Last updated: 2019-05-08

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Rmd	b794176	noah-padgett	2019-05-08	updated tables and figures

Purpose of this file:

Create simple boxplots for trends
Creating publication boxplots

Packages and Set-Up

## Chunk iptions
knitr::opts_chunk$set(out.width = "225%")

## Packages General Packages
library(tidyverse)

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

v ggplot2 3.1.0       v purrr   0.2.5  
v tibble  2.0.1       v dplyr   0.8.0.1
v tidyr   0.8.2       v stringr 1.3.1  
v readr   1.3.1       v forcats 0.3.0

Warning: package 'dplyr' was built under R version 3.5.3

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

# 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())
# Data manipulating
library(dplyr)

## One global parameter for printing figures
save.fig <- F

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, 
    0, 1)
# 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)
## Set up iterators for remainder of script
mods <- c('C', 'M1', 'M2', 'M12')
ests <- c('MLR', 'ULSMV', 'WLSMV')

Boxplots

One of the best ways to visualize the results of the distribution of fit statistics across conditions is to use boxplots. In the section, we created three sets of boxplots. First, the boxplots are large and give a very course overview of the results across conditions, but can be difficult to make sense of because of how large they are. Secondly, the boxplots are made with facets by model and estimator from the larger set of data so that more concise plots can be made for easier viewing. Third, subsets based on Model and estimator are made to create a “smaller” plot that is easier to handle. Most of these latter plots are included in my thesis document.

For all graphs, we haved placed a dashed line where the commonly used cut-off for these fit statistics lies.

Hu and Bentler (1999) commonly reported cut-off criteria:

CFI > .95
TLI > .95
RMSEA < .06
SRMR < .08
- Note: For SRMR, there are two versions SRMR-Within and SRMR-Between. The same commonly use cut-off is applied but is likely inappropriate for both.

First, filter the results to the converged and admissible replications/

mydata <- filter(sim_results, Converge == 1 & Admissible == 1)

Large General Boxplots

For these larger Boxplots, there are two sets.

Coarse distributions of each fit statistic
Marginal Plots of Condition (ss_l1, ss_l2, icc_ov, icc_lv)

For outliers, we used alpha shading to limit the opacity. This was done to help show the relative differences in density of outliers across different conditions. This was recommended by Dr. Mandy Herring from the Baylor University Statistics Department during Noah Padgett’s thesis defense. We set the alpha shading to 0.10 (i.e., 10% opacity).

Coarse Boxplots

## Set 1 Coarse distributions of each fit statistic
p <- ggplot(mydata, aes(x=Condition, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("cfi_all_conditions.pdf",plot = p, width=12, height=6, units='in')

p <- ggplot(mydata, aes(x=Condition, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_all_conditions.pdf",plot = p, width=12, height=6, units='in')

p <- ggplot(mydata, aes(x=Condition, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_all_conditions.pdf",plot = p, width=12, height=6, units='in')

p <- ggplot(mydata, aes(x=Condition, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("srmrw_all_conditions.pdf",plot = p, width=12, height=6, units='in')

p <- ggplot(mydata, aes(x=Condition, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_all_conditions.pdf",plot = p, width=12, height=6, units='in')

## SRMRB without range restriction
p <- ggplot(mydata, aes(x=Condition, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

Marginal Plots of Conditions

CFI

p <- ggplot(mydata, aes(x=Model, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("cfi_marginal_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=Estimator, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("cfi_marginal_estimator.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=ss_l1, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("cfi_marginal_ssl1.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("cfi_marginal_ss_l2.pdf",plot = p,width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_ov, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    labs(title = 'CFI by Obs. Var. ICC')
p

if(save.fig == T) ggsave("cfi_marginal_icc_ov.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("cfi_marginal_icc_lv.pdf",plot = p, width=6, height=4, units='in')

TLI

p <- ggplot(mydata, aes(x=Model, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_marginal_model.pdf",plot = p, width=6, height=4, units='in')
p <- ggplot(mydata, aes(x=Estimator, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_marginal_estimator.pdf",plot = p,width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=ss_l1, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_marginal_ss_l1.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_marginal_ss_l2.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_marginal_icc_ov.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_lv, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_marginal_icc_lv.pdf",plot = p, width=6, height=4, units='in')

