Last updated: 2019-05-07
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Knit directory: mcfa-fit/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 569bace | noah-padgett | 2019-05-07 | getting files to show |
Purpose of this file:
The output is numerous .eps files (figures) and code for tables in latex
## Chunk iptions
knitr::opts_chunk$set(out.width = "225%")
# setwd('C:/Users/noahp/Dropbox/MCFA Thesis/Code Results')
## Packages General Packages
library(tidyverse)-- Attaching packages ---------------------------------------------- tidyverse 1.2.1 --v ggplot2 3.1.0 v purrr 0.2.5
v tibble 2.0.1 v dplyr 0.8.0.1
v tidyr 0.8.2 v stringr 1.3.1
v readr 1.3.1 v forcats 0.3.0 Warning: package 'dplyr' was built under R version 3.5.3-- Conflicts ------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()# Formatting and Tables
library(kableExtra)
Attaching package: 'kableExtra'The following object is masked from 'package:dplyr':
group_rowslibrary(xtable)
# For plotting
library(ggplot2)
theme_set(theme_bw())
# Data manipulating
library(dplyr)
# ROC Analysis
library(pROC)Type 'citation("pROC")' for a citation.
Attaching package: 'pROC'The following objects are masked from 'package:stats':
cov, smooth, var## One global parameter for printing figures
save.fig <- Fsim_results <- as_tibble(read.table("data/compiled_fit_results.txt", header = T,
sep = "\t"))
## Next, turn condition into a factor for plotting
sim_results$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$chisqu_pvalue < 0.05,
0, 1)
# 0 = rejected that these data fit this model 1 = failed to reject that
# these data fit this modelCurrently, each condition is kind of like a hidden id that we don’t know what the actual factor is. So, first thing isto create meaningful labels for us to use. Remember, the 72 conditions for the this study were
## level-1 Sample size
ss_l1 <- c(5, 10, 30) ## 6 conditions each
ss_l2 <- c(30, 50, 100, 200) ## 18 condition each
icc_ov <- c(.1, .3, .5) ## 2 conditions each
icc_lv <- c(.1, .5) ## every other condition
nCon <- 72 # number of conditions
nRep <- 500 # number of replications per condition
nMod <- 12 ## numberof estimated models per conditions
## Total number of rows: 432,000
ss_l2 <- c(rep(ss_l2[1], 18*nRep*nMod), rep(ss_l2[2], 18*nRep*nMod),
rep(ss_l2[3], 18*nRep*nMod), rep(ss_l2[4], 18*nRep*nMod))
ss_l1 <- rep(c(rep(ss_l1[1],6*nRep*nMod), rep(ss_l1[2],6*nRep*nMod), rep(ss_l1[3],6*nRep*nMod)), 4)
icc_ov <- rep(c(rep(icc_ov[1], 2*nRep*nMod), rep(icc_ov[2], 2*nRep*nMod), rep(icc_ov[3], 2*nRep*nMod)), 12)
icc_lv <- rep(c(rep(icc_lv[1], nRep*nMod), rep(icc_lv[2], nRep*nMod)), 36)
## Force these vectors to be column vectors
ss_l1 <- matrix(ss_l1, ncol=1)
ss_l2 <- matrix(ss_l2, ncol=1)
icc_ov <- matrix(icc_ov, ncol=1)
icc_lv <- matrix(icc_lv, ncol=1)
## Add the labels to the results data frame
sim_results <- sim_results[order(sim_results$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')For the descriptive statistics, I will use dplyr. From here I can easily create matrices that store the results so that I can easily print out the results for summarizing the results. Each will be printed out as a html table and a xtable (latex ready) table.
Convergence will be broken out by Model (C, M1, M2, M12) and estimator (MLR, WLSMV, ULSMV). So, there will 12 tables piecemail tables and then one larger table.
