vignettes/analysisExampleSimulatedData.R
In enaY15/TabulationAutomation: Auomated analysis and tabulation of results
## ----Model and Factor Name Vectors and Lists, include=FALSE-------------------
library(TabulationAutomation)
#=============================================================================================================#
##### Specify Regression Analyses Outcome Sets ####
#=============================================================================================================#
TabulationAutomation::addModelContent(setName = 'drinking_outcomes',
list = 'outcomesList',
setContent = c('MAXDRINKS', 'DRINKFREQ', 'USUALAMT', 'BINGE', 'INTOX'),
setNameAttribute = c('Maximum Drinks', 'Drinking Frequency', 'Usual Amount', 'Binge Frequency', 'Intoxication Frequency'))
TabulationAutomation::addModelContent(setName = 'sf12_outcomes',
list = 'outcomesList',
setContent = c('gh', 'calm', 'depressed', 'accomplishedless', 'carefulless') ,
setNameAttribute = c('General Health', 'Calm/Peaceful', 'Down/Depressed', 'Less Accomplished', 'Less Careful'))
TabulationAutomation::addModelContent(setName = 'dx_outcomes',
list = 'outcomesList',
setContent = c('pyptsd', 'lptsd', 'pymddisorder' , 'lmddisorder',
'bpddx1', 'pygadind', 'lgadind', 'pyspeind', 'lspeind',
'pypanicind', 'lpanicind', 'pyaud5', 'lifeaud5',
'lmaud5', 'lnicdep5'),
setNameAttribute = c('Past Year PTSD', 'Lifetime PTSD', 'Past Year MDD', 'Life MDD',
'BPD', 'Past Year GAD', 'Life GAD', 'Past Year Phobia', 'Life Phobia',
'Past Year Panic', 'Life Panic', 'Past Year AUD', 'Life AUD',
'Last Month AUD', 'Life Nicotine Dependence'))
#=============================================================================================================#
##### Specify Regression Analyses Predictor Sets (e.g. Sets of Factors in Different Models) ####
#=============================================================================================================#
TabulationAutomation::addModelContent(setName = 'dsm5',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'COGMOOD', 'HYPERAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Cognition/Mood', 'Hyperarousal'))
TabulationAutomation::addModelContent(setName = 'dysphoria',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'DYSPHORIA', 'HYPERAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Dysphoria', 'Hyperarousal'))
TabulationAutomation::addModelContent(setName = 'dysphoricarousal',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'COGMOOD', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Dysphoria', 'Dysphoric Arousal', 'Anxious Arousal'))
TabulationAutomation::addModelContent(setName = 'anhedonia',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Negative Affect', 'Anhedonia', 'Dysphoric Arousal', 'Anxious Arousal'))
TabulationAutomation::addModelContent(setName = 'externalizing',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'NUMBING', 'EXTERNALIZING', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Numbing', 'Externalizing', 'Dysphoric Arousal', 'Anxious Arousal'))
TabulationAutomation::addModelContent(setName = 'hybrid',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'EXTERNALIZING', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Negative Affect', 'Anhedonia', 'Externalizing', 'Dysphoric Arousal', 'Anxious Arousal'))
#=============================================================================================================#
##### Make More Variables from Specifications ####
#=============================================================================================================#
##### NESARC 3: Indicators and Factors ####
indicatorModels <- matrix(c('B1', 'In', 'In', 'In', 'In', 'In', 'In',
'B2', 'In', 'In', 'In', 'In', 'In', 'In',
'B3', 'In', 'In', 'In', 'In', 'In', 'In',
'B4', 'In', 'In', 'In', 'In', 'In', 'In',
'B5', 'In', 'In', 'In', 'In', 'In', 'In',
'C1', 'Av', 'Av', 'Av', 'Av', 'Av', 'Av',
'C2', 'Av', 'Av', 'Av', 'Av', 'Av', 'Av',
