	0.713	4926	4632
688	CvM1	SRMRW	ULSMV	30	30	0.904	0.887	0.911	0.047	0.963	0.766	6402	5803
689	CvM1	SRMRW	ULSMV	50	ALL	0.817	0.795	0.840	0.048	0.953	0.603	17918	16505
690	CvM1	SRMRW	ULSMV	50	5	0.790	0.688	0.793	0.076	0.730	0.703	4573	4319
691	CvM1	SRMRW	ULSMV	50	10	0.906	0.867	0.913	0.054	0.922	0.770	6038	5551
692	CvM1	SRMRW	ULSMV	50	30	0.968	0.958	0.970	0.044	0.983	0.894	7307	6635
693	CvM1	SRMRW	ULSMV	100	ALL	0.945	0.924	0.954	0.046	0.961	0.839	21277	19571
694	CvM1	SRMRW	ULSMV	100	5	0.931	0.870	0.934	0.057	0.865	0.845	5969	5570
695	CvM1	SRMRW	ULSMV	100	10	0.989	0.978	0.989	0.046	0.972	0.942	7390	6741
696	CvM1	SRMRW	ULSMV	100	30	0.998	0.997	0.998	0.042	0.998	0.984	7918	7260
697	CvM1	SRMRW	ULSMV	200	ALL	0.997	0.995	0.997	0.043	0.992	0.979	23791	21873
698	CvM1	SRMRW	ULSMV	200	5	0.995	0.989	0.996	0.045	0.982	0.954	7507	6898
699	CvM1	SRMRW	ULSMV	200	10	1.000	1.000	1.000	0.041	0.996	0.999	8062	7376
700	CvM1	SRMRW	ULSMV	200	30	1.000	1.000	NA	0.039	1.000	1.000	8222	7599
701	CvM1	SRMRW	WLSMV	ALL	ALL	0.905	0.911	0.915	0.044	0.979	0.815	74425	66637
702	CvM1	SRMRW	WLSMV	ALL	5	0.811	0.802	0.840	0.049	0.954	0.621	20313	18523
703	CvM1	SRMRW	WLSMV	ALL	10	0.944	0.919	0.953	0.045	0.966	0.824	25257	22664
704	CvM1	SRMRW	WLSMV	ALL	30	1.000	1.000	1.000	0.039	0.999	0.997	28855	25450
705	CvM1	SRMRW	WLSMV	30	ALL	0.801	0.786	0.822	0.053	0.896	0.634	13548	12278
706	CvM1	SRMRW	WLSMV	30	5	0.778	0.641	0.775	0.085	0.718	0.714	3099	2886
707	CvM1	SRMRW	WLSMV	30	10	0.949	0.883	0.944	0.061	0.868	0.893	4487	4131
708	CvM1	SRMRW	WLSMV	30	30	1.000	0.999	1.000	0.042	0.993	0.998	5962	5261
709	CvM1	SRMRW	WLSMV	50	ALL	0.877	0.862	0.897	0.047	0.962	0.731	16883	15246
710	CvM1	SRMRW	WLSMV	50	5	0.892	0.787	0.891	0.069	0.797	0.834	4162	3851
711	CvM1	SRMRW	WLSMV	50	10	0.992	0.980	0.992	0.049	0.965	0.954	5746	5218
712	CvM1	SRMRW	WLSMV	50	30	1.000	1.000	NA	0.037	1.000	1.000	6975	6177
713	CvM1	SRMRW	WLSMV	100	ALL	0.982	0.965	0.984	0.045	0.966	0.913	20592	18372
714	CvM1	SRMRW	WLSMV	100	5	0.984	0.957	0.982	0.053	0.905	0.967	5756	5241
715	CvM1	SRMRW	WLSMV	100	10	1.000	1.000	1.000	0.039	0.998	0.998	7084	6279
716	CvM1	SRMRW	WLSMV	100	30	1.000	1.000	NA	0.031	1.000	1.000	7752	6852
717	CvM1	SRMRW	WLSMV	200	ALL	1.000	1.000	1.000	0.040	0.999	0.998	23402	20741
718	CvM1	SRMRW	WLSMV	200	5	1.000	1.000	1.000	0.041	0.996	0.996	7296	6545
719	CvM1	SRMRW	WLSMV	200	10	1.000	1.000	NA	0.034	1.000	1.000	7940	7036
720	CvM1	SRMRW	WLSMV	200	30	1.000	1.000	NA	0.028	1.000	1.000	8166	7160
721	CvM1	SRMRB	ALL	ALL	ALL	0.537	0.541	0.547	0.057	0.839	0.243	241960	223868
722	CvM1	SRMRB	ALL	ALL	5	0.511	0.519	0.516	0.064	0.839	0.205	68483	63082
723	CvM1	SRMRB	ALL	ALL	10	0.536	0.539	0.546	0.057	0.844	0.236	81868	75749
724	CvM1	SRMRB	ALL	ALL	30	0.564	0.559	0.577	0.057	0.792	0.330	91609	85037
725	CvM1	SRMRB	ALL	30	ALL	0.517	0.521	0.522	0.109	0.814	0.232	47053	43767
726	CvM1	SRMRB	ALL	30	5	0.495	NA	0.494	0.109	0.929	0.074	11584	10825
727	CvM1	SRMRB	ALL	30	10	0.511	0.512	0.511	0.127	0.647	0.388	15551	14595
728	CvM1	SRMRB	ALL	30	30	0.537	0.537	0.543	0.103	0.799	0.284	19918	18347
729	CvM1	SRMRB	ALL	50	ALL	0.533	0.534	0.539	0.084	0.839	0.233	56136	52168
730	CvM1	SRMRB	ALL	50	5	0.503	0.504	0.502	0.120	0.535	0.480	14818	13894
731	CvM1	SRMRB	ALL	50	10	0.523	0.524	0.524	0.086	0.838	0.225	18954	17559
732	CvM1	SRMRB	ALL	50	30	0.570	0.560	0.578	0.080	0.815	0.313	22364	20715
733	CvM1	SRMRB	ALL	100	ALL	0.559	0.556	0.568	0.063	0.811	0.302	65882	60844
734	CvM1	SRMRB	ALL	100	5	0.509	0.514	0.509	0.074	0.767	0.270	18983	17448
735	CvM1	SRMRB	ALL	100	10	0.561	0.554	0.565	0.067	0.757	0.365	22711	20843
736	CvM1	SRMRB	ALL	100	30	0.621	0.594	0.634	0.059	0.813	0.375	24188	22553
737	CvM1	SRMRB	ALL	200	ALL	0.601	0.580	0.609	0.049	0.749	0.414	72889	67089
738	CvM1	SRMRB	ALL	200	5	0.540	0.534	0.540	0.061	0.633	0.452	23098	20915
739	CvM1	SRMRB	ALL	200	10	0.611	0.592	0.621	0.047	0.832	0.357	24652	22752
740	CvM1	SRMRB	ALL	200	30	0.682	0.627	0.703	0.043	0.828	0.417	25139	23422
741	CvM1	SRMRB	MLR	ALL	ALL	0.512	0.539	0.526	0.061	0.921	0.157	89822	85647
742	CvM1	SRMRB	MLR	ALL	5	0.502	0.508	0.491	0.147	0.675	0.361	26722	24572
743	CvM1	SRMRB	MLR	ALL	10	0.489	0.510	0.476	0.175	0.902	0.123	30195	28785
744	CvM1	SRMRB	MLR	ALL	30	0.544	0.563	0.562	0.059	0.913	0.211	32905	32290
745	CvM1	SRMRB	MLR	30	ALL	0.518	0.513	0.512	0.177	0.698	0.346	18778	17854
746	CvM1	SRMRB	MLR	30	5	0.520	0.512	0.519	0.208	0.695	0.349	5086	4739
747	CvM1	SRMRB	MLR	30	10	0.515	0.516	0.513	0.202	0.835	0.214	6138	5832
748	CvM1	SRMRB	MLR	30	30	0.504	0.513	0.497	0.164	0.761	0.286	7554	7283
749	CvM1	SRMRB	MLR	50	ALL	0.503	0.516	0.495	0.151	0.805	0.235	21335	20417
750	CvM1	SRMRB	MLR	50	5	0.509	0.515	0.505	0.198	0.864	0.183	6083	5724
751	CvM1	SRMRB	MLR	50	10	0.497	0.520	0.500	0.096	0.913	0.133	7170	6790
752	CvM1	SRMRB	MLR	50	30	0.526	0.542	0.535	0.087	0.890	0.199	8082	7903
753	CvM1	SRMRB	MLR	100	ALL	0.530	0.552	0.540	0.069	0.917	0.186	24013	22901
754	CvM1	SRMRB	MLR	100	5	0.500	0.523	0.504	0.088	0.801	0.254	7258	6637
755	CvM1	SRMRB	MLR	100	10	0.537	0.553	0.543	0.074	0.858	0.265	8237	7823
756	CvM1	SRMRB	MLR	100	30	0.597	0.595	0.614	0.066	0.919	0.271	8518	8441
757	CvM1	SRMRB	MLR	200	ALL	0.600	0.602	0.608	0.057	0.853	0.350	25696	24475
758	CvM1	SRMRB	MLR	200	5	0.540	0.564	0.546	0.067	0.770	0.378	8295	7472
759	CvM1	SRMRB	MLR	200	10	0.606	0.613	0.625	0.057	0.859	0.369	8650	8340
760	CvM1	SRMRB	MLR	200	30	0.715	0.665	0.732	0.054	0.854	0.468	8751	8663
761	CvM1	SRMRB	ULSMV	ALL	ALL	0.542	0.544	0.556	0.042	0.942	0.143	77713	71584
762	CvM1	SRMRB	ULSMV	ALL	5	0.517	0.521	0.524	0.051	0.893	0.154	21448	19987
763	CvM1	SRMRB	ULSMV	ALL	10	0.542	0.544	0.556	0.042	0.950	0.141	26416	24300
764	CvM1	SRMRB	ULSMV	ALL	30	0.562	0.563	0.580	0.038	0.960	0.164	29849	27297
765	CvM1	SRMRB	ULSMV	30	ALL	0.532	0.531	0.541	0.093	0.834	0.233	14727	13635
766	CvM1	SRMRB	ULSMV	30	5	0.508	0.501	0.511	0.172	0.216	0.810	3399	3200
767	CvM1	SRMRB	ULSMV	30	10	0.525	0.517	0.526	0.106	0.681	0.378	4926	4632
768	CvM1	SRMRB	ULSMV	30	30	0.551	0.554	0.559	0.093	0.753	0.359	6402	5803
769	CvM1	SRMRB	ULSMV	50	ALL	0.541	0.546	0.553	0.070	0.875	0.217	17918	16505
770	CvM1	SRMRB	ULSMV	50	5	0.504	0.507	0.505	0.076	0.935	0.085	4573	4319
771	CvM1	SRMRB	ULSMV	50	10	0.532	0.530	0.534	0.086	0.654	0.423	6038	5551
772	CvM1	SRMRB	ULSMV	50	30	0.578	0.583	0.591	0.063	0.897	0.268	7307	6635
773	CvM1	SRMRB	ULSMV	100	ALL	0.565	0.573	0.581	0.052	0.880	0.262	21277	19571
774	CvM1	SRMRB	ULSMV	100	5	0.520	0.522	0.522	0.064	0.798	0.262	5969	5570
775	CvM1	SRMRB	ULSMV	100	10	0.573	0.578	0.582	0.054	0.866	0.307	7390	6741
776	CvM1	SRMRB	ULSMV	100	30	0.612	0.626	0.636	0.047	0.915	0.331	7918	7260
777	CvM1	SRMRB	ULSMV	200	ALL	0.595	0.601	0.614	0.038	0.924	0.272	23791	21873
778	CvM1	SRMRB	ULSMV	200	5	0.549	0.548	0.553	0.046	0.854	0.263	7507	6898
779	CvM1	SRMRB	ULSMV	200	10	0.615	0.627	0.633	0.041	0.870	0.390	8062	7376
780	CvM1	SRMRB	ULSMV	200	30	0.657	0.668	0.688	0.036	0.930	0.395	8222	7599
781	CvM1	SRMRB	WLSMV	ALL	ALL	0.551	0.547	0.563	0.052	0.877	0.216	74425	66637
782	CvM1	SRMRB	WLSMV	ALL	5	0.528	0.526	0.534	0.055	0.908	0.148	20313	18523
783	CvM1	SRMRB	WLSMV	ALL	10	0.552	0.545	0.564	0.049	0.919	0.172	25257	22664
784	CvM1	SRMRB	WLSMV	ALL	30	0.566	0.563	0.581	0.049	0.864	0.258	28855	25450
785	CvM1	SRMRB	WLSMV	30	ALL	0.530	0.526	0.536	0.109	0.842	0.218	13548	12278
786	CvM1	SRMRB	WLSMV	30	5	0.503	NA	0.503	0.143	0.421	0.604	3099	2886
787	CvM1	SRMRB	WLSMV	30	10	0.523	0.513	0.523	0.127	0.588	0.463	4487	4131
788	CvM1	SRMRB	WLSMV	30	30	0.545	0.541	0.551	0.109	0.738	0.356	5962	5261
789	CvM1	SRMRB	WLSMV	50	ALL	0.552	0.545	0.562	0.086	0.827	0.266	16883	15246
790	CvM1	SRMRB	WLSMV	50	5	0.514	0.507	0.514	0.111	0.570	0.462	4162	3851
791	CvM1	SRMRB	WLSMV	50	10	0.538	0.528	0.542	0.096	0.672	0.409	5746	5218
792	CvM1	SRMRB	WLSMV	50	30	0.592	0.577	0.601	0.080	0.842	0.322	6975	6177
793	CvM1	SRMRB	WLSMV	100	ALL	0.582	0.574	0.593	0.060	0.883	0.267	20592	18372
794	CvM1	SRMRB	WLSMV	100	5	0.526	0.522	0.526	0.074	0.759	0.304	5756	5241
795	CvM1	SRMRB	WLSMV	100	10	0.586	0.571	0.589	0.066	0.757	0.407	7084	6279
796	CvM1	SRMRB	WLSMV	100	30	0.641	0.623	0.657	0.058	0.852	0.392	7752	6852
797	CvM1	SRMRB	WLSMV	200	ALL	0.626	0.603	0.637	0.048	0.754	0.455	23402	20741
798	CvM1	SRMRB	WLSMV	200	5	0.570	0.556	0.574	0.052	0.851	0.286	7296	6545
799	CvM1	SRMRB	WLSMV	200	10	0.641	0.618	0.651	0.047	0.814	0.438	7940	7036
800	CvM1	SRMRB	WLSMV	200	30	0.707	0.660	0.725	0.043	0.837	0.479	8166	7160