## first table summary table
c <- sim_results %>% group_by(Model, Estimator) %>% summarise(Converge = mean(Converge))
# Next make the columns the estimator factor
c <- cbind(c[c$Estimator == "MLR", "Converge"], c[c$Estimator == "ULSMV", "Converge"],
c[c$Estimator == "WLSMV", "Converge"])
colnames(c) <- c("MLR", "ULSMV", "WLSMV")
rownames(c) <- c("C", "M1", "M2", "M12")
## Print results in a nice looking table in HTML
kable(c, format = "html") %>% kable_styling(full_width = T)| MLR | ULSMV | WLSMV | |
|---|---|---|---|
| C | 0.9998056 | 0.9989722 | 0.9997778 |
| M1 | 0.9819444 | 0.9737500 | 0.9645000 |
| M2 | 0.9998056 | 0.9882500 | 0.9997500 |
| M12 | 0.9997778 | 0.9853056 | 0.9919444 |
## Print out in tex
print(xtable(c, digits = 3), booktabs = T, include.rownames = T)% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Tue May 07 18:12:14 2019
\begin{table}[ht]
\centering
\begin{tabular}{rrrr}
\toprule
& MLR & ULSMV & WLSMV \\
\midrule
C & 1.000 & 0.999 & 1.000 \\
M1 & 0.982 & 0.974 & 0.965 \\
M2 & 1.000 & 0.988 & 1.000 \\
M12 & 1.000 & 0.985 & 0.992 \\
\bottomrule
\end{tabular}
\end{table}# ## Next, start the more descriptive tables across conditions ## loop
# around these iterators for(M in mods){ for(E in ests){ ### subset tothe
# model (M) and estimator (E) #M <- 'C' #E <- 'MLR' cat('\n\n
# ===============================\n') cat('\nModel:\t', M)
# cat('\nEstimator:\t', E, '\n') sub_dat <- sim_results[
# sim_results$Model == M & sim_results$Estimator == E,] c <- sub_dat %>%
# group_by(ss_l1, ss_l2, icc_ov, icc_lv) %>% summarize(C = mean(Converge))
# ## Now, print out the unformatted table kable(c, format='html') %>%
# kable_styling(full_width = T) print(c) ## now, make the columns the ICC
# conditions c <- cbind( c[ c$icc_ov == 0.1 & c$icc_lv == .1,
# c('ss_l1','ss_l2','C')], c[ c$icc_ov == 0.3 & c$icc_lv == .1, 'C'], c[
# c$icc_ov == 0.5 & c$icc_lv == .1, 'C'], c[ c$icc_ov == 0.1 & c$icc_lv ==
# .5, 'C'], c[ c$icc_ov == 0.3 & c$icc_lv == .5, 'C'], c[ c$icc_ov == 0.5 &
# c$icc_lv == .5, 'C'] ) ## End cbind() colnames(c) <- c('ss_l1','ss_l2',
# '.1' ,'.3', '.5', '.1' ,'.3', '.5') ## Print results in a nice looking
# table kable(c, format='html') %>% kable_styling(full_width = T) %>%
# add_header_above(c(' ' = 2, 'LV ICC = .10' = 3, 'LV ICC = .50'=3))
# print(xtable(c, digits = 3), booktabs = T, include.rownames = F) } } ##
# For a longtable instead c <-sim_results %>% group_by(Model, Estimator,
# ss_l1, ss_l2, icc_ov, icc_lv) %>% summarize(C = mean(Converge)) ## Now,
# print out the unformatted table kable(c, format='html') %>%
# kable_styling(full_width = T) ## now, make the columns the ICC conditions
# c <- cbind( c[ c$icc_ov == 0.1 & c$icc_lv == .1, c('Model', 'Estimator',
# 'ss_l1','ss_l2','C')], c[ c$icc_ov == 0.3 & c$icc_lv == .1, 'C'], c[
# c$icc_ov == 0.5 & c$icc_lv == .1, 'C'], c[ c$icc_ov == 0.1 & c$icc_lv ==
# .5, 'C'], c[ c$icc_ov == 0.3 & c$icc_lv == .5, 'C'], c[ c$icc_ov == 0.5 &
# c$icc_lv == .5, 'C'] ) ## End cbind() colnames(c) <- c('Model',
# 'Estimator', 'ss_l1','ss_l2', '.1' ,'.3', '.5', '.1' ,'.3', '.5') ## Print
# results in a nice looking table kable(c, format='html') %>%
# kable_styling(full_width = T) %>% add_header_above(c(' ' = 4, 'LV ICC =
# .10' = 3, 'LV ICC = .50'=3)) print(xtable(c, digits = 3), booktabs = T,
# include.rownames = F)
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] pROC_1.13.0 xtable_1.8-3 kableExtra_1.0.1 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.2.5 readr_1.3.1
[9] tidyr_0.8.2 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 haven_2.0.0
[4] lattice_0.20-38 colorspace_1.4-0 generics_0.0.2
[7] htmltools_0.3.6 viridisLite_0.3.0 yaml_2.2.0
[10] rlang_0.3.1 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.2.0
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] workflowr_1.3.0 cellranger_1.1.0 rvest_0.3.2
[22] evaluate_0.12 knitr_1.21 highr_0.7
[25] broom_0.5.1 Rcpp_1.0.0 scales_1.0.0
[28] backports_1.1.3 formatR_1.5 webshot_0.5.1
[31] jsonlite_1.6 fs_1.2.6 hms_0.4.2
[34] digest_0.6.18 stringi_1.2.4 grid_3.5.2
[37] rprojroot_1.3-2 cli_1.0.1 tools_3.5.2
[40] magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[43] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[46] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.11
[49] httr_1.4.0 rstudioapi_0.9.0 R6_2.3.0
[52] nlme_3.1-137 git2r_0.24.0 compiler_3.5.2