'D1', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D2', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D3', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D4', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D5', 'CM', 'Dy', 'CM', 'Nu', 'An', 'An',
'D6', 'CM', 'Dy', 'CM', 'Nu', 'An', 'An',
'D7', 'CM', 'Dy', 'CM', 'Nu', 'An', 'An',
'E1', 'HA', 'Dy', 'DA', 'EB', 'DA', 'EB',
'E2', 'HA', 'Dy', 'DA', 'EB', 'DA', 'EB',
'E3', 'HA', 'HA', 'AA', 'AA', 'AA', 'AA',
'E4', 'HA', 'HA', 'AA', 'AA', 'AA', 'AA',
'E5', 'HA', 'Dy', 'DA', 'DA', 'DA', 'DA',
'E6', 'HA', 'Dy', 'DA', 'DA', 'DA', 'DA'), nrow = 20, byrow = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'In', replacement = 'INTRUSIONS', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Av', replacement = 'AVOIDANCE', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'CM', replacement = 'COGMOOD', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'HA', replacement = 'HYPERAROUSAL', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Dy', replacement = 'DYSPHORIA', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Nu', replacement = 'NUMBING', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'AA', replacement = 'ANXAROUSAL', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Na', replacement = 'NEGAFF', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'An', replacement = 'ANHEDONIA', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'EB', replacement = 'EXTERNALIZING', fixed = TRUE)
indicatorModels[which(indicatorModels == 'DA')] <- 'DYSPHAROUSAL'
indicatorModels <- data.frame(indicatorModels)
colnames(indicatorModels) <- c('mplusSymptom', 'dsm5', 'dysphoria', 'dysphoricarousal', 'externalizing', 'anhedonia', 'hybrid')
View(indicatorModels)
factorContentsList <- vector(mode = 'list', length(modelContentsList))
names(factorContentsList) <- names(modelContentsList)
for (m in 1:length(modelContentsList)) {
factorContentsList[[names(modelContentsList)[m]]] <- vector(mode = 'list', length(modelContentsList[[names(modelContentsList)[m]]]))
names(factorContentsList[[m]]) <- modelContentsList[[m]]
model <- names(factorContentsList)[m]
for (f in 1:length(factorContentsList[[m]])) {
factor <- names(factorContentsList[[m]][f])
factorContentsList[[model]][factor] <- paste0(indicatorModels[which(indicatorModels[, model] == factor), 'mplusSymptom'], collapse = ' ')
}
}
#=============================================================================================================#
##### Make More Variables from Specifications ####
#=============================================================================================================#
outcomeNames <- extractModelInfo(source = outcomesList, what = "vectorElements", topElementsSharedEnding = "_outcomes")
### Split up list of models into list (elements = list for each model) of lists (elements = vector for each factor) of vectors (elements = factor indicators)
# Can remove dashes and spaces to split up strings of MPlus syntax
modelFactorIndicators_listolists <- extractModelInfo(factorContentsList, what = "mplusVariables")
print(modelFactorIndicators_listolists)
# Example if just want to do for one model
print(extractModelInfo(factorContentsList[['hybrid']], what = "mplusVariables"))
## ----Simulate Data, include=FALSE---------------------------------------------
n = 100
data <- data.frame(matrix(nrow = n, ncol = length(unlist(outcomesList))))
colnames(data) <- names(unlist(outcomesList))
data$row <- 1:dim(data)[1]
## Simulate SF-12
sf12covMat <- matrix(nrow = length(sf12_outcomes), ncol = length(sf12_outcomes))
simulatedCorrs <- rnorm((length(sf12_outcomes)^2-length(sf12_outcomes))/2, 0.7, 0.02)
sf12covMat[upper.tri(sf12covMat)] <- simulatedCorrs # simulated factor correlations store in upper triangular
sf12covMat[lower.tri(sf12covMat)] <- sf12covMat[upper.tri(sf12covMat)] # fill in rest of symmetric matrix
diag(sf12covMat) <- 1
data[, sf12_outcomes] <- MASS::mvrnorm(n, rep(0, length(sf12_outcomes)), sf12covMat)