Detecting Misspecification at Level-2

General overview over all conditions

j <- 3 ## Which class?
for(index in INDEX){
    ## Print out which iteration so we know what we am looking at
    cat('\n\nROC Analysis in')
    cat('\nIndex:\t', index)
    cat('\nClassification:\t', CLASS[j])
    ## Set up iteration key
    key <- paste0(index,'.',CLASS[j])
    ## Create formula
    model <- as.formula(paste0(CLASS[j], '~', index))
    ## Fit ROC curve
    fit_roc[[key]] <-  roc(model, data=sim_results,quiet=T,
                           plot =TRUE, ci=TRUE, print.auc=TRUE)
    ## Create a plot of "smoothed" curve for plotting
    fit_roc_smooth[[key]] <-  smooth(roc(model, data=sim_results))
    ## Compute partial AUC for specificity .8-1
    p.auc <- auc(fit_roc[[key]], partial.auc = c(1,.8),
                 partial.auc.focus = 'sp', partial.auc.correct = T)
    ## get summary info
    roc_summary_gen[ig, 2] <- index
    roc_summary_gen[ig, 1] <- CLASS[j]
    roc_summary_gen[ig, 3] <- fit_roc[[key]]$auc ## total AUC
    roc_summary_gen[ig, 4] <- p.auc ## corrected partial AUC (.5 is no discrimination)
    roc_summary_gen[ig, 5] <- fit_roc_smooth[[key]]$auc ## smoothed AUC
    roc_summary_gen[ig, 6:8] <- coords(fit_roc[[key]], "best", 
                                   ret=c("threshold", "specificity", 'sensitivity'),
                                   transpose=TRUE)
    ## print summary
    cat('\n\nSummary of ROC:\n')
    print(roc_summary_gen[ig, ])
    ## add to summary iterator
    ig <- ig + 1
} ## End loop round index



ROC Analysis in
Index:   CFI
Classification:  CvM2

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
   Classification Index      AUC partial-AUC Smoothed-AUC
11           CvM2   CFI 0.669498   0.5483643    0.6916255
   Optimal-Threshold Specificity Sensitivity
11         0.9932424   0.5757261    0.694697


ROC Analysis in
Index:   TLI
Classification:  CvM2

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
   Classification Index      AUC partial-AUC Smoothed-AUC
12           CvM2   TLI 0.669498   0.5483643    0.6916017
   Optimal-Threshold Specificity Sensitivity
12         0.9918909   0.5757261    0.694697


ROC Analysis in
Index:   RMSEA
Classification:  CvM2

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
   Classification Index       AUC partial-AUC Smoothed-AUC
13           CvM2 RMSEA 0.6644986   0.5483738     0.691537
   Optimal-Threshold Specificity Sensitivity
13       0.008547063    0.587031   0.6791605


ROC Analysis in
Index:   SRMRW
Classification:  CvM2

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
   Classification Index       AUC partial-AUC Smoothed-AUC
14           CvM2 SRMRW 0.5270719   0.5050447    0.5241129
   Optimal-Threshold Specificity Sensitivity
14        0.03089441   0.4519094   0.5970939


ROC Analysis in
Index:   SRMRB
Classification:  CvM2

Version	Author	Date
982c8f1	noah-padgett	2019-05-18



Summary of ROC:
   Classification Index       AUC partial-AUC Smoothed-AUC
15           CvM2 SRMRB 0.6343083   0.6012881     0.641562
   Optimal-Threshold Specificity Sensitivity
15        0.07595733   0.7774833   0.4406392

kable(roc_summary_gen[11:15,], format = 'html', digits=3) %>%
  kable_styling(full_width = T)

	Classification	Index	AUC	partial-AUC	Smoothed-AUC	Optimal-Threshold	Specificity	Sensitivity
11	CvM2	CFI	0.669	0.548	0.692	0.993	0.576	0.695
12	CvM2	TLI	0.669	0.548	0.692	0.992	0.576	0.695
13	CvM2	RMSEA	0.664	0.548	0.692	0.009	0.587	0.679
14	CvM2	SRMRW	0.527	0.505	0.524	0.031	0.452	0.597
15	CvM2	SRMRB	0.634	0.601	0.642	0.076	0.777	0.441

print(xtable(roc_summary_gen[11:15,c(2:3,6:8)], digits = 3), booktabs=T,include.rownames = F)

% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Fri Oct 18 17:43:42 2019
\begin{table}[ht]
\centering
\begin{tabular}{lrrrr}
  \toprule
Index & AUC & Optimal-Threshold & Specificity & Sensitivity \\ 
  \midrule
CFI & 0.669 & 0.993 & 0.576 & 0.695 \\ 
  TLI & 0.669 & 0.992 & 0.576 & 0.695 \\ 
  RMSEA & 0.664 & 0.009 & 0.587 & 0.679 \\ 
  SRMRW & 0.527 & 0.031 & 0.452 & 0.597 \\ 
  SRMRB & 0.634 & 0.076 & 0.777 & 0.441 \\ 
   \bottomrule
\end{tabular}
\end{table}

More fine grained information within/across conditions

j <- 3 ## Which class?
for(index in INDEX){
  for(est in EST){
    for(s2 in SS_L2){
      for(s1 in SS_L1){
    ## Print out which iteration so we know what we are looking at
    #cat('\n\nROC Analysis in')
    #cat('\nIndex:\t', index)
    #cat('\nClassification:\t', CLASS[j])
    #cat('\nEstimation Method:\t', est)
    #cat('\nLevel-2 Sample Size:\t', s2)
    #cat('\nLevel-1 Sample Size:\t', s1)
    ## Set up iteration key
    key <- paste0(index,'.',CLASS[j],'.',est,'.', s2,'.',s1)
    # Subset data as  needed
    if(est == 'ALL' & s2 == 'ALL' & s1 == 'ALL') mydata <- sim_results
    if(est != 'ALL' & s2 == 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, Estimator == est)
    }
    if(est == 'ALL' & s2 != 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, ss_l2 == s2)
    }
    if(est == 'ALL' & s2 == 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, ss_l1 == s1)
    }
    if(est != 'ALL' & s2 != 'ALL' & s1 == 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l2 == s2)
    }
    if(est != 'ALL' & s2 == 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l1 == s1)
    }
    if(est == 'ALL' & s2 != 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, ss_l2 == s2, ss_l1 == s1)
    }
    if(est != 'ALL' & s2 != 'ALL' & s1 != 'ALL'){
      mydata <- filter(sim_results, Estimator == est, ss_l2 == s2, ss_l1 == s1)
    }
    ## Create formula
    model <- as.formula(paste0(CLASS[j], '~', index))
    ## Fit ROC curve
    fit_roc[[key]] <-  roc(model, data=mydata, quiet=T,
                           plot =F, ci=TRUE, print.auc=TRUE)
    ## Create a plot of "smoothed" curve for plotting
    fit_roc_smooth[[key]] <-  smooth(roc(model, data=mydata))
    ## Compute partial AUC for specificity .8-1
    p.auc <- auc(fit_roc[[key]], partial.auc = c(1,.8),
                 partial.auc.focus = 'sp', partial.auc.correct = T)
    ## get summary info
    roc_summary[i, 2] <- index
    roc_summary[i, 1] <- CLASS[j]
    roc_summary[i, 3] <- est ##estimator
    roc_summary[i, 4] <- s2 ## level-2 sample size
    roc_summary[i, 5] <- s1 ## level-1 sample size
    roc_summary[i, 6] <- fit_roc[[key]]$auc ## total AUC
    roc_summary[i, 7] <- p.auc ## corrected partial AUC (.5 is no discrimination)
    roc_summary[i, 8] <- fit_roc_smooth[[key]]$auc ## smoothed AUC
    roc_summary[i, 9:11] <- coords(fit_roc[[key]], "best", 
                                   ret=c("threshold", "specificity", 'sensitivity'),
                                   transpose=TRUE)
    
    ## add number of C and number of miss models in analysis
    n.C <- nrow(mydata[ mydata[, CLASS[j]] == 1, ])
    n.M <- nrow(mydata[ mydata[, CLASS[j]] == 0, ])
    roc_summary[i, 12] <- n.C
    roc_summary[i, 13] <- n.M
    
    ## print summary
    #cat('\n\nSummary of ROC:\n')
    #print(roc_summary[i, ])
    ## add to summary iterator
    i <- i + 1
      } ## end loop around ss l1
    } ## End loop around ss l2
  } ## End loop around estimator
} ## End loop round index
kable(roc_summary[801:1200, ], format = 'html', digits=3) %>%
  kable_styling(full_width = T)