RMSEA

p <- ggplot(mydata, aes(x=Model, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_marginal_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=Estimator, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_marginal_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l1, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l1.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l2.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_marginal_icc_ov.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_marginal_icc_lv.pdf",plot = p, width=6, height=4, units='in')

SRMRW

p <- ggplot(mydata, aes(x=Model, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("srmrw_marginal_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=Estimator, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("srmrw_marginal_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l1, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l1.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l2.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    labs(title = 'SRMRW by Obs. Var. ICC')
p

if(save.fig == T) ggsave("srmrw_marginal_icc_ov.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("srmrw_marginal_icc_lv.pdf",plot = p, width=6, height=4, units='in')

SRMRB

p <- ggplot(mydata, aes(x=Model, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=Estimator, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l1, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l1.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    labs(title = 'SRMRB by Level-2 Sample Size') +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l2.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_icc_ov.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_lv, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_icc_lv.pdf",plot = p, width=6, height=4, units='in')

Facetted Boxplots

For the facetted boxplots, these will also be created in multiple sets.

Full Results by conditions faetted by Model and estimator
Marginal Plots of Condition (ss_l1, ss_l2, icc_ov, icc_lv) Facetted by Est.
Marginal Plots of Condition (ss_l1, ss_l2, icc_ov, icc_lv) Facetted by Model
Marginal Plots of Condition (ss_l1, ss_l2, icc_ov, icc_lv) Facetted by Est. and Model

So, that is a lot of plots and onyl a select few will make it into the final document and publication manuscript.

Faceted Coarse Boxplots

## Set 1: Large boxplot figures faceted by Model and Estimator
p <- ggplot(mydata, aes(x=Condition, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("cfi_all_condition_facet.pdf",plot = p, width=16, height=12, units='in')

p <- ggplot(mydata, aes(x=Condition, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("tli_all_condition_facet.pdf",plot = p, width=16, height=12, units='in')

p <- ggplot(mydata, aes(x=Condition, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("rmsea_all_condition_facet.pdf",plot = p, width=16, height=12, units='in')


p <- ggplot(mydata, aes(x=Condition, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("srmrw_all_condition_facet.pdf",plot = p, width=16, height=12, units='in')

p <- ggplot(mydata, aes(x=Condition, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator) +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_all_condition_facet.pdf",plot = p, width=16, height=12, units='in')

Marginal Plots of Conditions Facetted by Estimator

CFI

p <- ggplot(mydata, aes(x=ss_l1, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("cfi_marginal_ssl1_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)+
    labs(title = 'CFI by Level-2 Sample Size')
p

if(save.fig == T) ggsave("cfi_marginal_ss_l2_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_ov, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("cfi_marginal_icc_ov_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("cfi_marginal_icc_lv_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

TLI

p <- ggplot(mydata, aes(x=ss_l1, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("tli_marginal_ss_l1_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("tli_marginal_ss_l2_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("tli_marginal_icc_ov_facet_estimator.pdf",plot = p, scale=1)

p <- ggplot(mydata, aes(x=icc_lv, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("tli_marginal_icc_lv_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

RMSEA

p <- ggplot(mydata, aes(x=ss_l1, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l1_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l2_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("rmsea_marginal_icc_ov_facet_estimator.pdf",plot = p,width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("rmsea_marginal_icc_lv_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

SRMRW

p <- ggplot(mydata, aes(x=ss_l1, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l1_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l2_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("srmrw_marginal_icc_ov_facet_estimator.pdf",plot = p,width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)
p

if(save.fig == T) ggsave("srmrw_marginal_icc_lv_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

SRMRB

p <- ggplot(mydata, aes(x=ss_l1, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator) +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l1_facet_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator) +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l2_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_icc_ov_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_lv, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Estimator)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_icc_lv_facet_estimator.pdf",plot = p, width=6, height=4, units='in')

Marginal Plots of Conditions Facetted by Model

CFI

p <- ggplot(mydata, aes(x=ss_l1, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("cfi_marginal_ssl1_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("cfi_marginal_ss_l2_facet_model.pdf",plot = p,width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_ov, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("cfi_marginal_icc_ov_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("cfi_marginal_icc_lv_facet_model.pdf",plot = p, width=6, height=4, units='in')

