## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# install.packages("MplusAutomation")
# 

## -----------------------------------------------------------------------------

library(MplusAutomation)


## -----------------------------------------------------------------------------

sessionInfo()


## ----eval=FALSE---------------------------------------------------------------
# 
# update.packages(ask=FALSE, checkBuilt=TRUE)
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# prepareMplusData(
#   my_data,
#   filename = "C:/Data_Analysis/Prepare Mplus.dat",
#   keepCols=c("id", "item1", "item3", "item6"))
# 
# prepareMplusData(
#   my_other_data,
#   filename = "C:/Data_Analysis/Prepare Dropped Mplus.dat",
#   dropCols=c("baditem1", "baditem2", "baditem7"))
# 

## -----------------------------------------------------------------------------
data(mtcars)
mtcars$gear <- factor(mtcars$gear)
prepareMplusData(mtcars, "mtcars.dat", dummyCode = c("cyl", "am"))

## ----eval=FALSE, echo = TRUE--------------------------------------------------
# 
# runModels("C:/Program Files/Mplus/Mplus Examples/Addendum Examples")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# runModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive=TRUE)
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# runModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive=TRUE,
#   logFile="C:/CFALCA-Comparison-Log.txt")|
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# runModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive=TRUE,
#   logFile=NULL)
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# runModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive=TRUE,
#   replaceOutfile="never")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# runModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive=TRUE,
#   replaceOutfile="modifiedDate")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# runModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive=TRUE,
#   showOutput=TRUE)
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# runModels_Interactive()
# 

## ----echo = FALSE, out.width = "80%", fig.pos="h", fig.cap = "Figure. Example of using runModels() in an interactive graphical interface."----

knitr::include_graphics("runModels_Interactive-Screenshot.png")


## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# allOutput <- readModels(
#   "C:/Data_Files/CFANesting",
#   recursive=TRUE)
# 
# ## assuming there are multiple files in this directory
# ## just model summaries could retained as a data.frame as follows:
# 
# library(plyr)
# justSummaries <- do.call("rbind.fill",
#   sapply(allOutput,"[", "summaries"))
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# mlist <- readModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive = TRUE,
#   what = "summaries")
# mySummaries <- do.call("rbind.fill", sapply(mlist, "[", "summaries"))
# 

## ----eval=FALSE, echo=FALSE---------------------------------------------------
# 
# mlist <- readModels(
#   "C:/Program Files/Mplus/Mplus Examples/User's Guide Examples/Outputs",
#   filefilter = "ex4.*",
#   what = "summaries")
# summaryStats <- do.call("rbind.fill", sapply(mlist, "[", "summaries"))
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# mlist <- readModels(
#   "C:/Data_Analysis/Multiclass Models",
#   filefilter = "[123]{1}-class.*Threshold.*",
#   what = "summaries")
# summaryStats <- do.call("rbind.fill", sapply(mlist, "[", "summaries"))
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# showSummaryTable(
#   summaryStats,
#   keepCols = c("Title", "LL", "AIC", "BIC", "CFI"),
#   sortBy = "AIC")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# showSummaryTable(
#   summaryStats,
#   dropCols = c("InputInstructions", "Observations", "Parameters"),
#   sortBy = "CFI")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# HTMLSummaryTable(
#   summaryStats,
#   filename = "C:/MyModelSummary.html",
#   display = TRUE,
#   keepCols = c("Title", "LL", "AIC", "BIC", "AICC"),
#   sortBy = "AIC")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# myLatexTable <- LatexSummaryTable(
#   summaryStats,
#   keepCols = c("Title", "BIC", "Parameters"),
#   sortBy = "Parameters",
#   caption = "Comparing CFA vs. LCA according to number of parameters",
#   label="CFALCATab")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# my_model <- readModels("~/Mplus_Test.out")
# my_model$mod_indices
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# modelResults <- readModels(
#   "C:/Data_Analysis/Mplus Output.out",
#   what = "parameters")$parameters
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# unstandardizedResults <- modelResults$unstandardized
# 
# #equivalently
# standardizedResults <- modelResults[["stdyx.standardized"]]
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# allModelParameters <- readModels(
#   "C:/Data_Analysis/ComparingLCAvCFA",
#   recursive = TRUE,
#   what = "parameters")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# names(allModelParameters)
# 
# ## ComparingLCAvCFA.LCA.1.class.LCA.out
# ## ComparingLCAvCFA.LCA.2.class.LCA.out
# ## ComparingLCAvCFA.LCA.3.class.LCA.out
# ## ComparingLCAvCFA.CFA.1.factor.CFA.out
# ## ComparingLCAvCFA.CFA.2.factor.CFA.out
# ## ComparingLCAvCFA.CFA.3.factor.CFA.out
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# TwoFacCFA.STDYX <- allModelParameters$ComparingLCAvCFA.CFA.2.factor.CFA.out$parameters$stdyx.standardized
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# unstandardizedOnly <- sapply(allModelParameters, "[", c("parameters"))
# unstandardizedOnly <- sapply(unstandardizedOnly, "[", "unstandardized")
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# oldNames <- names(allModelParameters)
# unstandardizedOnly <- sapply(allModelParameters, "[", c("parameters"))
# unstandardizedOnly <- sapply(unstandardizedOnly, "[", "unstandardized")
# names(unstandardizedOnly) <- oldNames
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# #add the filename as a field in the data.frame (so it's uniquely identified when combined)
# lapply(names(unstandardizedOnly), function(element) {
# 	unstandardizedOnly[[element]]$filename <<- element
# })
# 
# #this will only work if all data.frames have identical columns (i.e., same Mplus output fields)
# combinedParameters <- do.call("rbind", unstandardizedOnly)
# 

## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
#   library(MplusAutomation)
#   library(ggplot2)
#   modelParams <- readModels("output_to_plot.out")$parameters$unstandardized
#   modelParams <- subset(modelParams,
#     paramHeader=="Means" &
#       LatentClass != "Categorical.Latent.Variables",
#     select=c("LatentClass", "param", "est", "se"))
# 
#   limits <- aes(ymax = est + se, ymin=est - se)
# 	
# 	fmmMeanPlot <- ggplot(modelParams, aes(x=param, y=est)) +
# 	    geom_pointrange(limits) +
# 	    scale_x_discrete("") +
# 	    geom_hline(yintercept=0, color="grey50") +
# 	    facet_grid(LatentClass ~ .) +
# 	    theme_bw() +
# 	    ylab("Mean Value") +
# 	    coord_flip()
# 	print(fmmMeanPlot)
# 

## ----echo = FALSE, out.width = "80%", fig.pos="h", fig.cap = "Figure. Example of graphing finite mixutre model results from Mplus using ggplot2."----

knitr::include_graphics("mplusAutomationFMMPlot.png")


## ----eval=FALSE, echo=TRUE----------------------------------------------------
# 
# parallelModels <- readModels("10_14_Harsh_SelfCon_Impul")
# 
# compareModels(parallelModels[["backport.from.grand.model.out"]],
#   parallelModels[["backport.from.grand.model.slopesonw1.out"]],
#   show = c("diff", "pdiff", "summaries", "unique"),
#   equalityMargin = c(param = .05, pvalue = .02),
#   sort = "type", diffTest = TRUE, showNS = FALSE)
#