	Classification	Index	Estimator	Level-2 SS	Level-1 SS	AUC	partial-AUC	Smoothed-AUC	Threshold	Specificity	Sensitivity	Num-C	Num-Mis
801	CvM2	CFI	ALL	ALL	ALL	0.669	0.548	0.692	0.993	0.576	0.695	223583	223868
802	CvM2	CFI	ALL	ALL	5	0.654	0.549	0.672	0.981	0.593	0.675	62891	63082
803	CvM2	CFI	ALL	ALL	10	0.687	0.556	0.699	0.990	0.588	0.714	75570	75749
804	CvM2	CFI	ALL	ALL	30	0.686	0.543	0.682	0.998	0.556	0.752	85122	85037
805	CvM2	CFI	ALL	30	ALL	0.557	0.512	0.570	0.982	0.455	0.644	43725	43767
806	CvM2	CFI	ALL	30	5	0.556	0.516	0.559	0.971	0.670	0.420	10875	10825
807	CvM2	CFI	ALL	30	10	0.569	0.515	0.572	0.990	0.589	0.516	14530	14595
808	CvM2	CFI	ALL	30	30	0.565	0.510	0.535	0.986	0.292	0.818	18320	18347
809	CvM2	CFI	ALL	50	ALL	0.620	0.530	0.631	0.990	0.538	0.655	52104	52168
810	CvM2	CFI	ALL	50	5	0.595	0.527	0.600	0.971	0.566	0.586	13813	13894
811	CvM2	CFI	ALL	50	10	0.649	0.545	0.652	0.980	0.515	0.704	17513	17559
812	CvM2	CFI	ALL	50	30	0.637	0.525	0.599	0.995	0.431	0.788	20778	20715
813	CvM2	CFI	ALL	100	ALL	0.707	0.563	0.706	0.994	0.599	0.720	60747	60844
814	CvM2	CFI	ALL	100	5	0.695	0.561	0.701	0.977	0.548	0.757	17384	17448
815	CvM2	CFI	ALL	100	10	0.731	0.572	0.731	0.986	0.530	0.835	20799	20843
816	CvM2	CFI	ALL	100	30	0.731	0.559	0.718	0.997	0.543	0.829	22564	22553
817	CvM2	CFI	ALL	200	ALL	0.771	0.605	0.762	0.995	0.619	0.788	67007	67089
818	CvM2	CFI	ALL	200	5	0.757	0.577	0.756	0.985	0.567	0.859	20819	20915
819	CvM2	CFI	ALL	200	10	0.778	0.600	0.770	0.990	0.535	0.911	22728	22752
820	CvM2	CFI	ALL	200	30	0.816	0.637	0.811	0.998	0.694	0.817	23460	23422
821	CvM2	CFI	MLR	ALL	ALL	0.656	0.567	0.666	0.991	0.652	0.612	85732	85647
822	CvM2	CFI	MLR	ALL	5	0.630	0.557	0.639	0.981	0.669	0.582	24596	24572
823	CvM2	CFI	MLR	ALL	10	0.664	0.563	0.674	0.988	0.662	0.627	28824	28785
824	CvM2	CFI	MLR	ALL	30	0.697	0.579	0.705	0.995	0.642	0.669	32312	32290
825	CvM2	CFI	MLR	30	ALL	0.574	0.533	0.575	0.972	0.638	0.494	17855	17854
826	CvM2	CFI	MLR	30	5	0.563	0.525	0.562	0.896	0.573	0.551	4747	4739
827	CvM2	CFI	MLR	30	10	0.587	0.527	0.586	0.940	0.477	0.657	5832	5832
828	CvM2	CFI	MLR	30	30	0.638	0.543	0.637	0.982	0.576	0.630	7276	7283
829	CvM2	CFI	MLR	50	ALL	0.624	0.544	0.629	0.986	0.623	0.586	20411	20417
830	CvM2	CFI	MLR	50	5	0.598	0.530	0.601	0.971	0.685	0.475	5706	5724
831	CvM2	CFI	MLR	50	10	0.657	0.548	0.658	0.967	0.489	0.753	6787	6790
832	CvM2	CFI	MLR	50	30	0.688	0.551	0.685	0.988	0.477	0.824	7918	7903
833	CvM2	CFI	MLR	100	ALL	0.704	0.568	0.714	0.991	0.595	0.728	22941	22901
834	CvM2	CFI	MLR	100	5	0.691	0.561	0.699	0.977	0.580	0.727	6647	6637
835	CvM2	CFI	MLR	100	10	0.732	0.562	0.729	0.985	0.532	0.854	7855	7823
836	CvM2	CFI	MLR	100	30	0.757	0.581	0.748	0.995	0.551	0.857	8439	8441
837	CvM2	CFI	MLR	200	ALL	0.764	0.594	0.767	0.995	0.626	0.789	24525	24475
838	CvM2	CFI	MLR	200	5	0.761	0.576	0.759	0.986	0.592	0.845	7496	7472
839	CvM2	CFI	MLR	200	10	0.778	0.587	0.763	0.991	0.555	0.903	8350	8340
840	CvM2	CFI	MLR	200	30	0.815	0.621	0.804	0.997	0.620	0.898	8679	8663
841	CvM2	CFI	ULSMV	ALL	ALL	0.691	0.546	0.719	0.978	0.435	0.871	71635	71584
842	CvM2	CFI	ULSMV	ALL	5	0.677	0.547	0.706	0.971	0.535	0.767	19976	19987
843	CvM2	CFI	ULSMV	ALL	10	0.711	0.555	0.722	0.970	0.455	0.888	24264	24300
844	CvM2	CFI	ULSMV	ALL	30	0.696	0.540	0.596	0.999	0.522	0.812	27395	27297
845	CvM2	CFI	ULSMV	30	ALL	0.570	0.512	0.622	0.999	0.409	0.720	13710	13635
846	CvM2	CFI	ULSMV	30	5	0.565	0.514	0.584	0.972	0.521	0.604	3256	3200
847	CvM2	CFI	ULSMV	30	10	0.589	0.516	0.611	0.999	0.487	0.664	4632	4632
848	CvM2	CFI	ULSMV	30	30	0.571	0.510	0.422	0.999	0.250	0.881	5822	5803
849	CvM2	CFI	ULSMV	50	ALL	0.641	0.531	0.665	0.996	0.545	0.685	16566	16505
850	CvM2	CFI	ULSMV	50	5	0.606	0.527	0.615	0.991	0.644	0.521	4339	4319
851	CvM2	CFI	ULSMV	50	10	0.675	0.548	0.682	0.971	0.466	0.800	5547	5551
852	CvM2	CFI	ULSMV	50	30	0.647	0.525	0.537	0.998	0.399	0.861	6680	6635
853	CvM2	CFI	ULSMV	100	ALL	0.729	0.565	0.712	0.970	0.447	0.906	19541	19571
854	CvM2	CFI	ULSMV	100	5	0.715	0.565	0.718	0.975	0.584	0.752	5546	5570
855	CvM2	CFI	ULSMV	100	10	0.750	0.582	0.737	0.964	0.470	0.932	6712	6741
856	CvM2	CFI	ULSMV	100	30	0.733	0.556	0.658	0.999	0.586	0.806	7283	7260
857	CvM2	CFI	ULSMV	200	ALL	0.784	0.610	0.750	0.978	0.511	0.946	21818	21873
858	CvM2	CFI	ULSMV	200	5	0.763	0.576	0.745	0.975	0.536	0.914	6835	6898
859	CvM2	CFI	ULSMV	200	10	0.780	0.604	0.744	0.968	0.505	0.974	7373	7376
860	CvM2	CFI	ULSMV	200	30	0.822	0.651	0.781	0.999	0.766	0.742	7610	7599
861	CvM2	CFI	WLSMV	ALL	ALL	0.665	0.542	0.700	0.997	0.563	0.707	66216	66637
862	CvM2	CFI	WLSMV	ALL	5	0.663	0.547	0.683	0.987	0.585	0.692	18319	18523
863	CvM2	CFI	WLSMV	ALL	10	0.694	0.553	0.710	0.992	0.535	0.781	22482	22664
864	CvM2	CFI	WLSMV	ALL	30	0.675	0.534	0.658	0.998	0.458	0.847	25415	25450
865	CvM2	CFI	WLSMV	30	ALL	0.541	0.507	0.559	1.000	0.441	0.635	12160	12278
866	CvM2	CFI	WLSMV	30	5	0.553	0.514	0.557	0.968	0.475	0.615	2872	2886
867	CvM2	CFI	WLSMV	30	10	0.561	0.512	0.577	0.991	0.424	0.688	4066	4131
868	CvM2	CFI	WLSMV	30	30	0.531	0.504	0.535	1.000	0.235	0.825	5222	5261
869	CvM2	CFI	WLSMV	50	ALL	0.601	0.523	0.622	0.999	0.590	0.586	15127	15246
870	CvM2	CFI	WLSMV	50	5	0.590	0.523	0.594	0.970	0.434	0.708	3768	3851
871	CvM2	CFI	WLSMV	50	10	0.635	0.539	0.639	0.993	0.613	0.592	5179	5218
872	CvM2	CFI	WLSMV	50	30	0.607	0.517	0.556	0.999	0.393	0.797	6180	6177
873	CvM2	CFI	WLSMV	100	ALL	0.699	0.559	0.712	0.996	0.603	0.708	18265	18372
874	CvM2	CFI	WLSMV	100	5	0.689	0.558	0.694	0.985	0.583	0.711	5191	5241
875	CvM2	CFI	WLSMV	100	10	0.733	0.573	0.722	0.990	0.535	0.836	6232	6279
876	CvM2	CFI	WLSMV	100	30	0.723	0.552	0.691	0.998	0.492	0.881	6842	6852
877	CvM2	CFI	WLSMV	200	ALL	0.776	0.611	0.767	0.997	0.644	0.771	20664	20741
878	CvM2	CFI	WLSMV	200	5	0.760	0.578	0.749	0.986	0.547	0.883	6488	6545
879	CvM2	CFI	WLSMV	200	10	0.794	0.611	0.776	0.993	0.581	0.890	7005	7036
880	CvM2	CFI	WLSMV	200	30	0.827	0.638	0.812	0.999	0.663	0.873	7171	7160
881	CvM2	TLI	ALL	ALL	ALL	0.669	0.548	0.692	0.992	0.576	0.695	223583	223868
882	CvM2	TLI	ALL	ALL	5	0.654	0.549	0.672	0.977	0.593	0.675	62891	63082
883	CvM2	TLI	ALL	ALL	10	0.687	0.556	0.699	0.988	0.588	0.714	75570	75749
884	CvM2	TLI	ALL	ALL	30	0.686	0.543	0.682	0.997	0.556	0.752	85122	85037
885	CvM2	TLI	ALL	30	ALL	0.557	0.512	0.570	0.979	0.455	0.644	43725	43767
886	CvM2	TLI	ALL	30	5	0.556	0.516	0.559	0.966	0.670	0.420	10875	10825
887	CvM2	TLI	ALL	30	10	0.569	0.515	0.572	0.988	0.589	0.516	14530	14595
888	CvM2	TLI	ALL	30	30	0.565	0.510	0.535	0.983	0.292	0.818	18320	18347
889	CvM2	TLI	ALL	50	ALL	0.620	0.530	0.631	0.988	0.538	0.655	52104	52168
890	CvM2	TLI	ALL	50	5	0.595	0.527	0.600	0.965	0.566	0.586	13813	13894
891	CvM2	TLI	ALL	50	10	0.649	0.545	0.652	0.976	0.515	0.704	17513	17559
892	CvM2	TLI	ALL	50	30	0.637	0.525	0.599	0.994	0.431	0.788	20778	20715
893	CvM2	TLI	ALL	100	ALL	0.707	0.563	0.706	0.992	0.599	0.720	60747	60844
894	CvM2	TLI	ALL	100	5	0.695	0.561	0.701	0.972	0.548	0.757	17384	17448
895	CvM2	TLI	ALL	100	10	0.731	0.572	0.731	0.983	0.530	0.835	20799	20843
896	CvM2	TLI	ALL	100	30	0.731	0.559	0.718	0.996	0.543	0.829	22564	22553
897	CvM2	TLI	ALL	200	ALL	0.771	0.605	0.762	0.994	0.619	0.788	67007	67089
898	CvM2	TLI	ALL	200	5	0.757	0.577	0.756	0.982	0.567	0.859	20819	20915
899	CvM2	TLI	ALL	200	10	0.778	0.600	0.770	0.988	0.535	0.911	22728	22752
900	CvM2	TLI	ALL	200	30	0.816	0.637	0.811	0.998	0.694	0.817	23460	23422
901	CvM2	TLI	MLR	ALL	ALL	0.656	0.567	0.666	0.989	0.652	0.612	85732	85647
902	CvM2	TLI	MLR	ALL	5	0.630	0.557	0.639	0.977	0.669	0.582	24596	24572
903	CvM2	TLI	MLR	ALL	10	0.664	0.563	0.674	0.986	0.662	0.627	28824	28785
904	CvM2	TLI	MLR	ALL	30	0.697	0.579	0.705	0.994	0.642	0.669	32312	32290
905	CvM2	TLI	MLR	30	ALL	0.574	0.533	0.575	0.967	0.638	0.494	17855	17854
906	CvM2	TLI	MLR	30	5	0.563	0.525	0.562	0.875	0.573	0.551	4747	4739
907	CvM2	TLI	MLR	30	10	0.587	0.527	0.586	0.928	0.477	0.657	5832	5832
908	CvM2	TLI	MLR	30	30	0.638	0.543	0.637	0.978	0.576	0.630	7276	7283
909	CvM2	TLI	MLR	50	ALL	0.624	0.544	0.629	0.983	0.623	0.586	20411	20417
910	CvM2	TLI	MLR	50	5	0.598	0.530	0.601	0.965	0.685	0.475	5706	5724
911	CvM2	TLI	MLR	50	10	0.657	0.548	0.658	0.960	0.489	0.753	6787	6790
912	CvM2	TLI	MLR	50	30	0.688	0.551	0.685	0.985	0.477	0.824	7918	7903
913	CvM2	TLI	MLR	100	ALL	0.704	0.568	0.714	0.989	0.595	0.728	22941	22901
914	CvM2	TLI	MLR	100	5	0.691	0.561	0.699	0.972	0.580	0.727	6647	6637
915	CvM2	TLI	MLR	100	10	0.732	0.562	0.729	0.981	0.532	0.854	7855	7823
916	CvM2	TLI	MLR	100	30	0.757	0.581	0.748	0.993	0.551	0.857	8439	8441
917	CvM2	TLI	MLR	200	ALL	0.764	0.594	0.767	0.994	0.626	0.789	24525	24475
918	CvM2	TLI	MLR	200	5	0.761	0.576	0.759	0.983	0.592	0.845	7496	7472
919	CvM2	TLI	MLR	200	10	0.778	0.587	0.763	0.989	0.555	0.903	8350	8340
920	CvM2	TLI	MLR	200	30	0.815	0.621	0.804	0.997	0.620	0.898	8679	8663
921	CvM2	TLI	ULSMV	ALL	ALL	0.691	0.546	0.719	0.974	0.435	0.871	71635	71584
922	CvM2	TLI	ULSMV	ALL	5	0.677	0.547	0.706	0.966	0.535	0.767	19976	19987
923	CvM2	TLI	ULSMV	ALL	10	0.711	0.555	0.722	0.964	0.455	0.888	24264	24300
924	CvM2	TLI	ULSMV	ALL	30	0.696	0.540	0.596	0.999	0.522	0.812	27395	27297
925	CvM2	TLI	ULSMV	30	ALL	0.570	0.512	0.622	0.999	0.409	0.720	13710	13635
926	CvM2	TLI	ULSMV	30	5	0.565	0.514	0.584	0.966	0.521	0.604	3256	3200
927	CvM2	TLI	ULSMV	30	10	0.589	0.516	0.611	0.999	0.487	0.664	4632	4632
928	CvM2	TLI	ULSMV	30	30	0.571	0.510	0.422	0.999	0.250	0.881	5822	5803
929	CvM2	TLI	ULSMV	50	ALL	0.641	0.531	0.665	0.995	0.545	0.685	16566	16505
930	CvM2	TLI	ULSMV	50	5	0.606	0.527	0.615	0.989	0.644	0.521	4339	4319
931	CvM2	TLI	ULSMV	50	10	0.675	0.548	0.682	0.966	0.466	0.800	5547	5551
932	CvM2	TLI	ULSMV	50	30	0.647	0.525	0.537	0.997	0.399	0.861	6680	6635
933	CvM2	TLI	ULSMV	100	ALL	0.729	0.565	0.712	0.964	0.447	0.906	19541	19571
934	CvM2	TLI	ULSMV	100	5	0.715	0.565	0.718	0.969	0.584	0.752	5546	5570
935	CvM2	TLI	ULSMV	100	10	0.750	0.582	0.737	0.957	0.470	0.932	6712	6741
