archive/AutoAnalysis.R
In enaY15/TabulationAutomation: Auomated analysis and tabulation of results
## ----Set Up, message=FALSE, include=FALSE-------------------------------------
#options(tinytex.verbose = TRUE)
source('combinedLaTeXtableR.R', encoding = 'UTF-8')
source('combinedLaTeXtableR.R', encoding = 'UTF-8')
source('combinedLaTeXtableR.R', encoding = 'UTF-8')
require(pscl, quietly = TRUE); require(tidyr, quietly = TRUE); require(MASS, quietly = TRUE); require(brms, quietly = TRUE); require(magrittr, quietly = TRUE); require(knitr, quietly = TRUE); require(dplyr, quietly = TRUE); require(htmlTable, quietly = TRUE); require(tibble, quietly = TRUE); require(psych, quietly = TRUE); require(stats, quietly = TRUE); require(MplusAutomation, quietly = TRUE); require(papaja, quietly = TRUE); require(Hmisc, quietly = TRUE); require(pscl, quietly = TRUE)
homepath <- paste0('C:/Users/', Sys.getenv('USERNAME'))
#### Function to read in files ####
dataRead <- function(homepath, file) eval(parse(text = paste0('read.csv("', homepath, file, ')')))
n3.a1 <- dataRead(homepath, file = '/Box Sync/Research/0 Data 0/NESARCIII/nesarcA1final.csv", na.strings = (c("9999", "NA")))')
n3.a1$CASEID <- as.character(n3.a1$CASEID)
n3 <- dataRead(homepath, file = '/Box Sync/Research/0 Data 0/NESARCIII/n3csv.csv", na.strings = (c("9999", "NA")))')
sf12 <- read.csv('sf12_n3.csv', na.strings = '9')
colnames(sf12)[1] <- 'CASEID'
sf12$calm <- sf12$N1Q33A
sf12$depressed <- sf12$N1Q33C
sf12$accomplishedless <- sf12$N1Q32A
sf12$carefulless <- sf12$N1Q32B
sf12$GeneralHealth <- sf12$N1Q25
sf12$gh <- ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[1], 1, ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[2], 2, ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[3], 3, ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[4], 4, 5))))
diagnoses <- read.csv('diagnoses.csv'); diagnoses <- diagnoses[, -1]
#impairment <- read.csv('impairment.csv'); impairment <- impairment[, -1]
outcomes <- read.csv('outcomes.csv'); outcomes <- outcomes[, -1]
outcomes <- outcomes[, -which(names(outcomes) %in% names(diagnoses))]
outcomes <- data.frame(n3.a1$CASEID, outcomes); colnames(outcomes)[1] <- 'CASEID'
outcomes <- cbind(sf12[sf12$CASEID %in% outcomes$CASEID, -1])
#### Drinking Data Cleaning ####
nesarc3 <- dataRead(homepath, file = '/Box Sync/Research/0 Data 0/NESARCIII.csv")')
nesarc3.a1 <- nesarc3[nesarc3$CASEID %in% n3.a1$CASEID, ]
rm(nesarc3)
## MAXDRINKS
MAXDRINKS <- ifelse(is.na(nesarc3.a1$S2AQ4C), 0, ifelse(nesarc3.a1$S2AQ4C==99, NA, nesarc3.a1$S2AQ4C))
## FREQENCY DRINKING
DRINKFREQ <- ifelse(is.na(nesarc3.a1$S2AQ4A), 0, ifelse(nesarc3.a1$S2AQ4A==99, NA, 11-nesarc3.a1$S2AQ4A))
## USUAL AMOUNT DRINKING
USUALAMT <- ifelse(is.na(nesarc3.a1$S2AQ4B), 0, ifelse(nesarc3.a1$S2AQ4B==99, NA, nesarc3.a1$S2AQ4B))
## FREQENCY MAX
FREQMAX <- ifelse(is.na(nesarc3.a1$S2AQ4E), 0, ifelse(nesarc3.a1$S2AQ4E==99, NA, 11-nesarc3.a1$S2AQ4E))
#HOW OFTEN DRANK LARGEST NUMBER OF DRINKS IN A SINGLE DAY IN LAST 12 MONTHS
## BINGING WITH DifFERENT CUTOFFS FOR MEN AND WOMEN
BINGE <- ifelse((nesarc3.a1$SEX==1 & is.na(nesarc3.a1$S2AQ4H)), 0, ifelse((nesarc3.a1$SEX==1 & nesarc3.a1$S2AQ4H==99), NA, ifelse(nesarc3.a1$SEX==1, 11-nesarc3.a1$S2AQ4H, ifelse((nesarc3.a1$SEX==2 & is.na(nesarc3.a1$S2AQ4F)), 0, ifelse((nesarc3.a1$SEX == 2 & nesarc3.a1$S2AQ4F == 99), NA, 11-nesarc3.a1$S2AQ4F)))))
## HOW OFTEN INTOXICATED
INTOX <- ifelse(is.na(nesarc3.a1$S2AQ9), 0, ifelse(nesarc3.a1$S2AQ9==99, NA, 11-nesarc3.a1$S2AQ9))
## FREQUENCY OF EXCEEDING DAILY DRINKING LIMITS
#if (is.na(nesarc3.a1$FEXMAX)) FREQEX <- 0 else if (nesarc3.a1$FEXMAX==99) FREQEX <- NA else FREQEX <- FEXMAX
#### CREATE DRINKING + OUTCOME DF ####
drinking <- data.frame(MAXDRINKS, DRINKFREQ, USUALAMT, FREQMAX, BINGE, INTOX
#, FREQEX
)
outcomes <- cbind(outcomes, drinking)
## ----Model and Factor Name Vectors and Lists, include=FALSE-------------------
## Factors and Models
factorModelNameVector <- c('dsm5', 'dysphoria', 'dysphoricarousal', 'anhedonia', 'externalizing', 'hybrid')
modelFactorNamesList <- list(c('INTRUSIONS', 'AVOIDANCE', 'COGMOOD', 'HYPERAROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'DYSPHORIA', 'HYPERAROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'DYSPHORIA', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'NUMBING', 'EXTERNALIZING', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'EXTERNALIZING', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'))
names(modelFactorNamesList) <- factorModelNameVector
## Outcomes
sf12_outcomes <- c('gh', 'calm', 'depressed', 'accomplishedless', 'carefulless')