baseRate <- rnorm(n = length(dx_outcomes), mean = 0.08, sd = 0.02) # randomly simulate base rates for each disorder
for (y in 1:length(dx_outcomes)) {
diagnosedPeople <- sample(x = data$row, floor(baseRate[y]*n)) # randomly pick people (such that correct proportion of sample) to have dx
data[, dx_outcomes[y]] <- ifelse(data$row %in% diagnosedPeople, 1, 0)
}
factorModelNameVector <- names(modelContentsList)
for (m in 1:length(modelContentsList)) {
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(matrix(NA, nrow = ', n, ', ncol = ', length(modelContentsList[[m]]), '))')))
colnames <- modelContentsList[[m]]
eval(parse(text = paste0('colnames(data_cfa_', factorModelNameVector[m], ') <- colnames')))
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(c(1:', n, '), data_cfa_', factorModelNameVector[m], ')')))
eval(parse(text = paste0('colnames(data_cfa_', factorModelNameVector[m], ')[1] <- \'ID\'')))
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(data_cfa_', factorModelNameVector[m], ', data)')))
n.factors <- length(modelContentsList[[m]])
## Simulate factor correlation matrix
factorCovMat <- matrix(nrow = n.factors, ncol = n.factors)
simulatedCorrs <- rnorm((n.factors^2-n.factors)/2, 0.8, 0.02)
factorCovMat[upper.tri(factorCovMat)] <- simulatedCorrs # simulated factor correlations store in upper triangular
factorCovMat[lower.tri(factorCovMat)] <- factorCovMat[upper.tri(factorCovMat)] # fill in rest of symmetric matrix
diag(factorCovMat) <- 1
## Simulate factor scores
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], '[, ', paste0('c(', paste0('\'', modelContentsList[[m]], collapse = '\','), '\')'), '] <- MASS::mvrnorm(', n, ', rep(0, n.factors), factorCovMat)')))
}
## ----Mplus, eval=FALSE, include=FALSE-----------------------------------------
#
# dataCols <- paste0(paste0("b", 1:5, collapse = ' '), ' ', paste0("c", 1:2, collapse = ' '), ' ', paste0("d", 1:7, collapse = ' '), ' ', paste0("e", 1:6, collapse = ' '), sep = ' ')
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate', mplusOutputDirectory = getwd(),
# factorVarSpec = 'free', mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = "all", na.string = "-9999",
# run = FALSE)
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate1_fixed', mplusOutputDirectory = getwd(),
# factorVarSpec = 'standardized', mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = "all", na.string = "-9999",
# run = FALSE)
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate_noCat_oneFixedAllMods', mplusOutputDirectory = getwd(),
# factorVarSpec = c('@5', rep('free', length(factorContentsList)-1)),
# mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = NULL, na.string = "-9999",
# run = FALSE)
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate_someCat', mplusOutputDirectory = getwd(),
# factorVarSpec = 'free',
# mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = 'a1 a2 b5', na.string = "-9999",
# run = FALSE)
#
# fVarSpecMixed <- vector(mode = 'list', length(modelContentsList))
# names(fVarSpecMixed) <- names(modelContentsList)
# for (m in 1:(length(modelContentsList))) fVarSpecMixed[[m]] <- rep('free', length(modelContentsList[[m]]))
# TabulationAutomation::addModelContent(setName = 'hybrid',
# list = 'fVarSpecMixed',
# setContent = c("free", "free", "@1", "@1", "free", "free", "free"),
# setNameAttribute = modelContentsList[['hybrid']])
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate_oneFixedSomeMods', mplusOutputDirectory = getwd(),
# factorVarSpec = fVarSpecMixed,
# mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = "all", na.string = "-9999",
# run = FALSE)
#
## ----Test Run-----------------------------------------------------------------
outputList <- TabulationAutomation::autoAnalyzeR(corrReturn = TRUE, modelPredictorNamesList = modelContentsList, outcomeVector = sf12_outcomes, outcomesSetName = 'sf12', analysis = 'cfa', minDecimals = 2, n.roundDigits = 2)
enaY15/TabulationAutomation documentation built on March 18, 2020, 8:35 p.m.