936	CvM2	TLI	ULSMV	100	30	0.733	0.556	0.658	0.999	0.586	0.806	7283	7260
937	CvM2	TLI	ULSMV	200	ALL	0.784	0.610	0.750	0.973	0.511	0.946	21818	21873
938	CvM2	TLI	ULSMV	200	5	0.763	0.576	0.745	0.970	0.536	0.914	6835	6898
939	CvM2	TLI	ULSMV	200	10	0.780	0.604	0.744	0.962	0.505	0.974	7373	7376
940	CvM2	TLI	ULSMV	200	30	0.822	0.651	0.781	0.999	0.766	0.742	7610	7599
941	CvM2	TLI	WLSMV	ALL	ALL	0.665	0.542	0.700	0.996	0.563	0.707	66216	66637
942	CvM2	TLI	WLSMV	ALL	5	0.663	0.547	0.683	0.984	0.585	0.692	18319	18523
943	CvM2	TLI	WLSMV	ALL	10	0.694	0.553	0.710	0.990	0.535	0.781	22482	22664
944	CvM2	TLI	WLSMV	ALL	30	0.675	0.534	0.658	0.998	0.458	0.847	25415	25450
945	CvM2	TLI	WLSMV	30	ALL	0.541	0.507	0.559	1.000	0.441	0.635	12160	12278
946	CvM2	TLI	WLSMV	30	5	0.553	0.514	0.557	0.962	0.475	0.615	2872	2886
947	CvM2	TLI	WLSMV	30	10	0.561	0.512	0.577	0.989	0.424	0.688	4066	4131
948	CvM2	TLI	WLSMV	30	30	0.531	0.504	0.535	1.000	0.235	0.825	5222	5261
949	CvM2	TLI	WLSMV	50	ALL	0.601	0.523	0.622	0.999	0.590	0.586	15127	15246
950	CvM2	TLI	WLSMV	50	5	0.590	0.523	0.594	0.965	0.434	0.708	3768	3851
951	CvM2	TLI	WLSMV	50	10	0.635	0.539	0.639	0.992	0.613	0.592	5179	5218
952	CvM2	TLI	WLSMV	50	30	0.607	0.517	0.556	0.999	0.393	0.797	6180	6177
953	CvM2	TLI	WLSMV	100	ALL	0.699	0.559	0.712	0.995	0.603	0.708	18265	18372
954	CvM2	TLI	WLSMV	100	5	0.689	0.558	0.694	0.982	0.583	0.711	5191	5241
955	CvM2	TLI	WLSMV	100	10	0.733	0.573	0.722	0.988	0.535	0.836	6232	6279
956	CvM2	TLI	WLSMV	100	30	0.723	0.552	0.691	0.997	0.492	0.881	6842	6852
957	CvM2	TLI	WLSMV	200	ALL	0.776	0.611	0.767	0.996	0.644	0.771	20664	20741
958	CvM2	TLI	WLSMV	200	5	0.760	0.578	0.749	0.984	0.547	0.883	6488	6545
959	CvM2	TLI	WLSMV	200	10	0.794	0.611	0.776	0.992	0.581	0.890	7005	7036
960	CvM2	TLI	WLSMV	200	30	0.827	0.638	0.812	0.998	0.663	0.873	7171	7160
961	CvM2	RMSEA	ALL	ALL	ALL	0.664	0.548	0.692	0.009	0.587	0.679	223583	223868
962	CvM2	RMSEA	ALL	ALL	5	0.654	0.549	0.671	0.016	0.578	0.687	62891	63082
963	CvM2	RMSEA	ALL	ALL	10	0.686	0.556	0.704	0.012	0.573	0.734	75570	75749
964	CvM2	RMSEA	ALL	ALL	30	0.681	0.543	0.709	0.005	0.544	0.763	85122	85037
965	CvM2	RMSEA	ALL	30	ALL	0.551	0.512	0.558	0.004	0.607	0.479	43725	43767
966	CvM2	RMSEA	ALL	30	5	0.549	0.516	0.549	0.019	0.636	0.444	10875	10825
967	CvM2	RMSEA	ALL	30	10	0.558	0.515	0.555	0.008	0.619	0.477	14530	14595
968	CvM2	RMSEA	ALL	30	30	0.443	NA	0.473	-Inf	0.000	1.000	18320	18347
969	CvM2	RMSEA	ALL	50	ALL	0.612	0.530	0.625	0.009	0.582	0.598	52104	52168
970	CvM2	RMSEA	ALL	50	5	0.590	0.527	0.590	0.021	0.489	0.647	13813	13894
971	CvM2	RMSEA	ALL	50	10	0.642	0.545	0.640	0.014	0.559	0.648	17513	17559
972	CvM2	RMSEA	ALL	50	30	0.629	0.525	0.591	0.005	0.522	0.679	20778	20715
973	CvM2	RMSEA	ALL	100	ALL	0.705	0.563	0.710	0.009	0.596	0.720	60747	60844
974	CvM2	RMSEA	ALL	100	5	0.696	0.561	0.696	0.016	0.585	0.713	17384	17448
975	CvM2	RMSEA	ALL	100	10	0.736	0.572	0.722	0.014	0.498	0.868	20799	20843
976	CvM2	RMSEA	ALL	100	30	0.731	0.559	0.700	0.006	0.535	0.837	22564	22553
977	CvM2	RMSEA	ALL	200	ALL	0.771	0.605	0.760	0.006	0.679	0.729	67007	67089
978	CvM2	RMSEA	ALL	200	5	0.759	0.577	0.742	0.015	0.536	0.892	20819	20915
979	CvM2	RMSEA	ALL	200	10	0.787	0.601	0.766	0.010	0.582	0.883	22728	22752
980	CvM2	RMSEA	ALL	200	30	0.822	0.637	0.804	0.005	0.636	0.882	23460	23422
981	CvM2	RMSEA	MLR	ALL	ALL	0.653	0.566	0.662	0.010	0.672	0.576	85732	85647
982	CvM2	RMSEA	MLR	ALL	5	0.630	0.556	0.637	0.016	0.686	0.548	24596	24572
983	CvM2	RMSEA	MLR	ALL	10	0.662	0.562	0.669	0.013	0.637	0.634	28824	28785
984	CvM2	RMSEA	MLR	ALL	30	0.694	0.579	0.701	0.008	0.667	0.635	32312	32290
985	CvM2	RMSEA	MLR	30	ALL	0.570	0.532	0.572	0.019	0.665	0.456	17855	17854
986	CvM2	RMSEA	MLR	30	5	0.566	0.523	0.566	0.045	0.551	0.546	4747	4739
987	CvM2	RMSEA	MLR	30	10	0.583	0.525	0.584	0.035	0.379	0.744	5832	5832
988	CvM2	RMSEA	MLR	30	30	0.631	0.541	0.632	0.020	0.389	0.810	7276	7283
989	CvM2	RMSEA	MLR	50	ALL	0.620	0.543	0.624	0.013	0.646	0.549	20411	20417
990	CvM2	RMSEA	MLR	50	5	0.599	0.529	0.598	0.035	0.372	0.773	5706	5724
991	CvM2	RMSEA	MLR	50	10	0.652	0.546	0.650	0.026	0.382	0.841	6787	6790
992	CvM2	RMSEA	MLR	50	30	0.684	0.550	0.677	0.013	0.465	0.827	7918	7903
993	CvM2	RMSEA	MLR	100	ALL	0.699	0.568	0.703	0.010	0.621	0.685	22941	22901
994	CvM2	RMSEA	MLR	100	5	0.691	0.560	0.690	0.020	0.530	0.756	6647	6637
995	CvM2	RMSEA	MLR	100	10	0.724	0.562	0.714	0.016	0.492	0.877	7855	7823
996	CvM2	RMSEA	MLR	100	30	0.752	0.581	0.743	0.009	0.517	0.879	8439	8441
997	CvM2	RMSEA	MLR	200	ALL	0.758	0.594	0.753	0.008	0.640	0.755	24525	24475
998	CvM2	RMSEA	MLR	200	5	0.752	0.576	0.738	0.016	0.523	0.893	7496	7472
999	CvM2	RMSEA	MLR	200	10	0.771	0.587	0.754	0.012	0.530	0.911	8350	8340
1000	CvM2	RMSEA	MLR	200	30	0.812	0.621	0.798	0.006	0.606	0.903	8679	8663
1001	CvM2	RMSEA	ULSMV	ALL	ALL	0.687	0.546	0.711	0.007	0.567	0.730	71635	71584
1002	CvM2	RMSEA	ULSMV	ALL	5	0.684	0.547	0.703	0.016	0.512	0.788	19976	19987
1003	CvM2	RMSEA	ULSMV	ALL	10	0.715	0.555	0.710	0.012	0.515	0.833	24264	24300
1004	CvM2	RMSEA	ULSMV	ALL	30	0.695	0.540	0.652	0.004	0.493	0.844	27395	27297
1005	CvM2	RMSEA	ULSMV	30	ALL	0.433	NA	0.395	-Inf	0.000	1.000	13710	13635
1006	CvM2	RMSEA	ULSMV	30	5	0.566	0.514	0.581	0.011	0.541	0.586	3256	3200
1007	CvM2	RMSEA	ULSMV	30	10	0.412	NA	0.402	-Inf	0.000	1.000	4632	4632
1008	CvM2	RMSEA	ULSMV	30	30	0.433	NA	0.444	-Inf	0.000	1.000	5822	5803
1009	CvM2	RMSEA	ULSMV	50	ALL	0.635	0.531	0.659	0.005	0.564	0.661	16566	16505
1010	CvM2	RMSEA	ULSMV	50	5	0.609	0.527	0.611	0.016	0.503	0.662	4339	4319
1011	CvM2	RMSEA	ULSMV	50	10	0.676	0.548	0.666	0.011	0.545	0.722	5547	5551
1012	CvM2	RMSEA	ULSMV	50	30	0.357	NA	0.451	-Inf	0.000	1.000	6680	6635
1013	CvM2	RMSEA	ULSMV	100	ALL	0.724	0.565	0.714	0.007	0.627	0.719	19541	19571
1014	CvM2	RMSEA	ULSMV	100	5	0.719	0.565	0.713	0.017	0.523	0.821	5546	5570
1015	CvM2	RMSEA	ULSMV	100	10	0.756	0.582	0.736	0.012	0.576	0.830	6712	6741
1016	CvM2	RMSEA	ULSMV	100	30	0.735	0.556	0.676	0.005	0.513	0.883	7283	7260
1017	CvM2	RMSEA	ULSMV	200	ALL	0.782	0.610	0.760	0.006	0.663	0.758	21818	21873
1018	CvM2	RMSEA	ULSMV	200	5	0.766	0.576	0.744	0.015	0.541	0.907	6835	6898
1019	CvM2	RMSEA	ULSMV	200	10	0.794	0.604	0.769	0.012	0.538	0.944	7373	7376
1020	CvM2	RMSEA	ULSMV	200	30	0.833	0.651	0.823	0.004	0.716	0.817	7610	7599
1021	CvM2	RMSEA	WLSMV	ALL	ALL	0.664	0.542	0.697	0.006	0.583	0.685	66216	66637
1022	CvM2	RMSEA	WLSMV	ALL	5	0.665	0.547	0.684	0.013	0.585	0.692	18319	18523
1023	CvM2	RMSEA	WLSMV	ALL	10	0.696	0.553	0.711	0.011	0.547	0.767	22482	22664
1024	CvM2	RMSEA	WLSMV	ALL	30	0.674	0.534	0.679	0.004	0.468	0.836	25415	25450
1025	CvM2	RMSEA	WLSMV	30	ALL	0.459	NA	0.444	0.054	1.000	0.001	12160	12278
1026	CvM2	RMSEA	WLSMV	30	5	0.553	0.514	0.557	0.017	0.542	0.545	2872	2886
1027	CvM2	RMSEA	WLSMV	30	10	0.560	0.512	0.575	0.008	0.478	0.629	4066	4131
1028	CvM2	RMSEA	WLSMV	30	30	0.470	NA	0.455	0.021	1.000	0.000	5222	5261
1029	CvM2	RMSEA	WLSMV	50	ALL	0.599	0.523	0.617	0.003	0.589	0.587	15127	15246
1030	CvM2	RMSEA	WLSMV	50	5	0.589	0.523	0.593	0.013	0.605	0.533	3768	3851
1031	CvM2	RMSEA	WLSMV	50	10	0.634	0.539	0.638	0.009	0.637	0.569	5179	5218
1032	CvM2	RMSEA	WLSMV	50	30	0.396	NA	0.420	-Inf	0.000	1.000	6180	6177
1033	CvM2	RMSEA	WLSMV	100	ALL	0.696	0.559	0.708	0.007	0.613	0.698	18265	18372
1034	CvM2	RMSEA	WLSMV	100	5	0.689	0.558	0.691	0.015	0.549	0.745	5191	5241
1035	CvM2	RMSEA	WLSMV	100	10	0.733	0.573	0.722	0.012	0.540	0.826	6232	6279
1036	CvM2	RMSEA	WLSMV	100	30	0.722	0.552	0.689	0.005	0.501	0.867	6842	6852
1037	CvM2	RMSEA	WLSMV	200	ALL	0.775	0.611	0.768	0.006	0.675	0.743	20664	20741
1038	CvM2	RMSEA	WLSMV	200	5	0.760	0.578	0.746	0.014	0.541	0.892	6488	6545
1039	CvM2	RMSEA	WLSMV	200	10	0.797	0.611	0.778	0.010	0.602	0.879	7005	7036
1040	CvM2	RMSEA	WLSMV	200	30	0.827	0.638	0.810	0.005	0.644	0.891	7171	7160
1041	CvM2	SRMRW	ALL	ALL	ALL	0.527	0.505	0.524	0.031	0.452	0.597	223583	223868
1042	CvM2	SRMRW	ALL	ALL	5	0.524	0.512	0.523	0.037	0.639	0.405	62891	63082
1043	CvM2	SRMRW	ALL	ALL	10	0.530	0.508	0.528	0.031	0.498	0.553	75570	75749
1044	CvM2	SRMRW	ALL	ALL	30	0.535	0.505	0.536	0.025	0.268	0.796	85122	85037
1045	CvM2	SRMRW	ALL	30	ALL	0.505	0.500	0.505	0.041	0.572	0.440	43725	43767
1046	CvM2	SRMRW	ALL	30	5	0.505	0.503	0.506	0.067	0.671	0.344	10875	10825
1047	CvM2	SRMRW	ALL	30	10	0.504	0.500	0.506	0.069	0.112	0.904	14530	14595
1048	CvM2	SRMRW	ALL	30	30	0.507	0.500	0.509	0.041	0.135	0.891	18320	18347
1049	CvM2	SRMRW	ALL	50	ALL	0.517	0.502	0.517	0.037	0.483	0.547	52104	52168
1050	CvM2	SRMRW	ALL	50	5	0.513	0.505	0.515	0.054	0.618	0.405	13813	13894
1051	CvM2	SRMRW	ALL	50	10	0.516	0.504	0.519	0.047	0.198	0.834	17513	17559
1052	CvM2	SRMRW	ALL	50	30	0.516	0.501	0.522	0.033	0.137	0.916	20778	20715
1053	CvM2	SRMRW	ALL	100	ALL	0.532	0.503	0.534	0.033	0.359	0.697	60747	60844
1054	CvM2	SRMRW	ALL	100	5	0.538	0.510	0.543	0.044	0.365	0.690	17384	17448
1055	CvM2	SRMRW	ALL	100	10	0.536	0.506	0.545	0.038	0.138	0.932	20799	20843
1056	CvM2	SRMRW	ALL	100	30	0.528	0.503	0.540	0.026	0.153	0.935	22564	22553
1057	CvM2	SRMRW	ALL	200	ALL	0.554	0.506	0.561	0.028	0.275	0.822	67007	67089
1058	CvM2	SRMRW	ALL	200	5	0.570	0.516	0.579	0.032	0.383	0.721	20819	20915
1059	CvM2	SRMRW	ALL	200	10	0.558	0.510	0.573	0.028	0.161	0.948	22728	22752
1060	CvM2	SRMRW	ALL	200	30	0.545	0.506	0.566	0.023	0.153	0.974	23460	23422
1061	CvM2	SRMRW	MLR	ALL	ALL	0.507	0.501	0.508	0.028	0.386	0.630	85732	85647
1062	CvM2	SRMRW	MLR	ALL	5	0.522	0.516	0.524	0.029	0.752	0.296	24596	24572
1063	CvM2	SRMRW	MLR	ALL	10	0.509	0.508	0.510	0.018	0.782	0.240	28824	28785
1064	CvM2	SRMRW	MLR	ALL	30	0.504	0.504	0.504	0.009	0.884	0.127	32312	32290
1065	CvM2	SRMRW	MLR	30	ALL	0.499	NA	0.497	0.024	0.270	0.736	17855	17854
1066	CvM2	SRMRW	MLR	30	5	0.521	0.505	0.523	0.071	0.204	0.836	4747	4739
1067	CvM2	SRMRW	MLR	30	10	0.504	NA	0.503	0.044	0.329	0.693	5832	5832
1068	CvM2	SRMRW	MLR	30	30	0.502	0.501	0.502	0.024	0.674	0.340	7276	7283
1069	CvM2	SRMRW	MLR	50	ALL	0.506	0.501	0.508	0.048	0.181	0.834	20411	20417
