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# getPowerFitNested: This function will find a power of each fit index in
# nested model comparison based on specified cutoffs of each fit index
getPowerFitNonNested <- function(dat2Mod1, dat2Mod2, cutoff = NULL, dat1Mod1 = NULL, dat1Mod2 = NULL, revDirec = FALSE, usedFit = NULL, alpha = 0.05, nVal = NULL, pmMCARval = NULL, pmMARval = NULL, condCutoff = TRUE, df = 0, onetailed = FALSE) {
result <- NULL
if(is.null(cutoff)) {
if(!is.null(dat1Mod1) & !is.null(dat1Mod2)) {
result <- getPowerFitNonNestedNullObj(dat2Mod1 = dat2Mod1, dat2Mod2 = dat2Mod2, dat1Mod1 = dat1Mod1, dat1Mod2 = dat1Mod2, usedFit = usedFit, alpha = alpha, revDirec = revDirec, nVal = nVal, pmMCARval = pmMCARval, pmMARval = pmMARval, df = df, onetailed = onetailed)
} else {
stop("Please specify fit index cutoff, 'cutoff', or the result object representing the null model, 'nullObject'.")
}
} else {
if(is.null(dat1Mod1) & is.null(dat1Mod2)) {
result <- getPowerFitNonNestedCutoff(dat2Mod1 = dat2Mod1, dat2Mod2 = dat2Mod2, cutoff = cutoff, usedFit = usedFit, revDirec = revDirec, nVal = nVal, pmMCARval = pmMCARval, pmMARval = pmMARval, condCutoff = condCutoff, df = df)
} else {
stop("Please specify either fit index cutoff, 'cutoff', or the result object representing the null model, 'nullObject', but not both.")
}
}
result
}
getPowerFitNonNestedCutoff <- function(dat2Mod1, dat2Mod2, cutoff, usedFit = NULL, revDirec = FALSE, nVal = NULL, pmMCARval = NULL, pmMARval = NULL, condCutoff = TRUE, df = 0) {
getPowerFitNested(altNested = dat2Mod1, altParent = dat2Mod2, cutoff = cutoff,
revDirec = revDirec, usedFit = usedFit, nVal = nVal, pmMCARval = pmMCARval,
pmMARval = pmMARval, condCutoff = condCutoff, df = df)
}
getPowerFitNonNestedNullObj <- function(dat2Mod1, dat2Mod2, dat1Mod1, dat1Mod2, usedFit = NULL, alpha = 0.05, revDirec = FALSE, nVal = NULL, pmMCARval = NULL, pmMARval = NULL, df = 0, onetailed = FALSE) {
usedFit <- cleanUsedFit(usedFit, colnames(dat2Mod1@fit), colnames(dat2Mod2@fit), colnames(dat1Mod1@fit), colnames(dat1Mod2@fit))
mod1 <- clean(dat2Mod1, dat2Mod2)
dat2Mod1 <- mod1[[1]]
dat2Mod2 <- mod1[[2]]
mod2 <- clean(dat1Mod1, dat1Mod2)
dat1Mod1 <- mod2[[1]]
dat1Mod2 <- mod2[[2]]
if (!isTRUE(all.equal(unique(dat2Mod1@paramValue), unique(dat2Mod2@paramValue))))
stop("'dat2Mod1' and 'dat2Mod2' are based on different data and cannot be compared, check your random seed")
if (!isTRUE(all.equal(unique(dat1Mod1@paramValue), unique(dat1Mod2@paramValue))))
stop("'dat1Mod1' and 'dat1Mod2' are based on different data and cannot be compared, check your random seed")
if (!multipleAllEqual(unique(dat2Mod1@n), unique(dat2Mod2@n), unique(dat1Mod1@n),
unique(dat1Mod2@n)))
stop("Models are based on different values of sample sizes")
if (!multipleAllEqual(unique(dat2Mod1@pmMCAR), unique(dat2Mod2@pmMCAR), unique(dat1Mod1@pmMCAR),
unique(dat1Mod2@pmMCAR)))
stop("Models are based on different values of the percent completely missing at random")
if (!multipleAllEqual(unique(dat2Mod1@pmMAR), unique(dat2Mod2@pmMAR), unique(dat1Mod1@pmMAR),
unique(dat1Mod2@pmMAR)))
stop("Models are based on different values of the percent missing at random")
if (is.null(nVal) || is.na(nVal))
nVal <- NULL
if (is.null(pmMCARval) || is.na(pmMCARval))
pmMCARval <- NULL
if (is.null(pmMARval) || is.na(pmMARval))
pmMARval <- NULL
condition <- c(length(unique(dat2Mod1@pmMCAR)) > 1, length(unique(dat2Mod1@pmMAR)) >
1, length(unique(dat2Mod1@n)) > 1)
condValue <- cbind(dat2Mod1@pmMCAR, dat2Mod1@pmMAR, dat2Mod1@n)
colnames(condValue) <- c("Percent MCAR", "Percent MAR", "N")
condValue <- condValue[, condition]
if (is.null(condValue) || length(condValue) == 0)
condValue <- NULL