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## ----Model and Factor Name Vectors and Lists, include=FALSE-------------------
library(TabulationAutomation)
#=============================================================================================================#
##### Specify Regression Analyses Outcome Sets ####
#=============================================================================================================#
TabulationAutomation::addModelContent(setName = 'drinking_outcomes',
list = 'outcomesList',
setContent = c('MAXDRINKS', 'DRINKFREQ', 'USUALAMT', 'BINGE', 'INTOX'),
setNameAttribute = c('Maximum Drinks', 'Drinking Frequency', 'Usual Amount', 'Binge Frequency', 'Intoxication Frequency'))
TabulationAutomation::addModelContent(setName = 'sf12_outcomes',
list = 'outcomesList',
setContent = c('gh', 'calm', 'depressed', 'accomplishedless', 'carefulless') ,
setNameAttribute = c('General Health', 'Calm/Peaceful', 'Down/Depressed', 'Less Accomplished', 'Less Careful'))
TabulationAutomation::addModelContent(setName = 'dx_outcomes',
list = 'outcomesList',
setContent = c('pyptsd', 'lptsd', 'pymddisorder' , 'lmddisorder',
'bpddx1', 'pygadind', 'lgadind', 'pyspeind', 'lspeind',
'pypanicind', 'lpanicind', 'pyaud5', 'lifeaud5',
'lmaud5', 'lnicdep5'),
setNameAttribute = c('Past Year PTSD', 'Lifetime PTSD', 'Past Year MDD', 'Life MDD',
'BPD', 'Past Year GAD', 'Life GAD', 'Past Year Phobia', 'Life Phobia',
'Past Year Panic', 'Life Panic', 'Past Year AUD', 'Life AUD',
'Last Month AUD', 'Life Nicotine Dependence'))
#=============================================================================================================#
##### Specify Regression Analyses Predictor Sets (e.g. Sets of Factors in Different Models) ####
#=============================================================================================================#
TabulationAutomation::addModelContent(setName = 'dsm5',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'COGMOOD', 'HYPERAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Cognition/Mood', 'Hyperarousal'))
TabulationAutomation::addModelContent(setName = 'dysphoria',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'DYSPHORIA', 'HYPERAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Dysphoria', 'Hyperarousal'))
TabulationAutomation::addModelContent(setName = 'dysphoricarousal',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'COGMOOD', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Dysphoria', 'Dysphoric Arousal', 'Anxious Arousal'))
TabulationAutomation::addModelContent(setName = 'anhedonia',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Negative Affect', 'Anhedonia', 'Dysphoric Arousal', 'Anxious Arousal'))
TabulationAutomation::addModelContent(setName = 'externalizing',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'NUMBING', 'EXTERNALIZING', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Numbing', 'Externalizing', 'Dysphoric Arousal', 'Anxious Arousal'))
TabulationAutomation::addModelContent(setName = 'hybrid',
list = 'modelContentsList',
setContent = c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'EXTERNALIZING', 'DYSPHAROUSAL', 'ANXAROUSAL'),
setNameAttribute = c('Intrusions', 'Avoidance', 'Negative Affect', 'Anhedonia', 'Externalizing', 'Dysphoric Arousal', 'Anxious Arousal'))
#=============================================================================================================#
##### Make More Variables from Specifications ####
#=============================================================================================================#
##### NESARC 3: Indicators and Factors ####
indicatorModels <- matrix(c('B1', 'In', 'In', 'In', 'In', 'In', 'In',
'B2', 'In', 'In', 'In', 'In', 'In', 'In',
'B3', 'In', 'In', 'In', 'In', 'In', 'In',
'B4', 'In', 'In', 'In', 'In', 'In', 'In',
'B5', 'In', 'In', 'In', 'In', 'In', 'In',
'C1', 'Av', 'Av', 'Av', 'Av', 'Av', 'Av',
'C2', 'Av', 'Av', 'Av', 'Av', 'Av', 'Av',