#dx_outcomes <- c('pyptsd', 'lptsd', 'pymdepind', 'lmdepind', 'pymddisorder' , 'lmddisorder', 'ldysind', 'bpddx1', 'pygadind', 'lgadind', 'pyspeind', 'lspeind', 'pypanicind', 'lpanicind', 'lifeaud5', 'pyaud5', 'lnicdep5', 'lmaud5')
dx_outcomes <- c('pyptsd', 'lptsd', 'pymddisorder' , 'lmddisorder', 'bpddx1', 'pygadind', 'lgadind', 'pyspeind', 'lspeind', 'pypanicind', 'lpanicind', 'pyaud5', 'lifeaud5', 'lmaud5', 'lnicdep5')
dvs <- outcomes
#drinking_outcomes <- c('numwhendrink', 'agefirstdrink', 'freqyearlydrunk')
drinking_outcomes <- c('MAXDRINKS', 'DRINKFREQ', 'USUALAMT',
#'FREQMAX',
'BINGE', 'INTOX'
#, 'FREQEX'
)
#outcomeNameVector <- c('SF-12 General Health', 'SF-12 Calm or Peaceful', 'SF-12 Down or Depressed', 'SF-12 Less Accomplished', 'SF-12 Less Careful', 'Past Year PTSD', 'Lifetime PTSD', 'Past Year MDD', 'Life MDD', 'BPD', 'Past Year GAD', 'Life GAD', 'pyspeind', 'lspeind', 'Past Year Panic', 'Life Panic', 'Past Year AUD', 'Life AUD', 'lmaud5', 'Life Nicotine Dependence', 'Number Typical Drinks', 'Age 1st Drink', 'Intoxication Frequency')
outcomeNameVector <- c('SF-12 General Health', 'SF-12 Calm or Peaceful', 'SF-12 Down or Depressed', 'SF-12 Less Accomplished', 'SF-12 Less Careful', 'Past Year PTSD', 'Lifetime PTSD', 'Past Year MDD', 'Life MDD', 'BPD', 'Past Year GAD', 'Life GAD', 'pyspeind', 'lspeind', 'Past Year Panic', 'Life Panic', 'Past Year AUD', 'Life AUD', 'lmaud5', 'Life Nicotine Dependence', 'Maximum Drinks', 'Drinking Frequency', 'Usual Drink Number', 'Frequency Drink Maximum', 'Binge Frequency', 'Intoxication Frequency', 'Frequency Exceed Daily Limits')
#names(outcomeNameVector) <- c('gh', 'calm', 'depressed', 'accomplishedless', 'carefulless', 'lptsd', 'pyptsd', 'laud5', 'pyaud5', 'numwhendrink', 'agefirstdrink', 'freqyearlydrunk.1')
names(outcomeNameVector) <- c(sf12_outcomes, dx_outcomes, drinking_outcomes)
## ----Data, eval=FALSE, include=FALSE------------------------------------------
##
## lca_cprob <- read.csv('lca_cprob_nesarc.csv')
## lca <- data.frame(lca_cprob, outcomes, diagnoses)
##
## #### BCFA FScores ####
## #fscoresMakeR <- function() {
## for (m in 1:length(factorModelNameVector)) {
## model <- factorModelNameVector[m]
## factors <- modelFactorNamesList[[model]] # factors in structural model
## filePath <- paste0(getwd(), '/FScores_N3_BCFA_', length(factors), '_', model, '.csv')
## fscores <- read.delim(file = filePath, header = FALSE, sep = '', na.strings = '*')
## #fscores <- eval(parse(text = paste0('fscores_cfa_', model))) # Mplus fscores output
## fNames <- seq(from = -1*(dim(fscores)[2] - 4), by = 5, length.out = length(factors))
## # negative numbers so can count up starting with 5th from end of df (last factor score) is reference point,
## fNames <- fNames[(order(fNames*-1))] # then reorder by *-1
## fNames <- gsub(fNames, pattern = '-', replacement = 'V') # capitalize on negative signs left over to easily replace with "V" to make variable names
## data <- data.frame(fscores[, fNames])
## colnames(data) <- factors
## eval(envir = globalenv(), parse(text = paste0('data_bcfa_', model, ' <- data')))
## }
## #}
##
## #### Bifactor FScores ####
## #fscoresMakeR_bf <- function() {
## for (m in 1:length(factorModelNameVector)) {
## #options(browser())
## model <- factorModelNameVector[m]
## factors <- modelFactorNamesList[[model]] # factors in structural model
## filePath <- paste0(getwd(), '/FScores_N3_BF_', length(factors), '_', model, '.csv')
## factors_bf <- c('GENERAL', modelFactorNamesList[[model]]) # if bifactor
## fscores_bf <- read.delim(file = filePath, header = FALSE, sep = '', na.strings = '*')
## #fscores_bf <- eval(parse(text = paste0('fscores_cfa_', model))) # Mplus fscores output
## fNames_bf <- seq(from = -1*(dim(fscores_bf)[2] - 4), by = 5, length.out = length(factors_bf))
## # negative numbers so can count up starting with 5th from end of df (last factor score) is reference point,
## fNames_bf <- fNames_bf[(order(fNames_bf*-1))] # then reorder by *-1
## fNames_bf <- gsub(fNames_bf, pattern = '-', replacement = 'V') # capitalize on negative signs left over to easily replace with "V" to make variable names
## data <- data.frame(fscores_bf[, fNames_bf])
## colnames(data) <- factors_bf
## eval(envir = globalenv(), parse(text = paste0('data_bf_', model, ' <- data')))
## }
## #}
##
## # fscoresMakeR()
## # fscoresMakeR_bf()
##
## for (m in 1:length(factorModelNameVector)) {
## eval(envir = globalenv(), parse(text = paste0('data_bcfa_', factorModelNameVector[m], ' <- data.frame(dvs, data_bcfa_', factorModelNameVector[m], ')')))