1070	CvM2	SRMRW	MLR	50	5	0.526	0.506	0.527	0.048	0.630	0.417	5706	5724
1071	CvM2	SRMRW	MLR	50	10	0.516	0.503	0.515	0.031	0.624	0.408	6787	6790
1072	CvM2	SRMRW	MLR	50	30	0.504	0.501	0.504	0.019	0.278	0.736	7918	7903
1073	CvM2	SRMRW	MLR	100	ALL	0.509	0.502	0.513	0.036	0.134	0.892	22941	22901
1074	CvM2	SRMRW	MLR	100	5	0.556	0.512	0.557	0.038	0.354	0.734	6647	6637
1075	CvM2	SRMRW	MLR	100	10	0.523	0.507	0.524	0.024	0.399	0.641	7855	7823
1076	CvM2	SRMRW	MLR	100	30	0.510	0.502	0.510	0.013	0.302	0.720	8439	8441
1077	CvM2	SRMRW	MLR	200	ALL	0.518	0.504	0.527	0.029	0.081	0.964	24525	24475
1078	CvM2	SRMRW	MLR	200	5	0.592	0.520	0.599	0.029	0.252	0.889	7496	7472
1079	CvM2	SRMRW	MLR	200	10	0.548	0.511	0.548	0.016	0.555	0.518	8350	8340
1080	CvM2	SRMRW	MLR	200	30	0.523	0.507	0.523	0.009	0.569	0.472	8679	8663
1081	CvM2	SRMRW	ULSMV	ALL	ALL	0.562	0.521	0.553	0.032	0.608	0.503	71635	71584
1082	CvM2	SRMRW	ULSMV	ALL	5	0.533	0.511	0.528	0.042	0.674	0.407	19976	19987
1083	CvM2	SRMRW	ULSMV	ALL	10	0.563	0.516	0.555	0.032	0.660	0.469	24264	24300
1084	CvM2	SRMRW	ULSMV	ALL	30	0.600	0.511	0.588	0.025	0.565	0.618	27395	27297
1085	CvM2	SRMRW	ULSMV	30	ALL	0.513	0.502	0.512	0.041	0.722	0.313	13710	13635
1086	CvM2	SRMRW	ULSMV	30	5	0.503	NA	0.503	0.102	0.320	0.704	3256	3200
1087	CvM2	SRMRW	ULSMV	30	10	0.516	NA	0.517	0.069	0.338	0.712	4632	4632
1088	CvM2	SRMRW	ULSMV	30	30	0.533	0.501	0.532	0.041	0.407	0.677	5822	5803
1089	CvM2	SRMRW	ULSMV	50	ALL	0.539	0.508	0.536	0.034	0.716	0.353	16566	16505
1090	CvM2	SRMRW	ULSMV	50	5	0.522	0.502	0.524	0.084	0.184	0.862	4339	4319
1091	CvM2	SRMRW	ULSMV	50	10	0.544	0.501	0.543	0.056	0.278	0.808	5547	5551
1092	CvM2	SRMRW	ULSMV	50	30	0.570	0.501	0.569	0.033	0.408	0.751	6680	6635
1093	CvM2	SRMRW	ULSMV	100	ALL	0.580	0.511	0.576	0.035	0.520	0.613	19541	19571
1094	CvM2	SRMRW	ULSMV	100	5	0.563	0.503	0.566	0.055	0.330	0.792	5546	5570
1095	CvM2	SRMRW	ULSMV	100	10	0.595	0.505	0.597	0.038	0.402	0.801	6712	6741
1096	CvM2	SRMRW	ULSMV	100	30	0.605	0.503	0.611	0.026	0.457	0.807	7283	7260
1097	CvM2	SRMRW	ULSMV	200	ALL	0.630	0.514	0.628	0.028	0.527	0.695	21818	21873
1098	CvM2	SRMRW	ULSMV	200	5	0.606	0.504	0.616	0.041	0.333	0.889	6835	6898
1099	CvM2	SRMRW	ULSMV	200	10	0.630	0.504	0.644	0.029	0.427	0.893	7373	7376
1100	CvM2	SRMRW	ULSMV	200	30	0.636	0.504	0.661	0.023	0.459	0.921	7610	7599
1101	CvM2	SRMRW	WLSMV	ALL	ALL	0.515	0.507	0.516	0.021	0.700	0.325	66216	66637
1102	CvM2	SRMRW	WLSMV	ALL	5	0.520	0.515	0.520	0.032	0.794	0.257	18319	18523
1103	CvM2	SRMRW	WLSMV	ALL	10	0.522	0.518	0.523	0.021	0.834	0.220	22482	22664
1104	CvM2	SRMRW	WLSMV	ALL	30	0.518	0.517	0.521	0.012	0.838	0.207	25415	25450
1105	CvM2	SRMRW	WLSMV	30	ALL	0.504	0.503	0.504	0.026	0.885	0.126	12160	12278
1106	CvM2	SRMRW	WLSMV	30	5	0.509	0.500	0.509	0.081	0.429	0.593	2872	2886
1107	CvM2	SRMRW	WLSMV	30	10	0.504	NA	0.503	0.053	0.440	0.579	4066	4131
1108	CvM2	SRMRW	WLSMV	30	30	0.512	0.503	0.513	0.026	0.741	0.283	5222	5261
1109	CvM2	SRMRW	WLSMV	50	ALL	0.513	0.506	0.514	0.023	0.745	0.278	15127	15246
1110	CvM2	SRMRW	WLSMV	50	5	0.516	0.502	0.514	0.062	0.470	0.567	3768	3851
1111	CvM2	SRMRW	WLSMV	50	10	0.517	0.505	0.517	0.039	0.572	0.458	5179	5218
1112	CvM2	SRMRW	WLSMV	50	30	0.524	0.507	0.523	0.023	0.380	0.660	6180	6177
1113	CvM2	SRMRW	WLSMV	100	ALL	0.518	0.510	0.521	0.028	0.460	0.571	18265	18372
1114	CvM2	SRMRW	WLSMV	100	5	0.542	0.508	0.540	0.044	0.438	0.640	5191	5241
1115	CvM2	SRMRW	WLSMV	100	10	0.548	0.512	0.547	0.028	0.524	0.553	6232	6279
1116	CvM2	SRMRW	WLSMV	100	30	0.544	0.513	0.544	0.017	0.343	0.729	6842	6852
1117	CvM2	SRMRW	WLSMV	200	ALL	0.535	0.519	0.542	0.021	0.475	0.584	20664	20741
1118	CvM2	SRMRW	WLSMV	200	5	0.580	0.513	0.581	0.032	0.447	0.687	6488	6545
1119	CvM2	SRMRW	WLSMV	200	10	0.599	0.519	0.599	0.021	0.486	0.674	7005	7036
1120	CvM2	SRMRW	WLSMV	200	30	0.589	0.523	0.588	0.011	0.463	0.684	7171	7160
1121	CvM2	SRMRB	ALL	ALL	ALL	0.634	0.601	0.642	0.076	0.777	0.441	223583	223868
1122	CvM2	SRMRB	ALL	ALL	5	0.596	0.576	0.602	0.078	0.821	0.344	62891	63082
1123	CvM2	SRMRB	ALL	ALL	10	0.631	0.599	0.639	0.078	0.769	0.441	75570	75749
1124	CvM2	SRMRB	ALL	ALL	30	0.675	0.625	0.683	0.071	0.773	0.497	85122	85037
1125	CvM2	SRMRB	ALL	30	ALL	0.584	0.551	0.585	0.127	0.718	0.407	43725	43767
1126	CvM2	SRMRB	ALL	30	5	0.560	0.526	0.557	0.162	0.578	0.522	10875	10825
1127	CvM2	SRMRB	ALL	30	10	0.575	0.542	0.572	0.127	0.729	0.390	14530	14595
1128	CvM2	SRMRB	ALL	30	30	0.623	0.576	0.627	0.115	0.762	0.416	18320	18347
1129	CvM2	SRMRB	ALL	50	ALL	0.630	0.584	0.633	0.102	0.727	0.466	52104	52168
1130	CvM2	SRMRB	ALL	50	5	0.588	0.542	0.584	0.123	0.626	0.513	13813	13894
1131	CvM2	SRMRB	ALL	50	10	0.619	0.578	0.618	0.102	0.753	0.440	17513	17559
1132	CvM2	SRMRB	ALL	50	30	0.687	0.621	0.699	0.092	0.773	0.488	20778	20715
1133	CvM2	SRMRB	ALL	100	ALL	0.701	0.637	0.703	0.080	0.706	0.600	60747	60844
1134	CvM2	SRMRB	ALL	100	5	0.641	0.591	0.636	0.088	0.731	0.495	17384	17448
1135	CvM2	SRMRB	ALL	100	10	0.698	0.643	0.701	0.078	0.762	0.548	20799	20843
1136	CvM2	SRMRB	ALL	100	30	0.781	0.683	0.808	0.076	0.666	0.717	22564	22553
1137	CvM2	SRMRB	ALL	200	ALL	0.766	0.681	0.765	0.061	0.729	0.677	67007	67089
1138	CvM2	SRMRB	ALL	200	5	0.701	0.642	0.698	0.069	0.742	0.593	20819	20915
1139	CvM2	SRMRB	ALL	200	10	0.776	0.696	0.791	0.059	0.763	0.643	22728	22752
1140	CvM2	SRMRB	ALL	200	30	0.848	0.729	0.876	0.058	0.674	0.825	23460	23422
1141	CvM2	SRMRB	MLR	ALL	ALL	0.654	0.621	0.659	0.092	0.814	0.441	85732	85647
1142	CvM2	SRMRB	MLR	ALL	5	0.617	0.599	0.626	0.097	0.848	0.356	24596	24572
1143	CvM2	SRMRB	MLR	ALL	10	0.653	0.621	0.661	0.092	0.816	0.432	28824	28785
1144	CvM2	SRMRB	MLR	ALL	30	0.699	0.642	0.703	0.087	0.808	0.505	32312	32290
1145	CvM2	SRMRB	MLR	30	ALL	0.613	0.593	0.619	0.143	0.871	0.324	17855	17854
1146	CvM2	SRMRB	MLR	30	5	0.575	0.552	0.576	0.172	0.771	0.376	4747	4739
1147	CvM2	SRMRB	MLR	30	10	0.607	0.583	0.610	0.151	0.817	0.375	5832	5832
1148	CvM2	SRMRB	MLR	30	30	0.676	0.644	0.687	0.143	0.805	0.486	7276	7283
1149	CvM2	SRMRB	MLR	50	ALL	0.678	0.640	0.683	0.122	0.824	0.466	20411	20417
1150	CvM2	SRMRB	MLR	50	5	0.629	0.590	0.628	0.143	0.719	0.511	5706	5724
1151	CvM2	SRMRB	MLR	50	10	0.675	0.648	0.680	0.125	0.803	0.507	6787	6790
1152	CvM2	SRMRB	MLR	50	30	0.756	0.694	0.768	0.119	0.758	0.624	7918	7903
1153	CvM2	SRMRB	MLR	100	ALL	0.756	0.699	0.754	0.098	0.744	0.672	22941	22901
1154	CvM2	SRMRB	MLR	100	5	0.703	0.673	0.704	0.104	0.822	0.549	6647	6637
1155	CvM2	SRMRB	MLR	100	10	0.757	0.721	0.762	0.094	0.832	0.606	7855	7823
1156	CvM2	SRMRB	MLR	100	30	0.846	0.746	0.866	0.087	0.809	0.692	8439	8441
1157	CvM2	SRMRB	MLR	200	ALL	0.808	0.729	0.800	0.072	0.795	0.710	24525	24475
1158	CvM2	SRMRB	MLR	200	5	0.757	0.723	0.760	0.079	0.828	0.623	7496	7472
1159	CvM2	SRMRB	MLR	200	10	0.821	0.751	0.838	0.071	0.828	0.680	8350	8340
1160	CvM2	SRMRB	MLR	200	30	0.889	0.767	0.908	0.071	0.685	0.906	8679	8663
1161	CvM2	SRMRB	ULSMV	ALL	ALL	0.629	0.612	0.642	0.063	0.813	0.409	71635	71584
1162	CvM2	SRMRB	ULSMV	ALL	5	0.589	0.581	0.601	0.065	0.855	0.311	19976	19987
1163	CvM2	SRMRB	ULSMV	ALL	10	0.626	0.610	0.641	0.058	0.869	0.349	24264	24300
1164	CvM2	SRMRB	ULSMV	ALL	30	0.670	0.638	0.682	0.059	0.812	0.464	27395	27297
1165	CvM2	SRMRB	ULSMV	30	ALL	0.575	0.555	0.582	0.106	0.747	0.383	13710	13635
1166	CvM2	SRMRB	ULSMV	30	5	0.559	0.529	0.559	0.116	0.811	0.289	3256	3200
1167	CvM2	SRMRB	ULSMV	30	10	0.571	0.542	0.569	0.108	0.735	0.403	4632	4632
1168	CvM2	SRMRB	ULSMV	30	30	0.605	0.587	0.615	0.096	0.796	0.395	5822	5803
1169	CvM2	SRMRB	ULSMV	50	ALL	0.622	0.597	0.631	0.082	0.833	0.371	16566	16505
1170	CvM2	SRMRB	ULSMV	50	5	0.582	0.549	0.580	0.104	0.640	0.504	4339	4319
1171	CvM2	SRMRB	ULSMV	50	10	0.617	0.591	0.620	0.088	0.758	0.458	5547	5551
1172	CvM2	SRMRB	ULSMV	50	30	0.674	0.647	0.693	0.076	0.835	0.460	6680	6635
1173	CvM2	SRMRB	ULSMV	100	ALL	0.701	0.664	0.707	0.065	0.826	0.498	19541	19571
1174	CvM2	SRMRB	ULSMV	100	5	0.641	0.611	0.638	0.075	0.771	0.481	5546	5570
1175	CvM2	SRMRB	ULSMV	100	10	0.698	0.681	0.706	0.063	0.879	0.489	6712	6741
1176	CvM2	SRMRB	ULSMV	100	30	0.777	0.716	0.809	0.055	0.930	0.480	7283	7260
1177	CvM2	SRMRB	ULSMV	200	ALL	0.769	0.715	0.772	0.049	0.847	0.571	21818	21873
1178	CvM2	SRMRB	ULSMV	200	5	0.703	0.673	0.703	0.059	0.783	0.567	6835	6898
1179	CvM2	SRMRB	ULSMV	200	10	0.778	0.739	0.798	0.049	0.876	0.574	7373	7376
1180	CvM2	SRMRB	ULSMV	200	30	0.858	0.770	0.884	0.043	0.918	0.585	7610	7599
1181	CvM2	SRMRB	WLSMV	ALL	ALL	0.641	0.614	0.648	0.069	0.827	0.414	66216	66637
1182	CvM2	SRMRB	WLSMV	ALL	5	0.598	0.590	0.609	0.074	0.829	0.358	18319	18523
1183	CvM2	SRMRB	WLSMV	ALL	10	0.634	0.610	0.642	0.071	0.806	0.419	22482	22664
1184	CvM2	SRMRB	WLSMV	ALL	30	0.685	0.637	0.691	0.067	0.815	0.485	25415	25450
1185	CvM2	SRMRB	WLSMV	30	ALL	0.598	0.570	0.602	0.120	0.784	0.374	12160	12278
1186	CvM2	SRMRB	WLSMV	30	5	0.572	0.533	0.568	0.152	0.578	0.555	2872	2886
1187	CvM2	SRMRB	WLSMV	30	10	0.596	0.562	0.592	0.122	0.781	0.391	4066	4131
1188	CvM2	SRMRB	WLSMV	30	30	0.642	0.605	0.649	0.113	0.815	0.414	5222	5261
1189	CvM2	SRMRB	WLSMV	50	ALL	0.656	0.615	0.660	0.096	0.801	0.449	15127	15246
1190	CvM2	SRMRB	WLSMV	50	5	0.609	0.559	0.604	0.112	0.697	0.483	3768	3851
1191	CvM2	SRMRB	WLSMV	50	10	0.637	0.608	0.635	0.096	0.830	0.422	5179	5218
1192	CvM2	SRMRB	WLSMV	50	30	0.732	0.668	0.744	0.090	0.819	0.527	6180	6177
1193	CvM2	SRMRB	WLSMV	100	ALL	0.736	0.678	0.732	0.076	0.746	0.635	18265	18372
1194	CvM2	SRMRB	WLSMV	100	5	0.662	0.631	0.658	0.082	0.795	0.501	5191	5241
1195	CvM2	SRMRB	WLSMV	100	10	0.724	0.684	0.724	0.074	0.804	0.582	6232	6279
1196	CvM2	SRMRB	WLSMV	100	30	0.840	0.738	0.861	0.069	0.792	0.699	6842	6852
1197	CvM2	SRMRB	WLSMV	200	ALL	0.795	0.713	0.785	0.058	0.757	0.723	20664	20741
1198	CvM2	SRMRB	WLSMV	200	5	0.722	0.683	0.717	0.062	0.815	0.581	6488	6545
1199	CvM2	SRMRB	WLSMV	200	10	0.807	0.735	0.816	0.055	0.824	0.665	7005	7036
1200	CvM2	SRMRB	WLSMV	200	30	0.889	0.768	0.909	0.056	0.684	0.905	7171	7160