'D1', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D2', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D3', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D4', 'CM', 'Dy', 'CM', 'Nu', 'Na', 'Na',
'D5', 'CM', 'Dy', 'CM', 'Nu', 'An', 'An',
'D6', 'CM', 'Dy', 'CM', 'Nu', 'An', 'An',
'D7', 'CM', 'Dy', 'CM', 'Nu', 'An', 'An',
'E1', 'HA', 'Dy', 'DA', 'EB', 'DA', 'EB',
'E2', 'HA', 'Dy', 'DA', 'EB', 'DA', 'EB',
'E3', 'HA', 'HA', 'AA', 'AA', 'AA', 'AA',
'E4', 'HA', 'HA', 'AA', 'AA', 'AA', 'AA',
'E5', 'HA', 'Dy', 'DA', 'DA', 'DA', 'DA',
'E6', 'HA', 'Dy', 'DA', 'DA', 'DA', 'DA'), nrow = 20, byrow = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'In', replacement = 'INTRUSIONS', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Av', replacement = 'AVOIDANCE', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'CM', replacement = 'COGMOOD', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'HA', replacement = 'HYPERAROUSAL', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Dy', replacement = 'DYSPHORIA', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Nu', replacement = 'NUMBING', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'AA', replacement = 'ANXAROUSAL', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'Na', replacement = 'NEGAFF', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'An', replacement = 'ANHEDONIA', fixed = TRUE)
indicatorModels <- gsub(x = indicatorModels, pattern = 'EB', replacement = 'EXTERNALIZING', fixed = TRUE)
indicatorModels[which(indicatorModels == 'DA')] <- 'DYSPHAROUSAL'
indicatorModels <- data.frame(indicatorModels)
colnames(indicatorModels) <- c('mplusSymptom', 'dsm5', 'dysphoria', 'dysphoricarousal', 'externalizing', 'anhedonia', 'hybrid')
View(indicatorModels)
factorContentsList <- vector(mode = 'list', length(modelContentsList))
names(factorContentsList) <- names(modelContentsList)
for (m in 1:length(modelContentsList)) {
factorContentsList[[names(modelContentsList)[m]]] <- vector(mode = 'list', length(modelContentsList[[names(modelContentsList)[m]]]))
names(factorContentsList[[m]]) <- modelContentsList[[m]]
model <- names(factorContentsList)[m]
for (f in 1:length(factorContentsList[[m]])) {
factor <- names(factorContentsList[[m]][f])
factorContentsList[[model]][factor] <- paste0(indicatorModels[which(indicatorModels[, model] == factor), 'mplusSymptom'], collapse = ' ')
}
}
#=============================================================================================================#
##### Make More Variables from Specifications ####
#=============================================================================================================#
outcomeNames <- extractModelInfo(source = outcomesList, what = "vectorElements", topElementsSharedEnding = "_outcomes")
### Split up list of models into list (elements = list for each model) of lists (elements = vector for each factor) of vectors (elements = factor indicators)
# Can remove dashes and spaces to split up strings of MPlus syntax
modelFactorIndicators_listolists <- extractModelInfo(factorContentsList, what = "mplusVariables")
print(modelFactorIndicators_listolists)
# Example if just want to do for one model
print(extractModelInfo(factorContentsList[['hybrid']], what = "mplusVariables"))
## ----Simulate Data, include=FALSE---------------------------------------------
n = 100
data <- data.frame(matrix(nrow = n, ncol = length(unlist(outcomesList))))
colnames(data) <- names(unlist(outcomesList))
data$row <- 1:dim(data)[1]
## Simulate SF-12
sf12covMat <- matrix(nrow = length(sf12_outcomes), ncol = length(sf12_outcomes))
simulatedCorrs <- rnorm((length(sf12_outcomes)^2-length(sf12_outcomes))/2, 0.7, 0.02)
sf12covMat[upper.tri(sf12covMat)] <- simulatedCorrs # simulated factor correlations store in upper triangular
sf12covMat[lower.tri(sf12covMat)] <- sf12covMat[upper.tri(sf12covMat)] # fill in rest of symmetric matrix
diag(sf12covMat) <- 1
data[, sf12_outcomes] <- MASS::mvrnorm(n, rep(0, length(sf12_outcomes)), sf12covMat)
baseRate <- rnorm(n = length(dx_outcomes), mean = 0.08, sd = 0.02) # randomly simulate base rates for each disorder