## }
## rm(fscores)
##
## for (m in 1:length(factorModelNameVector)) {
## eval(envir = globalenv(), parse(text = paste0('data_bf_', factorModelNameVector[m], ' <- data.frame(dvs, data_bf_', factorModelNameVector[m], ')')))
## }
## rm(fscores_bf)
##
## #### Frequentist CFA FScores ####
## filepathcfa <- gsub(x = paste0(getwd(), '/FScores_N3_'), pattern = '/', replacement = '//')
## for (m in 1:length(factorModelNameVector)) {
## model <- factorModelNameVector[m]
## factors <- modelFactorNamesList[[model]] # factors in structural model
## factors_se <- rep(paste0(modelFactorNamesList[[model]], '_se'))
## for(f in 1:length(factors)) c(factors[f], factors_se[f])
## filePath <- paste0(model, '.csv')
## eval(parse(text = paste0('fscores_', model, ' <- read.delim(file = \"', filepathcfa, filePath, '\", header = FALSE, sep = \'\', na.strings = \'*\')')))
## fscores <- eval(parse(text = paste0('fscores_', model))) # Mplus fscores output
## fscores <- fscores[, -c(1:20)] # take out symptom columns
## fscores <- fscores[, c(seq(from = 1, to = dim(fscores)[2], by = 2))] # drop factor SE cols; leave just factors scores
## colnames(fscores) <- factors
## eval(envir = globalenv(), parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(dvs , fscores)')))
## }
##
## #### Write FScores to csv ####
## for (m in 1:length(factorModelNameVector)) {
## eval(parse(text = paste0('write.csv(data_bcfa_', factorModelNameVector[m], ', \'data_bcfa_', factorModelNameVector[m], '.csv\')')))
## }
## for (m in 1:length(factorModelNameVector)) {
## eval(parse(text = paste0('write.csv(data_bf_', factorModelNameVector[m], ', \'data_bf_', factorModelNameVector[m], '.csv\')')))
## }
## for (m in 1:length(factorModelNameVector)) {
## dims <- (20 + 2*length(modelFactorNamesList[[m]]))
## cols <- c(1:dims)
## eval(parse(text = paste0('write.csv(data_cfa_', factorModelNameVector[m], ', \'data_cfa_', factorModelNameVector[m], '.csv\', col.names = cols)')))
## }
##
## ----Summary Tables, include=FALSE--------------------------------------------
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- read.csv(\'data_cfa_', factorModelNameVector[m], '.csv\')')))
}
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data_bcfa_', factorModelNameVector[m], ' <- read.csv(\'data_bcfa_', factorModelNameVector[m], '.csv\')')))
}
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data_bf_', factorModelNameVector[m], ' <- read.csv(\'data_bf_', factorModelNameVector[m], '.csv\')')))
}
## ----Print Results, echo=FALSE------------------------------------------------
for (y in 1:length(drinking_outcomes)) {
dv <- drinking_outcomes[y]
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
dv <- paste0('data_cfa_', factorModelNameVector[m], '$', dv)
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_cfa_', factorModelNameVector[m], '_results <- printResults(analysis = \'cfa\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----SF12 Summary Tables, include=FALSE---------------------------------------
## CFA
cat('Frequentist Confirmatory Factor Analysis SF-12 Summary Table')
for (y in 1:length(sf12_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(sf12_outcomes[y], '_cfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'cfa\', outcomeVar = sf12_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## BCFA
cat('Bayesian Confirmatory Factor Analysis Short Form Health Survey (SF-12) Summary Tables')
for (y in 1:length(sf12_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(sf12_outcomes[y], '_bcfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bcfa\', outcomeVar = sf12_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bifactor Analysis Short Form Health Survey (SF-12) Summary Tables')
## Bi-Factor
for (y in 1:length(sf12_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(sf12_outcomes[y], '_bf_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bf\', outcomeVar = sf12_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----Diagnoses Summary Tables, include=FALSE----------------------------------
cat('Frequentist Confirmatory Factor Analysis and Psychiatric Diagnoses Summary Tables')
for (y in 1:length(dx_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
data <- data.frame(data, diagnoses)
eval(envir = globalenv(), parse(text = paste0(dx_outcomes[y], '_cfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'binomial\', analysis = \'cfa\', outcomeVar = dx_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bayesian Confirmatory Factor Analysis and Psychiatric Diagnoses Summary Tables')