Summarizing the Results

So, I need to parse down 1200 rows of information into somethingthat can fit into a single page table. The above (and very large tables) are condensed to only include the AUC and optimal threshold. The remaining information is left here for reference.

Detecting Any Misspecification

c <- filter(roc_summary, Classification == "C", `Level-2 SS` != 'ALL', `Level-1 SS` == 'ALL')
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Estimator == 'MLR', c(2,4,6,9:11)],
            c[ c$Estimator == 'ULSMV', c(6,9:11)],
            c[ c$Estimator == 'WLSMV', c(6,9:11)])
kable(c1, format = 'html',digits=3, row.names = F) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '=2, 'MLR'=4, 'USLMV'=4, 'WLSMV'=4))

		MLR				USLMV				WLSMV
Index	Level-2 SS	AUC	Threshold	Specificity	Sensitivity	AUC	Threshold	Specificity	Sensitivity	AUC	Threshold	Specificity	Sensitivity
CFI	30	0.747	0.940	0.726	0.698	0.628	0.982	0.431	0.821	0.705	0.983	0.546	0.811
CFI	50	0.828	0.956	0.713	0.835	0.712	0.973	0.552	0.826	0.802	0.980	0.647	0.848
CFI	100	0.893	0.974	0.750	0.918	0.827	0.971	0.667	0.906	0.876	0.979	0.708	0.936
CFI	200	0.917	0.981	0.767	0.973	0.911	0.975	0.785	0.957	0.910	0.986	0.756	0.960
TLI	30	0.746	0.928	0.725	0.698	0.628	0.979	0.430	0.821	0.705	0.979	0.547	0.809
TLI	50	0.827	0.947	0.713	0.835	0.712	0.968	0.552	0.826	0.801	0.976	0.648	0.846
TLI	100	0.893	0.968	0.750	0.918	0.827	0.965	0.666	0.906	0.876	0.975	0.708	0.936
TLI	200	0.917	0.977	0.767	0.973	0.911	0.970	0.785	0.956	0.910	0.984	0.756	0.960
RMSEA	30	0.725	0.029	0.741	0.660	0.628	0.008	0.448	0.771	0.700	0.013	0.545	0.786
RMSEA	50	0.814	0.026	0.716	0.814	0.706	0.009	0.590	0.749	0.790	0.011	0.718	0.736
RMSEA	100	0.889	0.020	0.747	0.916	0.813	0.012	0.671	0.839	0.873	0.015	0.723	0.910
RMSEA	200	0.915	0.017	0.759	0.979	0.893	0.013	0.689	0.947	0.910	0.014	0.752	0.963
SRMRW	30	0.674	0.042	0.738	0.568	0.642	0.052	0.789	0.459	0.682	0.050	0.738	0.585
SRMRW	50	0.737	0.037	0.744	0.682	0.709	0.048	0.770	0.595	0.733	0.047	0.689	0.738
SRMRW	100	0.816	0.037	0.672	0.903	0.803	0.046	0.689	0.835	0.797	0.045	0.627	0.910
SRMRW	200	0.832	0.032	0.661	0.993	0.855	0.042	0.664	0.971	0.813	0.038	0.607	0.992
SRMRB	30	0.565	0.143	0.793	0.325	0.567	0.106	0.734	0.381	0.576	0.117	0.793	0.325
SRMRB	50	0.610	0.119	0.744	0.435	0.596	0.081	0.795	0.366	0.613	0.096	0.730	0.450
SRMRB	100	0.673	0.093	0.681	0.583	0.649	0.060	0.843	0.398	0.670	0.071	0.728	0.534
SRMRB	200	0.732	0.069	0.708	0.646	0.701	0.047	0.793	0.516	0.723	0.054	0.699	0.643

c1 <- cbind(c[ c$Estimator == 'MLR', c(2,4,6,9)],
            c[ c$Estimator == 'ULSMV', c(6,9)],
            c[ c$Estimator == 'WLSMV', c(6,9)])
print(xtable(c1, digits = 3), booktabs=T,include.rownames = F)

% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Fri Oct 18 17:47:38 2019
\begin{table}[ht]
\centering
\begin{tabular}{llrrrrrr}
  \toprule
Index & Level-2 SS & AUC & Threshold & AUC & Threshold & AUC & Threshold \\ 
  \midrule
CFI & 30 & 0.747 & 0.940 & 0.628 & 0.982 & 0.705 & 0.983 \\ 
  CFI & 50 & 0.828 & 0.956 & 0.712 & 0.973 & 0.802 & 0.980 \\ 
  CFI & 100 & 0.893 & 0.974 & 0.827 & 0.971 & 0.876 & 0.979 \\ 
  CFI & 200 & 0.917 & 0.981 & 0.911 & 0.975 & 0.910 & 0.986 \\ 
  TLI & 30 & 0.746 & 0.928 & 0.628 & 0.979 & 0.705 & 0.979 \\ 
  TLI & 50 & 0.827 & 0.947 & 0.712 & 0.968 & 0.801 & 0.976 \\ 
  TLI & 100 & 0.893 & 0.968 & 0.827 & 0.965 & 0.876 & 0.975 \\ 
  TLI & 200 & 0.917 & 0.977 & 0.911 & 0.970 & 0.910 & 0.984 \\ 
  RMSEA & 30 & 0.725 & 0.029 & 0.628 & 0.008 & 0.700 & 0.013 \\ 
  RMSEA & 50 & 0.814 & 0.026 & 0.706 & 0.009 & 0.790 & 0.011 \\ 
  RMSEA & 100 & 0.889 & 0.020 & 0.813 & 0.012 & 0.873 & 0.015 \\ 
  RMSEA & 200 & 0.915 & 0.017 & 0.893 & 0.013 & 0.910 & 0.014 \\ 
  SRMRW & 30 & 0.674 & 0.042 & 0.642 & 0.052 & 0.682 & 0.050 \\ 
  SRMRW & 50 & 0.737 & 0.037 & 0.709 & 0.048 & 0.733 & 0.047 \\ 
  SRMRW & 100 & 0.816 & 0.037 & 0.803 & 0.046 & 0.797 & 0.045 \\ 
  SRMRW & 200 & 0.832 & 0.032 & 0.855 & 0.042 & 0.813 & 0.038 \\ 
  SRMRB & 30 & 0.565 & 0.143 & 0.567 & 0.106 & 0.576 & 0.117 \\ 
  SRMRB & 50 & 0.610 & 0.119 & 0.596 & 0.081 & 0.613 & 0.096 \\ 
  SRMRB & 100 & 0.673 & 0.093 & 0.649 & 0.060 & 0.670 & 0.071 \\ 
  SRMRB & 200 & 0.732 & 0.069 & 0.701 & 0.047 & 0.723 & 0.054 \\ 
   \bottomrule
\end{tabular}
\end{table}

Detecting Misspecification at level-1

c <- filter(roc_summary, Classification == "CvM1", `Level-2 SS` != 'ALL', `Level-1 SS` == 'ALL')
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Estimator == 'MLR', c(2,4,6,9:11)],
            c[ c$Estimator == 'ULSMV', c(6,9:11)],
            c[ c$Estimator == 'WLSMV', c(6,9:11)])
kable(c1, format = 'html',digits=3, row.names = F) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '=2, 'MLR'=4, 'USLMV'=4, 'WLSMV'=4))

		MLR				USLMV				WLSMV
Index	Level-2 SS	AUC	Threshold	Specificity	Sensitivity	AUC	Threshold	Specificity	Sensitivity	AUC	Threshold	Specificity	Sensitivity
CFI	30	0.829	0.944	0.918	0.684	0.649	0.979	0.482	0.830	0.804	0.983	0.732	0.811
CFI	50	0.926	0.955	0.921	0.842	0.735	0.972	0.613	0.834	0.926	0.980	0.884	0.849
CFI	100	0.990	0.962	0.959	0.960	0.875	0.971	0.781	0.905	0.992	0.972	0.965	0.965
CFI	200	1.000	0.969	0.994	0.995	0.989	0.975	0.954	0.956	1.000	0.971	0.994	0.996
TLI	30	0.829	0.932	0.918	0.684	0.649	0.975	0.482	0.830	0.804	0.979	0.732	0.811
TLI	50	0.926	0.945	0.921	0.842	0.735	0.966	0.613	0.834	0.926	0.976	0.884	0.849
TLI	100	0.990	0.954	0.959	0.960	0.875	0.965	0.781	0.905	0.992	0.966	0.965	0.965
TLI	200	1.000	0.962	0.994	0.995	0.989	0.970	0.954	0.956	1.000	0.966	0.994	0.996
RMSEA	30	0.799	0.030	0.903	0.663	0.651	0.010	0.460	0.803	0.796	0.014	0.707	0.794
RMSEA	50	0.908	0.026	0.912	0.823	0.729	0.012	0.589	0.800	0.908	0.014	0.850	0.806
RMSEA	100	0.988	0.024	0.953	0.958	0.854	0.011	0.765	0.827	0.988	0.017	0.967	0.931
RMSEA	200	1.000	0.020	0.996	0.993	0.958	0.013	0.821	0.947	1.000	0.020	0.995	0.996
SRMRW	30	0.769	0.042	0.907	0.567	0.722	0.053	0.922	0.464	0.801	0.053	0.896	0.634
SRMRW	50	0.861	0.038	0.960	0.692	0.817	0.048	0.953	0.603	0.877	0.047	0.962	0.731
SRMRW	100	0.981	0.037	0.967	0.905	0.945	0.046	0.961	0.839	0.982	0.045	0.966	0.913
SRMRW	200	1.000	0.033	0.998	0.998	0.997	0.043	0.992	0.979	1.000	0.040	0.999	0.998
SRMRB	30	0.518	0.177	0.698	0.346	0.532	0.093	0.834	0.233	0.530	0.109	0.842	0.218
SRMRB	50	0.503	0.151	0.805	0.235	0.541	0.070	0.875	0.217	0.552	0.086	0.827	0.266
SRMRB	100	0.530	0.069	0.917	0.186	0.565	0.052	0.880	0.262	0.582	0.060	0.883	0.267
SRMRB	200	0.600	0.057	0.853	0.350	0.595	0.038	0.924	0.272	0.626	0.048	0.754	0.455

c1 <- cbind(c[ c$Estimator == 'MLR', c(2,4,6,9)],
            c[ c$Estimator == 'ULSMV', c(6,9)],
            c[ c$Estimator == 'WLSMV', c(6,9)])
print(xtable(c1, digits = 3), booktabs=T,include.rownames = F)

% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Fri Oct 18 17:47:38 2019
\begin{table}[ht]
\centering
\begin{tabular}{llrrrrrr}
  \toprule
Index & Level-2 SS & AUC & Threshold & AUC & Threshold & AUC & Threshold \\ 
  \midrule
CFI & 30 & 0.829 & 0.944 & 0.649 & 0.979 & 0.804 & 0.983 \\ 
  CFI & 50 & 0.926 & 0.955 & 0.735 & 0.972 & 0.926 & 0.980 \\ 
  CFI & 100 & 0.990 & 0.962 & 0.875 & 0.971 & 0.992 & 0.972 \\ 
  CFI & 200 & 1.000 & 0.969 & 0.989 & 0.975 & 1.000 & 0.971 \\ 
  TLI & 30 & 0.829 & 0.932 & 0.649 & 0.975 & 0.804 & 0.979 \\ 
  TLI & 50 & 0.926 & 0.945 & 0.735 & 0.966 & 0.926 & 0.976 \\ 
  TLI & 100 & 0.990 & 0.954 & 0.875 & 0.965 & 0.992 & 0.966 \\ 
  TLI & 200 & 1.000 & 0.962 & 0.989 & 0.970 & 1.000 & 0.966 \\ 
  RMSEA & 30 & 0.799 & 0.030 & 0.651 & 0.010 & 0.796 & 0.014 \\ 
  RMSEA & 50 & 0.908 & 0.026 & 0.729 & 0.012 & 0.908 & 0.014 \\ 
  RMSEA & 100 & 0.988 & 0.024 & 0.854 & 0.011 & 0.988 & 0.017 \\ 
  RMSEA & 200 & 1.000 & 0.020 & 0.958 & 0.013 & 1.000 & 0.020 \\ 
  SRMRW & 30 & 0.769 & 0.042 & 0.722 & 0.053 & 0.801 & 0.053 \\ 
  SRMRW & 50 & 0.861 & 0.038 & 0.817 & 0.048 & 0.877 & 0.047 \\ 
  SRMRW & 100 & 0.981 & 0.037 & 0.945 & 0.046 & 0.982 & 0.045 \\ 
  SRMRW & 200 & 1.000 & 0.033 & 0.997 & 0.043 & 1.000 & 0.040 \\ 
  SRMRB & 30 & 0.518 & 0.177 & 0.532 & 0.093 & 0.530 & 0.109 \\ 
  SRMRB & 50 & 0.503 & 0.151 & 0.541 & 0.070 & 0.552 & 0.086 \\ 
  SRMRB & 100 & 0.530 & 0.069 & 0.565 & 0.052 & 0.582 & 0.060 \\ 
  SRMRB & 200 & 0.600 & 0.057 & 0.595 & 0.038 & 0.626 & 0.048 \\ 
   \bottomrule
\end{tabular}
\end{table}

Detecting Misspecification at level-2

c <- filter(roc_summary, Classification == "CvM2", `Level-2 SS` != 'ALL', `Level-1 SS` == 'ALL')
# Next make the columns the estimator factor
c1 <- cbind(c[ c$Estimator == 'MLR', c(2,4,6,9:11)],
            c[ c$Estimator == 'ULSMV', c(6,9:11)],
            c[ c$Estimator == 'WLSMV', c(6,9:11)])
kable(c1, format = 'html',digits=3, row.names = F) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '=2, 'MLR'=4, 'USLMV'=4, 'WLSMV'=4))

		MLR				USLMV				WLSMV
Index	Level-2 SS	AUC	Threshold	Specificity	Sensitivity	AUC	Threshold	Specificity	Sensitivity	AUC	Threshold	Specificity	Sensitivity
CFI	30	0.574	0.972	0.638	0.494	0.570	0.999	0.409	0.720	0.541	1.000	0.441	0.635
CFI	50	0.624	0.986	0.623	0.586	0.641	0.996	0.545	0.685	0.601	0.999	0.590	0.586
CFI	100	0.704	0.991	0.595	0.728	0.729	0.970	0.447	0.906	0.699	0.996	0.603	0.708
CFI	200	0.764	0.995	0.626	0.789	0.784	0.978	0.511	0.946	0.776	0.997	0.644	0.771
TLI	30	0.574	0.967	0.638	0.494	0.570	0.999	0.409	0.720	0.541	1.000	0.441	0.635
TLI	50	0.624	0.983	0.623	0.586	0.641	0.995	0.545	0.685	0.601	0.999	0.590	0.586
TLI	100	0.704	0.989	0.595	0.728	0.729	0.964	0.447	0.906	0.699	0.995	0.603	0.708
TLI	200	0.764	0.994	0.626	0.789	0.784	0.973	0.511	0.946	0.776	0.996	0.644	0.771
RMSEA	30	0.570	0.019	0.665	0.456	0.433	-Inf	0.000	1.000	0.459	0.054	1.000	0.001
RMSEA	50	0.620	0.013	0.646	0.549	0.635	0.005	0.564	0.661	0.599	0.003	0.589	0.587
RMSEA	100	0.699	0.010	0.621	0.685	0.724	0.007	0.627	0.719	0.696	0.007	0.613	0.698
RMSEA	200	0.758	0.008	0.640	0.755	0.782	0.006	0.663	0.758	0.775	0.006	0.675	0.743
SRMRW	30	0.499	0.024	0.270	0.736	0.513	0.041	0.722	0.313	0.504	0.026	0.885	0.126
SRMRW	50	0.506	0.048	0.181	0.834	0.539	0.034	0.716	0.353	0.513	0.023	0.745	0.278
SRMRW	100	0.509	0.036	0.134	0.892	0.580	0.035	0.520	0.613	0.518	0.028	0.460	0.571
SRMRW	200	0.518	0.029	0.081	0.964	0.630	0.028	0.527	0.695	0.535	0.021	0.475	0.584
SRMRB	30	0.613	0.143	0.871	0.324	0.575	0.106	0.747	0.383	0.598	0.120	0.784	0.374
SRMRB	50	0.678	0.122	0.824	0.466	0.622	0.082	0.833	0.371	0.656	0.096	0.801	0.449
SRMRB	100	0.756	0.098	0.744	0.672	0.701	0.065	0.826	0.498	0.736	0.076	0.746	0.635
SRMRB	200	0.808	0.072	0.795	0.710	0.769	0.049	0.847	0.571	0.795	0.058	0.757	0.723

c1 <- cbind(c[ c$Estimator == 'MLR', c(2,4,6,9)],
            c[ c$Estimator == 'ULSMV', c(6,9)],
            c[ c$Estimator == 'WLSMV', c(6,9)])
print(xtable(c1, digits = 3), booktabs=T,include.rownames = F)