TLI

p <- ggplot(mydata, aes(x=ss_l1, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("tli_marginal_ss_l1_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)+
    labs(title = 'TLI by Level-2 Sample Size')
p

if(save.fig == T) ggsave("tli_marginal_ss_l2_facet_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("tli_marginal_icc_ov_facet_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_lv, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("tli_marginal_icc_lv_facet_model.pdf",plot = p, width=6, height=4, units='in')

RMSEA

p <- ggplot(mydata, aes(x=ss_l1, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l1_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l2_facet_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("rmsea_marginal_icc_ov_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("rmsea_marginal_icc_lv_facet_model.pdf",plot = p, width=6, height=4, units='in')

SRMRW

p <- ggplot(mydata, aes(x=ss_l1, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l1_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l2_facet_model.pdf",plot = p, scale=1)


p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("srmrw_marginal_icc_ov_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=icc_lv, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)
p

if(save.fig == T) ggsave("srmrw_marginal_icc_lv_facet_model.pdf",plot = p, width=6, height=4, units='in')

SRMRB

p <- ggplot(mydata, aes(x=ss_l1, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l1_facet_model.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l2_facet_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_icc_ov_facet_model.pdf",plot = p, width=6, height=4, units='in')

p <- ggplot(mydata, aes(x=icc_lv, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(.~Model)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_icc_lv_facet_model.pdf",plot = p, width=6, height=4, units='in')

Marginal Plots of Conditions Facetted by Estimator and Model

CFI

p <- ggplot(mydata, aes(x=ss_l1, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("cfi_marginal_ssl1_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("cfi_marginal_ss_l2_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')


p <- ggplot(mydata, aes(x=icc_ov, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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


p <- ggplot(mydata, aes(x=icc_lv, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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

TLI

p <- ggplot(mydata, aes(x=ss_l1, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("tli_marginal_ss_l1_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("tli_marginal_ss_l2_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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

p <- ggplot(mydata, aes(x=icc_lv, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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

RMSEA

p <- ggplot(mydata, aes(x=ss_l1, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l1_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')


p <- ggplot(mydata, aes(x=ss_l2, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("rmsea_marginal_ss_l2_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')

p <- ggplot(mydata, aes(x=icc_ov, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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


p <- ggplot(mydata, aes(x=icc_lv, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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

SRMRW

p <- ggplot(mydata, aes(x=ss_l1, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l1_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

if(save.fig == T) ggsave("srmrw_marginal_ss_l2_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')


p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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


p <- ggplot(mydata, aes(x=icc_lv, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)
p

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

SRMRB

p <- ggplot(mydata, aes(x=ss_l1, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l1_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')


p <- ggplot(mydata, aes(x=ss_l2,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)+
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_ss_l2_facet_estimator_model.pdf",plot = p, width=9, height=6, units='in')

p <- ggplot(mydata, aes(x=icc_ov,  y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)+
    labs(title = 'SRMRB by Obs. Var. ICC')+
    lims(y=c(0,1))
p

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

p <- ggplot(mydata, aes(x=icc_lv, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red') +
    facet_grid(Model~Estimator)+
    lims(y=c(0,1))
p

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

Fit distribution by Model and Estimator only

p <- ggplot(mydata, aes(x=Model, y=CFI))+
    geom_boxplot(outlier.alpha = .1)+
    facet_grid(.~Estimator) +
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("cfi_marginal_model_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=Model, y=TLI))+
    geom_boxplot(outlier.alpha = .1)+
    facet_grid(.~Estimator) +
    geom_abline(intercept = .95, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("tli_marginal_model_estimator.pdf",plot = p, width=6, height=4, units='in')



p <- ggplot(mydata, aes(x=Model, y=RMSEA))+
    geom_boxplot(outlier.alpha = .1)+
    facet_grid(.~Estimator) +
    geom_abline(intercept = .06, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("rmsea_marginal_model_estimator.pdf",plot = p, width=6, height=4, units='in')



p <- ggplot(mydata, aes(x=Model, y=SRMRW))+
    geom_boxplot(outlier.alpha = .1)+
    facet_grid(.~Estimator) +
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')
p