for (y in 1:length(dx_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
data <- data.frame(data, diagnoses)
eval(envir = globalenv(), parse(text = paste0(dx_outcomes[y], '_bcfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'binomial\', analysis = \'bcfa\', outcomeVar = dx_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Biactor Analysis and Psychiatric Diagnoses Summary Tables')
for (y in 1:length(dx_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
data <- data.frame(data, diagnoses)
eval(envir = globalenv(), parse(text = paste0(dx_outcomes[y], '_bf_', factorModelNameVector[m], ' <- summaryTableFx(family = \'binomial\', analysis = \'bf\', outcomeVar = dx_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----Drinking Summary Tables, include=FALSE-----------------------------------
cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Summary Tables')
for (y in 1:length(drinking_outcomes)) {
dv <- drinking_outcomes[y]
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
dv <- paste0('data_cfa_', factorModelNameVector[m], '$', dv)
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_cfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'cfa\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bayesian Confirmatory Factor Analysis and Alcohol Validators Summary Tables')
for (y in 1:length(drinking_outcomes)) {
dv <- drinking_outcomes[y]
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
dv <- paste0('data_bcfa_', factorModelNameVector[m], '$', dv)
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_bcfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bcfa\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bifactor Analysis and Alcohol Validators Summary Tables')
for (y in 1:length(drinking_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_bf_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bf\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----Correlation Table Function, include=FALSE--------------------------------
#cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Correlation Tables')
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0('drinking_corr_cfa_', factorModelNameVector[m], ' <- corrTableFx(analysis = \'cfa\', outcomes = drinking_outcomes, data = data, modelNum = ', m, ')')))
}
#cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Correlation Tables')
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0('drinking_corr_bcfa_', factorModelNameVector[m], ' <- corrTableFx(analysis = \'bcfa\', outcomes = drinking_outcomes, data = data, modelNum = ', m, ')')))
}
#cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Correlation Tables')
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0('drinking_corr_bf_', factorModelNameVector[m], ' <- corrTableFx(analysis = \'bf\', outcomes = drinking_outcomes, data = data, modelNum = ', m, ')')))
}
## ----SF12 Combined Tables, echo=FALSE, message=FALSE, warning=FALSE, paged.print=TRUE----
combinedLaTeXtableR(analysis = 'cfa',
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
outcomes = sf12_outcomes, outcomeNameVector = outcomeNameVector,
basic = TRUE, stars = TRUE)
combinedLaTeXtableR(analysis = 'bcfa',
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
outcomes = sf12_outcomes, outcomeNameVector = outcomeNameVector,
basic = TRUE, stars = TRUE)
combinedLaTeXtableR(analysis = 'bf',
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
outcomes = sf12_outcomes, outcomeNameVector = outcomeNameVector,
basic = TRUE, stars = TRUE)
bodyBuildeR(mat = c('gh_cfa_dsm5', 'depressed_cfa_dsm5'), matModelName = 'dsm5', multiOutcome = TRUE, outcomeNames = c('General Health', 'Depressed'))
## ----Diagnoses Combined Tables, echo=FALSE, message=FALSE, warning=FALSE, paged.print=TRUE----
combinedLaTeXtableR(analysis = 'cfa',
outcomes = dx_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|z|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bcfa',
outcomes = dx_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|z|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bf', outcomes = dx_outcomes,
outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|z|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
## ----Drinking Combined Tables, echo=FALSE, message=FALSE, warning=FALSE, paged.print=TRUE----
combinedLaTeXtableR(analysis = 'cfa',
outcomes = drinking_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bcfa',
outcomes = drinking_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bf',
outcomes = drinking_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
enaY15/TabulationAutomation documentation built on March 18, 2020, 8:35 p.m.