% latex table generated in R 3.6.0 by xtable 1.8-4 package
% Fri Oct 18 17:47:38 2019
\begin{table}[ht]
\centering
\begin{tabular}{llrrrrrr}
  \toprule
Index & Level-2 SS & AUC & Threshold & AUC & Threshold & AUC & Threshold \\ 
  \midrule
CFI & 30 & 0.574 & 0.972 & 0.570 & 0.999 & 0.541 & 1.000 \\ 
  CFI & 50 & 0.624 & 0.986 & 0.641 & 0.996 & 0.601 & 0.999 \\ 
  CFI & 100 & 0.704 & 0.991 & 0.729 & 0.970 & 0.699 & 0.996 \\ 
  CFI & 200 & 0.764 & 0.995 & 0.784 & 0.978 & 0.776 & 0.997 \\ 
  TLI & 30 & 0.574 & 0.967 & 0.570 & 0.999 & 0.541 & 1.000 \\ 
  TLI & 50 & 0.624 & 0.983 & 0.641 & 0.995 & 0.601 & 0.999 \\ 
  TLI & 100 & 0.704 & 0.989 & 0.729 & 0.964 & 0.699 & 0.995 \\ 
  TLI & 200 & 0.764 & 0.994 & 0.784 & 0.973 & 0.776 & 0.996 \\ 
  RMSEA & 30 & 0.570 & 0.019 & 0.433 & -Inf & 0.459 & 0.054 \\ 
  RMSEA & 50 & 0.620 & 0.013 & 0.635 & 0.005 & 0.599 & 0.003 \\ 
  RMSEA & 100 & 0.699 & 0.010 & 0.724 & 0.007 & 0.696 & 0.007 \\ 
  RMSEA & 200 & 0.758 & 0.008 & 0.782 & 0.006 & 0.775 & 0.006 \\ 
  SRMRW & 30 & 0.499 & 0.024 & 0.513 & 0.041 & 0.504 & 0.026 \\ 
  SRMRW & 50 & 0.506 & 0.048 & 0.539 & 0.034 & 0.513 & 0.023 \\ 
  SRMRW & 100 & 0.509 & 0.036 & 0.580 & 0.035 & 0.518 & 0.028 \\ 
  SRMRW & 200 & 0.518 & 0.029 & 0.630 & 0.028 & 0.535 & 0.021 \\ 
  SRMRB & 30 & 0.613 & 0.143 & 0.575 & 0.106 & 0.598 & 0.120 \\ 
  SRMRB & 50 & 0.678 & 0.122 & 0.622 & 0.082 & 0.656 & 0.096 \\ 
  SRMRB & 100 & 0.756 & 0.098 & 0.701 & 0.065 & 0.736 & 0.076 \\ 
  SRMRB & 200 & 0.808 & 0.072 & 0.769 & 0.049 & 0.795 & 0.058 \\ 
   \bottomrule
\end{tabular}
\end{table}

ROC Curves

First extract the data

roc_smooth_data <- as.data.frame(matrix(0,ncol=7, nrow=514*(3*4*5*5)))
colnames(roc_smooth_data) <- c('Index', 'Classification', 'Estimator','Level-2 SS', 'AUC', 'Sensitivity', 'Specificity')
i <- 1
j <- 514
for(index in INDEX){
  for(est in EST){
    for(class in CLASS){
    for(s2 in SS_L2){
    ## Set up iteration key
    key <- paste0(index,'.',class,'.',est,'.', s2,'.ALL')
      ## update extracted data
      roc_smooth_data[i:j, 1] <- index
      roc_smooth_data[i:j, 2] <- class
      roc_smooth_data[i:j, 3] <- est
      roc_smooth_data[i:j, 4] <- s2
      ## extract smooth fit object
      fit <- fit_roc_smooth[[key]]
      if(is.null(fit) == T){
        ## update sen,spec, and auc
        roc_smooth_data[i:j, 5] <- NA
        roc_smooth_data[i:j, 6] <- NA
        roc_smooth_data[i:j, 7] <- NA
      } else {
        ## update sen,spec, and auc
        roc_smooth_data[i:j, 5] <- fit$auc
        roc_smooth_data[i:j, 6] <- fit$sensitivities
        roc_smooth_data[i:j, 7] <- fit$specificities
      }
      
      ## update iterators
      i <- i + 514
      j <- j + 514
    }
  }
}}
## Forcing factor orders
roc_smooth_data$Index <- factor(
  roc_smooth_data$Index, ordered = T,
  levels=c('CFI', 'TLI', 'RMSEA', 'SRMRW', 'SRMRB'))
roc_smooth_data$Classification <- factor(
  roc_smooth_data$Classification,
  levels=c('C','CvM1','CvM2'),
  labels=c('Any Mis.', 'Level-1 Mis.', 'Level-2 Mis.'),
  ordered = T
)
roc_smooth_data$Estimator <- as.factor(roc_smooth_data$Estimator)
roc_smooth_data$`Level-2 SS` <- factor(roc_smooth_data$`Level-2 SS`,
                                       levels=c('ALL','30','50', '100', '200'),
                                       ordered = T)

Plot by Misspecification and Estimation Method

subdata <- filter(roc_smooth_data, `Level-2 SS`=='ALL', Estimator!='ALL')
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(Estimator~Classification) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index")) +
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_plot_mis_est.pdf', plot = p, height = 6,width = 9,units = 'in')

Plot by Misspecification and Level-2 Sample Size

subdata <- filter(roc_smooth_data, `Level-2 SS`!='ALL', Estimator=='ALL')
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(`Level-2 SS` ~Classification) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_plot_mis_n2.pdf', plot = p, height = 6,width = 9, units = 'in')

Figures of Subconditions and Smaller Plots for Exporting

Figures by Classification Outcome

subdata <- filter(roc_smooth_data, `Level-2 SS`=='ALL',  Estimator == "ALL")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~Classification) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_class_all.pdf', plot = p, height = 4, width = 9, units = 'in')
subdata <- filter(roc_smooth_data, `Level-2 SS`=='ALL', Estimator == "MLR")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~Classification) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_class_mlr.pdf', plot = p, height = 4,width = 9,units = 'in')
subdata <- filter(roc_smooth_data, `Level-2 SS`=='ALL', Estimator == "ULSMV")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~Classification) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_class_ulsmv.pdf', plot = p, height = 4,width = 9,units = 'in')
subdata <- filter(roc_smooth_data,`Level-2 SS`=='ALL',  Estimator == "WLSMV")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~Classification) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_class_wlsmv.pdf', plot = p, height = 4,width = 9,units = 'in')

Figures by Estimation Method

subdata <- filter(roc_smooth_data, `Level-2 SS`=='ALL', Estimator!='ALL',
                  Classification == "Any Mis.")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~Estimator) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_est_c.pdf', plot = p, height = 4,width = 9,units = 'in')
subdata <- filter(roc_smooth_data, `Level-2 SS`=='ALL', Estimator!='ALL',
                  Classification == "Level-1 Mis.")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~Estimator) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_est_cl1.pdf', plot = p, height = 4,width = 9,units = 'in')
subdata <- filter(roc_smooth_data,`Level-2 SS`=='ALL', Estimator!='ALL',
                  Classification == "Level-2 Mis.")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~Estimator) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_est_cl2.pdf', plot = p, height = 4,width = 9,units = 'in')

Figures by Level-2 Sample Size

subdata <- filter(roc_smooth_data, Estimator=='ALL', Classification == "Any Mis.",
                  `Level-2 SS`!='ALL')
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~`Level-2 SS`) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray')
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_n2_c.pdf', plot = p, height = 4,width = 9,units = 'in')
subdata <- filter(roc_smooth_data, `Level-2 SS`!='ALL', Estimator=='ALL',
                  Classification == "Level-1 Mis.")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~`Level-2 SS`) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_n2_cl1.pdf', plot = p, height = 4,width = 9,units = 'in')
subdata <- filter(roc_smooth_data,`Level-2 SS`!='ALL', Estimator=='ALL',  Classification == "Level-2 Mis.")
p <- ggplot(subdata, aes(x = Specificity, y=Sensitivity, group = Index)) +
  geom_line(aes(linetype=Index, color=Index))+
  facet_grid(.~`Level-2 SS`) +
  scale_x_reverse() +
  scale_color_brewer(palette="Set1") +
  guides(color=guide_legend(title="Fit Index"),
         linetype=guide_legend(title="Fit Index"))+
  geom_abline(intercept = 1, slope = 1, color='dimgray' )
p

Version	Author	Date
ba44658	noah-padgett	2019-09-29
982c8f1	noah-padgett	2019-05-18

if(save.fig == T) ggsave('roc_n2_cl2.pdf', plot = p, height = 4,width = 9,units = 'in')

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

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] pROC_1.15.0      xtable_1.8-4     kableExtra_1.1.0 psych_1.8.12    
 [5] car_3.0-3        carData_3.0-2    forcats_0.4.0    stringr_1.4.0   
 [9] dplyr_0.8.1      purrr_0.3.2      readr_1.3.1      tidyr_0.8.3     
[13] tibble_2.1.1     ggplot2_3.2.0    tidyverse_1.2.1 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1         lubridate_1.7.4    lattice_0.20-38   
 [4] assertthat_0.2.1   rprojroot_1.3-2    digest_0.6.19     
 [7] R6_2.4.0           cellranger_1.1.0   plyr_1.8.4        
[10] backports_1.1.4    evaluate_0.14      highr_0.8         
[13] httr_1.4.0         pillar_1.4.1       rlang_0.3.4       
[16] lazyeval_0.2.2     curl_3.3           readxl_1.3.1      
[19] rstudioapi_0.10    data.table_1.12.2  whisker_0.3-2     
[22] rmarkdown_1.13     labeling_0.3       webshot_0.5.1     
[25] foreign_0.8-71     munsell_0.5.0      broom_0.5.2       
[28] compiler_3.6.0     modelr_0.1.4       xfun_0.7          
[31] pkgconfig_2.0.2    mnormt_1.5-5       htmltools_0.3.6   
[34] tidyselect_0.2.5   workflowr_1.4.0    rio_0.5.16        
[37] viridisLite_0.3.0  crayon_1.3.4       withr_2.1.2       
[40] grid_3.6.0         nlme_3.1-139       jsonlite_1.6      
[43] gtable_0.3.0       git2r_0.26.1       magrittr_1.5      
[46] scales_1.0.0       zip_2.0.2          cli_1.1.0         
[49] stringi_1.4.3      reshape2_1.4.3     fs_1.3.1          
[52] xml2_1.2.0         generics_0.0.2     openxlsx_4.1.0    
[55] RColorBrewer_1.1-2 tools_3.6.0        glue_1.3.1        
[58] hms_0.4.2          abind_1.4-5        parallel_3.6.0    
[61] yaml_2.2.0         colorspace_1.4-1   rvest_0.3.4       
[64] knitr_1.23         haven_2.1.0

ROC Analyses

2019-05-09

Packages and Set-Up

Data Management

Adding Labels to Conditions

ROC Analysis Labels

Introduction to ROC Analysis

ROC Analyses

Detecting any type of misspecification

General overview over all conditions

More fine grained information within/across conditions

Detecting Misspecification at Level-1

General overview over all conditions

More fine grained information within/across conditions

Detecting Misspecification at Level-2

General overview over all conditions

More fine grained information within/across conditions

Summarizing the Results

Detecting Any Misspecification

Detecting Misspecification at level-1

Detecting Misspecification at level-2

ROC Curves

First extract the data

Plot by Misspecification and Estimation Method

Plot by Misspecification and Level-2 Sample Size

Figures of Subconditions and Smaller Plots for Exporting

Figures by Classification Outcome

Figures by Estimation Method

Figures by Level-2 Sample Size