predictorVal <- rep(NA, 3)
if (condition[3]) {
ifelse(is.null(nVal), stop("Please specify the sample size value, 'nVal', because the sample size in the result object is varying"),
predictorVal[3] <- nVal)
}
if (condition[1]) {
ifelse(is.null(pmMCARval), stop("Please specify the percent of completely missing at random, 'pmMCARval', because the percent of completely missing at random in the result object is varying"),
predictorVal[1] <- pmMCARval)
}
if (condition[2]) {
ifelse(is.null(pmMARval), stop("Please specify the percent of missing at random, 'pmMARval', because the percent of missing at random in the result object is varying"),
predictorVal[2] <- pmMARval)
}
predictorVal <- predictorVal[condition]
usedDirec <- (usedFit %in% getKeywords()$reversedFit) # CFA --> TRUE, RMSEA --> FALSE
if (revDirec)
usedDirec <- !usedDirec
usedDirecInverse <- !usedDirec
Data1 <- as.data.frame((dat1Mod1@fit - dat1Mod2@fit)[, usedFit])
Data2 <- as.data.frame((dat2Mod1@fit - dat2Mod2@fit)[, usedFit])
cut1Data1 <- alpha
cut2Data1 <- NULL
cut1Data2 <- 1 - alpha
cut2Data2 <- NULL
if (onetailed == FALSE) {
lower <- alpha/2
upper <- 1 - (alpha/2)
cut1Data1 <- lower
cut2Data1 <- upper
cut1Data2 <- upper
cut2Data2 <- lower
}
cutoff1 <- list()
cutoff1[[1]] <- getCutoffDataFrame(Data1, cut1Data1, FALSE, usedFit, predictor = condValue,
predictorVal = "all", df = df)
if (!is.null(cut2Data1)) {
cutoff1[[2]] <- getCutoffDataFrame(Data1, cut2Data1, FALSE, usedFit, predictor = condValue,
predictorVal = "all", df = df)
}
cutoff2 <- list()
cutoff2[[1]] <- getCutoffDataFrame(Data2, cut1Data2, FALSE, usedFit, predictor = condValue,
predictorVal = "all", df = df)
if (!is.null(cut2Data2)) {
cutoff2[[2]] <- getCutoffDataFrame(Data2, cut2Data2, FALSE, usedFit, predictor = condValue,
predictorVal = "all", df = df)
}
power1 <- rep(NA, length(usedFit))
power2 <- rep(NA, length(usedFit))
if (is.null(condValue)) {
power2 <- pValueDataFrame(as.numeric(cutoff1[[1]]), Data2, revDirec = usedDirec)
power1 <- pValueDataFrame(as.numeric(cutoff2[[1]]), Data1, revDirec = !usedDirec)
if (onetailed == FALSE) {
power2 <- power2 + pValueDataFrame(as.numeric(cutoff1[[2]]), Data2, revDirec = !usedDirec)
power1 <- power1 + pValueDataFrame(as.numeric(cutoff2[[2]]), Data1, revDirec = usedDirec)
}
} else {
for (i in 1:length(power2)) {
power2[i] <- pValueVariedCutoff(cutoff1[[1]][, i], Data2[, i], revDirec = usedDirec[i],
x = condValue, xval = predictorVal)
if (onetailed == FALSE) {
power2[i] <- power2[i] + pValueVariedCutoff(cutoff1[[2]][, i], Data2[,
i], revDirec = !usedDirec[i], x = condValue, xval = predictorVal)
}
}
for (i in 1:length(power1)) {
power1[i] <- pValueVariedCutoff(cutoff2[[1]][, i], Data1[, i], revDirec = !usedDirec[i],
x = condValue, xval = predictorVal)
if (onetailed == FALSE) {
power1[i] <- power1[i] + pValueVariedCutoff(cutoff2[[2]][, i], Data1[,
i], revDirec = usedDirec[i], x = condValue, xval = predictorVal)
}
}
}
names(power1) <- usedFit
names(power2) <- usedFit
return(list(reject1FromNull2 = power1, reject2FromNull1 = power2))
}
# \title{
# Sort two objects in a list
# }
# \description{
# Sort two objects in a list by swapping the values of both objects so that the first object contains the lower value and the second object contains the larger value
# }
# \usage{
# sortList(object)
# }
# \arguments{
# \item{object}{
# The list with two objects (e.g., vector, matrix)
# }
# }
# \value{
# The sorted list
# }
sortList <- function(object) {
object1 <- object[[1]]
object2 <- object[[2]]
result1 <- object[[1]]
result2 <- object[[2]]
temp <- object2 < object1
result2[temp] <- object1[temp]
result1[temp] <- object2[temp]
return(list(result1, result2))
}
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