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## ----Set Up, message=FALSE, include=FALSE-------------------------------------
#options(tinytex.verbose = TRUE)
source('combinedLaTeXtableR.R', encoding = 'UTF-8')
source('combinedLaTeXtableR.R', encoding = 'UTF-8')
source('combinedLaTeXtableR.R', encoding = 'UTF-8')
require(pscl, quietly = TRUE); require(tidyr, quietly = TRUE); require(MASS, quietly = TRUE); require(brms, quietly = TRUE); require(magrittr, quietly = TRUE); require(knitr, quietly = TRUE); require(dplyr, quietly = TRUE); require(htmlTable, quietly = TRUE); require(tibble, quietly = TRUE); require(psych, quietly = TRUE); require(stats, quietly = TRUE); require(MplusAutomation, quietly = TRUE); require(papaja, quietly = TRUE); require(Hmisc, quietly = TRUE); require(pscl, quietly = TRUE)
homepath <- paste0('C:/Users/', Sys.getenv('USERNAME'))
#### Function to read in files ####
dataRead <- function(homepath, file) eval(parse(text = paste0('read.csv("', homepath, file, ')')))
n3.a1 <- dataRead(homepath, file = '/Box Sync/Research/0 Data 0/NESARCIII/nesarcA1final.csv", na.strings = (c("9999", "NA")))')
n3.a1$CASEID <- as.character(n3.a1$CASEID)
n3 <- dataRead(homepath, file = '/Box Sync/Research/0 Data 0/NESARCIII/n3csv.csv", na.strings = (c("9999", "NA")))')
sf12 <- read.csv('sf12_n3.csv', na.strings = '9')
colnames(sf12)[1] <- 'CASEID'
sf12$calm <- sf12$N1Q33A
sf12$depressed <- sf12$N1Q33C
sf12$accomplishedless <- sf12$N1Q32A
sf12$carefulless <- sf12$N1Q32B
sf12$GeneralHealth <- sf12$N1Q25
sf12$gh <- ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[1], 1, ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[2], 2, ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[3], 3, ifelse(sf12$N1Q25 == levels(sf12$N1Q25)[4], 4, 5))))
diagnoses <- read.csv('diagnoses.csv'); diagnoses <- diagnoses[, -1]
#impairment <- read.csv('impairment.csv'); impairment <- impairment[, -1]
outcomes <- read.csv('outcomes.csv'); outcomes <- outcomes[, -1]
outcomes <- outcomes[, -which(names(outcomes) %in% names(diagnoses))]
outcomes <- data.frame(n3.a1$CASEID, outcomes); colnames(outcomes)[1] <- 'CASEID'
outcomes <- cbind(sf12[sf12$CASEID %in% outcomes$CASEID, -1])
#### Drinking Data Cleaning ####
nesarc3 <- dataRead(homepath, file = '/Box Sync/Research/0 Data 0/NESARCIII.csv")')
nesarc3.a1 <- nesarc3[nesarc3$CASEID %in% n3.a1$CASEID, ]
rm(nesarc3)
## MAXDRINKS
MAXDRINKS <- ifelse(is.na(nesarc3.a1$S2AQ4C), 0, ifelse(nesarc3.a1$S2AQ4C==99, NA, nesarc3.a1$S2AQ4C))
## FREQENCY DRINKING
DRINKFREQ <- ifelse(is.na(nesarc3.a1$S2AQ4A), 0, ifelse(nesarc3.a1$S2AQ4A==99, NA, 11-nesarc3.a1$S2AQ4A))
## USUAL AMOUNT DRINKING
USUALAMT <- ifelse(is.na(nesarc3.a1$S2AQ4B), 0, ifelse(nesarc3.a1$S2AQ4B==99, NA, nesarc3.a1$S2AQ4B))
## FREQENCY MAX
FREQMAX <- ifelse(is.na(nesarc3.a1$S2AQ4E), 0, ifelse(nesarc3.a1$S2AQ4E==99, NA, 11-nesarc3.a1$S2AQ4E))
#HOW OFTEN DRANK LARGEST NUMBER OF DRINKS IN A SINGLE DAY IN LAST 12 MONTHS
## BINGING WITH DifFERENT CUTOFFS FOR MEN AND WOMEN
BINGE <- ifelse((nesarc3.a1$SEX==1 & is.na(nesarc3.a1$S2AQ4H)), 0, ifelse((nesarc3.a1$SEX==1 & nesarc3.a1$S2AQ4H==99), NA, ifelse(nesarc3.a1$SEX==1, 11-nesarc3.a1$S2AQ4H, ifelse((nesarc3.a1$SEX==2 & is.na(nesarc3.a1$S2AQ4F)), 0, ifelse((nesarc3.a1$SEX == 2 & nesarc3.a1$S2AQ4F == 99), NA, 11-nesarc3.a1$S2AQ4F)))))
## HOW OFTEN INTOXICATED
INTOX <- ifelse(is.na(nesarc3.a1$S2AQ9), 0, ifelse(nesarc3.a1$S2AQ9==99, NA, 11-nesarc3.a1$S2AQ9))
## FREQUENCY OF EXCEEDING DAILY DRINKING LIMITS
#if (is.na(nesarc3.a1$FEXMAX)) FREQEX <- 0 else if (nesarc3.a1$FEXMAX==99) FREQEX <- NA else FREQEX <- FEXMAX
#### CREATE DRINKING + OUTCOME DF ####
drinking <- data.frame(MAXDRINKS, DRINKFREQ, USUALAMT, FREQMAX, BINGE, INTOX
#, FREQEX
)
outcomes <- cbind(outcomes, drinking)
## ----Model and Factor Name Vectors and Lists, include=FALSE-------------------
## Factors and Models
factorModelNameVector <- c('dsm5', 'dysphoria', 'dysphoricarousal', 'anhedonia', 'externalizing', 'hybrid')
modelFactorNamesList <- list(c('INTRUSIONS', 'AVOIDANCE', 'COGMOOD', 'HYPERAROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'DYSPHORIA', 'HYPERAROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'DYSPHORIA', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'NUMBING', 'EXTERNALIZING', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'),
c('INTRUSIONS', 'AVOIDANCE', 'NEGAFF', 'ANHEDONIA', 'EXTERNALIZING', 'DYSPHORIC_AROUSAL', 'ANXIOUS_AROUSAL'))
names(modelFactorNamesList) <- factorModelNameVector
## Outcomes
sf12_outcomes <- c('gh', 'calm', 'depressed', 'accomplishedless', 'carefulless')