if(save.fig == T) ggsave("srmrw_marginal_model_estimator.pdf",plot = p, width=6, height=4, units='in')


p <- ggplot(mydata, aes(x=Model, y=SRMRB))+
    geom_boxplot(outlier.alpha = .1)+
    geom_abline(intercept = .08, slope=0,
                linetype='dashed', color = 'red')+
    facet_grid(.~Estimator) +
    lims(y=c(0,1))
p

if(save.fig == T) ggsave("srmrb_marginal_model_estimator.pdf",plot = p, width=6, height=4, units='in')

interaction between Estimator and number of groups

p <- ggplot(mydata, aes(x=ss_l2, y=RMSEA))+
    geom_boxplot()+
    facet_grid(.~Estimator)
p

Warning: Removed 111 rows containing non-finite values (stat_boxplot).

with(mydata, {
  interaction.plot(ss_l2, Estimator, RMSEA,
                   fun = function(x) mean(x, na.rm = TRUE),
                   main = "RMSEA Interaction Plot")
})

Interaction between Estimator and Latent ICC for SRMRW

p <- ggplot(mydata, aes(x=Estimator, y=SRMRW))+
    geom_boxplot()+
    facet_grid(.~icc_lv)
p

Warning: Removed 111 rows containing non-finite values (stat_boxplot).

with(mydata, {
  interaction.plot(icc_lv, Estimator, SRMRW,
                   fun = function(x) mean(x, na.rm = TRUE),
                   main = "SRMRW Interaction Plot", ylab="SRMRW")
})

sessionInfo()

R version 3.5.2 (2018-12-20)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] xtable_1.8-3     kableExtra_1.0.1 forcats_0.3.0    stringr_1.3.1   
 [5] dplyr_0.8.0.1    purrr_0.2.5      readr_1.3.1      tidyr_0.8.2     
 [9] tibble_2.0.1     ggplot2_3.1.0    tidyverse_1.2.1 

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5  xfun_0.4          reshape2_1.4.3   
 [4] haven_2.0.0       lattice_0.20-38   colorspace_1.4-0 
 [7] generics_0.0.2    htmltools_0.3.6   viridisLite_0.3.0
[10] yaml_2.2.0        rlang_0.3.1       pillar_1.3.1     
[13] glue_1.3.0        withr_2.1.2       modelr_0.1.2     
[16] readxl_1.2.0      plyr_1.8.4        munsell_0.5.0    
[19] gtable_0.2.0      workflowr_1.3.0   cellranger_1.1.0 
[22] rvest_0.3.2       evaluate_0.12     labeling_0.3     
[25] knitr_1.21        broom_0.5.1       Rcpp_1.0.0       
[28] scales_1.0.0      backports_1.1.3   formatR_1.5      
[31] webshot_0.5.1     jsonlite_1.6      fs_1.2.6         
[34] hms_0.4.2         digest_0.6.18     stringi_1.2.4    
[37] grid_3.5.2        rprojroot_1.3-2   cli_1.0.1        
[40] tools_3.5.2       magrittr_1.5      lazyeval_0.2.1   
[43] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.2  
[46] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.0 
[49] rmarkdown_1.11    httr_1.4.0        rstudioapi_0.9.0 
[52] R6_2.3.0          nlme_3.1-137      git2r_0.24.0     
[55] compiler_3.5.2

Fit Statistic Boxplots

2019-05-08

Packages and Set-Up

Data Management

Adding Labels to Conditions

Boxplots

Large General Boxplots

Coarse Boxplots

Marginal Plots of Conditions

CFI

TLI

RMSEA

SRMRW

SRMRB

Facetted Boxplots

Faceted Coarse Boxplots

Marginal Plots of Conditions Facetted by Estimator

CFI

TLI

RMSEA

SRMRW

SRMRB

Marginal Plots of Conditions Facetted by Model

CFI

TLI

RMSEA

SRMRW

SRMRB

Marginal Plots of Conditions Facetted by Estimator and Model

CFI

TLI

RMSEA

SRMRW

SRMRB

Fit distribution by Model and Estimator only

interaction between Estimator and number of groups

Interaction between Estimator and Latent ICC for SRMRW