for (y in 1:length(dx_outcomes)) {
diagnosedPeople <- sample(x = data$row, floor(baseRate[y]*n)) # randomly pick people (such that correct proportion of sample) to have dx
data[, dx_outcomes[y]] <- ifelse(data$row %in% diagnosedPeople, 1, 0)
}
factorModelNameVector <- names(modelContentsList)
for (m in 1:length(modelContentsList)) {
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(matrix(NA, nrow = ', n, ', ncol = ', length(modelContentsList[[m]]), '))')))
colnames <- modelContentsList[[m]]
eval(parse(text = paste0('colnames(data_cfa_', factorModelNameVector[m], ') <- colnames')))
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(c(1:', n, '), data_cfa_', factorModelNameVector[m], ')')))
eval(parse(text = paste0('colnames(data_cfa_', factorModelNameVector[m], ')[1] <- \'ID\'')))
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(data_cfa_', factorModelNameVector[m], ', data)')))
n.factors <- length(modelContentsList[[m]])
## Simulate factor correlation matrix
factorCovMat <- matrix(nrow = n.factors, ncol = n.factors)
simulatedCorrs <- rnorm((n.factors^2-n.factors)/2, 0.8, 0.02)
factorCovMat[upper.tri(factorCovMat)] <- simulatedCorrs # simulated factor correlations store in upper triangular
factorCovMat[lower.tri(factorCovMat)] <- factorCovMat[upper.tri(factorCovMat)] # fill in rest of symmetric matrix
diag(factorCovMat) <- 1
## Simulate factor scores
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], '[, ', paste0('c(', paste0('\'', modelContentsList[[m]], collapse = '\','), '\')'), '] <- MASS::mvrnorm(', n, ', rep(0, n.factors), factorCovMat)')))
}
## ----Mplus, eval=FALSE, include=FALSE-----------------------------------------
#
# dataCols <- paste0(paste0("b", 1:5, collapse = ' '), ' ', paste0("c", 1:2, collapse = ' '), ' ', paste0("d", 1:7, collapse = ' '), ' ', paste0("e", 1:6, collapse = ' '), sep = ' ')
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate', mplusOutputDirectory = getwd(),
# factorVarSpec = 'free', mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = "all", na.string = "-9999",
# run = FALSE)
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate1_fixed', mplusOutputDirectory = getwd(),
# factorVarSpec = 'standardized', mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = "all", na.string = "-9999",
# run = FALSE)
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate_noCat_oneFixedAllMods', mplusOutputDirectory = getwd(),
# factorVarSpec = c('@5', rep('free', length(factorContentsList)-1)),
# mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = NULL, na.string = "-9999",
# run = FALSE)
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate_someCat', mplusOutputDirectory = getwd(),
# factorVarSpec = 'free',
# mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = 'a1 a2 b5', na.string = "-9999",
# run = FALSE)
#
# fVarSpecMixed <- vector(mode = 'list', length(modelContentsList))
# names(fVarSpecMixed) <- names(modelContentsList)
# for (m in 1:(length(modelContentsList))) fVarSpecMixed[[m]] <- rep('free', length(modelContentsList[[m]]))
# TabulationAutomation::addModelContent(setName = 'hybrid',
# list = 'fVarSpecMixed',
# setContent = c("free", "free", "@1", "@1", "free", "free", "free"),
# setNameAttribute = modelContentsList[['hybrid']])
#
# factorAnalyzeR(mplusDataFileColumnNames = dataCols,
# factorIndicatorsList = factorContentsList, modelFactorsList = modelContentsList,
# templateName = 'testTemplate_oneFixedSomeMods', mplusOutputDirectory = getwd(),
# factorVarSpec = fVarSpecMixed,
# mplusDataFilePath = "C:/Users/ENA/Documents/GitHub/PTSDn2/n2.csv",
# categoricalInds = "all", na.string = "-9999",
# run = FALSE)
#
## ----Test Run-----------------------------------------------------------------
outputList <- TabulationAutomation::autoAnalyzeR(corrReturn = TRUE, modelPredictorNamesList = modelContentsList, outcomeVector = sf12_outcomes, outcomesSetName = 'sf12', analysis = 'cfa', minDecimals = 2, n.roundDigits = 2)
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