#dx_outcomes <- c('pyptsd', 'lptsd', 'pymdepind', 'lmdepind', 'pymddisorder' , 'lmddisorder', 'ldysind', 'bpddx1', 'pygadind', 'lgadind', 'pyspeind', 'lspeind', 'pypanicind', 'lpanicind', 'lifeaud5', 'pyaud5', 'lnicdep5', 'lmaud5')
dx_outcomes <- c('pyptsd', 'lptsd', 'pymddisorder' , 'lmddisorder', 'bpddx1', 'pygadind', 'lgadind', 'pyspeind', 'lspeind', 'pypanicind', 'lpanicind', 'pyaud5', 'lifeaud5', 'lmaud5', 'lnicdep5')
dvs <- outcomes
#drinking_outcomes <- c('numwhendrink', 'agefirstdrink', 'freqyearlydrunk')
drinking_outcomes <- c('MAXDRINKS', 'DRINKFREQ', 'USUALAMT',
#'FREQMAX',
'BINGE', 'INTOX'
#, 'FREQEX'
)
#outcomeNameVector <- c('SF-12 General Health', 'SF-12 Calm or Peaceful', 'SF-12 Down or Depressed', 'SF-12 Less Accomplished', 'SF-12 Less Careful', 'Past Year PTSD', 'Lifetime PTSD', 'Past Year MDD', 'Life MDD', 'BPD', 'Past Year GAD', 'Life GAD', 'pyspeind', 'lspeind', 'Past Year Panic', 'Life Panic', 'Past Year AUD', 'Life AUD', 'lmaud5', 'Life Nicotine Dependence', 'Number Typical Drinks', 'Age 1st Drink', 'Intoxication Frequency')
outcomeNameVector <- c('SF-12 General Health', 'SF-12 Calm or Peaceful', 'SF-12 Down or Depressed', 'SF-12 Less Accomplished', 'SF-12 Less Careful', 'Past Year PTSD', 'Lifetime PTSD', 'Past Year MDD', 'Life MDD', 'BPD', 'Past Year GAD', 'Life GAD', 'pyspeind', 'lspeind', 'Past Year Panic', 'Life Panic', 'Past Year AUD', 'Life AUD', 'lmaud5', 'Life Nicotine Dependence', 'Maximum Drinks', 'Drinking Frequency', 'Usual Drink Number', 'Frequency Drink Maximum', 'Binge Frequency', 'Intoxication Frequency', 'Frequency Exceed Daily Limits')
#names(outcomeNameVector) <- c('gh', 'calm', 'depressed', 'accomplishedless', 'carefulless', 'lptsd', 'pyptsd', 'laud5', 'pyaud5', 'numwhendrink', 'agefirstdrink', 'freqyearlydrunk.1')
names(outcomeNameVector) <- c(sf12_outcomes, dx_outcomes, drinking_outcomes)
## ----Data, eval=FALSE, include=FALSE------------------------------------------
##
## lca_cprob <- read.csv('lca_cprob_nesarc.csv')
## lca <- data.frame(lca_cprob, outcomes, diagnoses)
##
## #### BCFA FScores ####
## #fscoresMakeR <- function() {
## for (m in 1:length(factorModelNameVector)) {
## model <- factorModelNameVector[m]
## factors <- modelFactorNamesList[[model]] # factors in structural model
## filePath <- paste0(getwd(), '/FScores_N3_BCFA_', length(factors), '_', model, '.csv')
## fscores <- read.delim(file = filePath, header = FALSE, sep = '', na.strings = '*')
## #fscores <- eval(parse(text = paste0('fscores_cfa_', model))) # Mplus fscores output
## fNames <- seq(from = -1*(dim(fscores)[2] - 4), by = 5, length.out = length(factors))
## # negative numbers so can count up starting with 5th from end of df (last factor score) is reference point,
## fNames <- fNames[(order(fNames*-1))] # then reorder by *-1
## fNames <- gsub(fNames, pattern = '-', replacement = 'V') # capitalize on negative signs left over to easily replace with "V" to make variable names
## data <- data.frame(fscores[, fNames])
## colnames(data) <- factors
## eval(envir = globalenv(), parse(text = paste0('data_bcfa_', model, ' <- data')))
## }
## #}
##
## #### Bifactor FScores ####
## #fscoresMakeR_bf <- function() {
## for (m in 1:length(factorModelNameVector)) {
## #options(browser())
## model <- factorModelNameVector[m]
## factors <- modelFactorNamesList[[model]] # factors in structural model
## filePath <- paste0(getwd(), '/FScores_N3_BF_', length(factors), '_', model, '.csv')
## factors_bf <- c('GENERAL', modelFactorNamesList[[model]]) # if bifactor
## fscores_bf <- read.delim(file = filePath, header = FALSE, sep = '', na.strings = '*')
## #fscores_bf <- eval(parse(text = paste0('fscores_cfa_', model))) # Mplus fscores output
## fNames_bf <- seq(from = -1*(dim(fscores_bf)[2] - 4), by = 5, length.out = length(factors_bf))
## # negative numbers so can count up starting with 5th from end of df (last factor score) is reference point,
## fNames_bf <- fNames_bf[(order(fNames_bf*-1))] # then reorder by *-1
## fNames_bf <- gsub(fNames_bf, pattern = '-', replacement = 'V') # capitalize on negative signs left over to easily replace with "V" to make variable names
## data <- data.frame(fscores_bf[, fNames_bf])
## colnames(data) <- factors_bf
## eval(envir = globalenv(), parse(text = paste0('data_bf_', model, ' <- data')))
## }
## #}
##
## # fscoresMakeR()
## # fscoresMakeR_bf()
##
## for (m in 1:length(factorModelNameVector)) {
## eval(envir = globalenv(), parse(text = paste0('data_bcfa_', factorModelNameVector[m], ' <- data.frame(dvs, data_bcfa_', factorModelNameVector[m], ')')))
## }
## rm(fscores)
##
## for (m in 1:length(factorModelNameVector)) {
## eval(envir = globalenv(), parse(text = paste0('data_bf_', factorModelNameVector[m], ' <- data.frame(dvs, data_bf_', factorModelNameVector[m], ')')))
## }
## rm(fscores_bf)
##
## #### Frequentist CFA FScores ####
## filepathcfa <- gsub(x = paste0(getwd(), '/FScores_N3_'), pattern = '/', replacement = '//')
## for (m in 1:length(factorModelNameVector)) {
## model <- factorModelNameVector[m]
## factors <- modelFactorNamesList[[model]] # factors in structural model
## factors_se <- rep(paste0(modelFactorNamesList[[model]], '_se'))
## for(f in 1:length(factors)) c(factors[f], factors_se[f])
## filePath <- paste0(model, '.csv')
## eval(parse(text = paste0('fscores_', model, ' <- read.delim(file = \"', filepathcfa, filePath, '\", header = FALSE, sep = \'\', na.strings = \'*\')')))
## fscores <- eval(parse(text = paste0('fscores_', model))) # Mplus fscores output
## fscores <- fscores[, -c(1:20)] # take out symptom columns
## fscores <- fscores[, c(seq(from = 1, to = dim(fscores)[2], by = 2))] # drop factor SE cols; leave just factors scores
## colnames(fscores) <- factors
## eval(envir = globalenv(), parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- data.frame(dvs , fscores)')))
## }
##
## #### Write FScores to csv ####
## for (m in 1:length(factorModelNameVector)) {
## eval(parse(text = paste0('write.csv(data_bcfa_', factorModelNameVector[m], ', \'data_bcfa_', factorModelNameVector[m], '.csv\')')))
## }
## for (m in 1:length(factorModelNameVector)) {
## eval(parse(text = paste0('write.csv(data_bf_', factorModelNameVector[m], ', \'data_bf_', factorModelNameVector[m], '.csv\')')))
## }
## for (m in 1:length(factorModelNameVector)) {
## dims <- (20 + 2*length(modelFactorNamesList[[m]]))
## cols <- c(1:dims)
## eval(parse(text = paste0('write.csv(data_cfa_', factorModelNameVector[m], ', \'data_cfa_', factorModelNameVector[m], '.csv\', col.names = cols)')))
## }
##
## ----Summary Tables, include=FALSE--------------------------------------------
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data_cfa_', factorModelNameVector[m], ' <- read.csv(\'data_cfa_', factorModelNameVector[m], '.csv\')')))
}
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data_bcfa_', factorModelNameVector[m], ' <- read.csv(\'data_bcfa_', factorModelNameVector[m], '.csv\')')))
}
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data_bf_', factorModelNameVector[m], ' <- read.csv(\'data_bf_', factorModelNameVector[m], '.csv\')')))
}
## ----Print Results, echo=FALSE------------------------------------------------
for (y in 1:length(drinking_outcomes)) {
dv <- drinking_outcomes[y]
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
dv <- paste0('data_cfa_', factorModelNameVector[m], '$', dv)
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_cfa_', factorModelNameVector[m], '_results <- printResults(analysis = \'cfa\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----SF12 Summary Tables, include=FALSE---------------------------------------
## CFA
cat('Frequentist Confirmatory Factor Analysis SF-12 Summary Table')
for (y in 1:length(sf12_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(sf12_outcomes[y], '_cfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'cfa\', outcomeVar = sf12_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## BCFA
cat('Bayesian Confirmatory Factor Analysis Short Form Health Survey (SF-12) Summary Tables')
for (y in 1:length(sf12_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(sf12_outcomes[y], '_bcfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bcfa\', outcomeVar = sf12_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bifactor Analysis Short Form Health Survey (SF-12) Summary Tables')
## Bi-Factor
for (y in 1:length(sf12_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(sf12_outcomes[y], '_bf_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bf\', outcomeVar = sf12_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----Diagnoses Summary Tables, include=FALSE----------------------------------
cat('Frequentist Confirmatory Factor Analysis and Psychiatric Diagnoses Summary Tables')
for (y in 1:length(dx_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
data <- data.frame(data, diagnoses)
eval(envir = globalenv(), parse(text = paste0(dx_outcomes[y], '_cfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'binomial\', analysis = \'cfa\', outcomeVar = dx_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bayesian Confirmatory Factor Analysis and Psychiatric Diagnoses Summary Tables')
for (y in 1:length(dx_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
data <- data.frame(data, diagnoses)
eval(envir = globalenv(), parse(text = paste0(dx_outcomes[y], '_bcfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'binomial\', analysis = \'bcfa\', outcomeVar = dx_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Biactor Analysis and Psychiatric Diagnoses Summary Tables')
for (y in 1:length(dx_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
data <- data.frame(data, diagnoses)
eval(envir = globalenv(), parse(text = paste0(dx_outcomes[y], '_bf_', factorModelNameVector[m], ' <- summaryTableFx(family = \'binomial\', analysis = \'bf\', outcomeVar = dx_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----Drinking Summary Tables, include=FALSE-----------------------------------
cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Summary Tables')
for (y in 1:length(drinking_outcomes)) {
dv <- drinking_outcomes[y]
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
dv <- paste0('data_cfa_', factorModelNameVector[m], '$', dv)
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_cfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'cfa\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bayesian Confirmatory Factor Analysis and Alcohol Validators Summary Tables')
for (y in 1:length(drinking_outcomes)) {
dv <- drinking_outcomes[y]
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
dv <- paste0('data_bcfa_', factorModelNameVector[m], '$', dv)
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_bcfa_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bcfa\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
cat('Bifactor Analysis and Alcohol Validators Summary Tables')
for (y in 1:length(drinking_outcomes)) {
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0(drinking_outcomes[y], '_bf_', factorModelNameVector[m], ' <- summaryTableFx(family = \'gaussian\', analysis = \'bf\', outcomeVar = drinking_outcomes[', y, '], data = data, modelNum = ', m, ')')))
}
}
## ----Correlation Table Function, include=FALSE--------------------------------
#cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Correlation Tables')
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_cfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0('drinking_corr_cfa_', factorModelNameVector[m], ' <- corrTableFx(analysis = \'cfa\', outcomes = drinking_outcomes, data = data, modelNum = ', m, ')')))
}
#cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Correlation Tables')
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bcfa_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0('drinking_corr_bcfa_', factorModelNameVector[m], ' <- corrTableFx(analysis = \'bcfa\', outcomes = drinking_outcomes, data = data, modelNum = ', m, ')')))
}
#cat('Frequentist Confirmatory Factor Analysis and Alcohol Validators Correlation Tables')
for (m in 1:length(factorModelNameVector)) {
eval(parse(text = paste0('data <- data_bf_', factorModelNameVector[m])))
eval(envir = globalenv(), parse(text = paste0('drinking_corr_bf_', factorModelNameVector[m], ' <- corrTableFx(analysis = \'bf\', outcomes = drinking_outcomes, data = data, modelNum = ', m, ')')))
}
## ----SF12 Combined Tables, echo=FALSE, message=FALSE, warning=FALSE, paged.print=TRUE----
combinedLaTeXtableR(analysis = 'cfa',
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
outcomes = sf12_outcomes, outcomeNameVector = outcomeNameVector,
basic = TRUE, stars = TRUE)
combinedLaTeXtableR(analysis = 'bcfa',
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
outcomes = sf12_outcomes, outcomeNameVector = outcomeNameVector,
basic = TRUE, stars = TRUE)
combinedLaTeXtableR(analysis = 'bf',
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
outcomes = sf12_outcomes, outcomeNameVector = outcomeNameVector,
basic = TRUE, stars = TRUE)
bodyBuildeR(mat = c('gh_cfa_dsm5', 'depressed_cfa_dsm5'), matModelName = 'dsm5', multiOutcome = TRUE, outcomeNames = c('General Health', 'Depressed'))
## ----Diagnoses Combined Tables, echo=FALSE, message=FALSE, warning=FALSE, paged.print=TRUE----
combinedLaTeXtableR(analysis = 'cfa',
outcomes = dx_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|z|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bcfa',
outcomes = dx_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|z|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bf', outcomes = dx_outcomes,
outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|z|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
## ----Drinking Combined Tables, echo=FALSE, message=FALSE, warning=FALSE, paged.print=TRUE----
combinedLaTeXtableR(analysis = 'cfa',
outcomes = drinking_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bcfa',
outcomes = drinking_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
combinedLaTeXtableR(analysis = 'bf',
outcomes = drinking_outcomes, outcomeNameVector = outcomeNameVector,
stats = c('Estimate', 'Pr(>|t|)'), statsNames = c('Estimate', 'P'),
stars = TRUE, basic = TRUE)
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