R/plotPowerFitNested.R

In simsem: SIMulated Structural Equation Modeling

# plotPowerFitNested: This function will plot sampling distributions of
# difference in fit indices that visualize power

plotPowerFitNested <- function(altNested, altParent, nullNested = NULL, nullParent = NULL, 
    cutoff = NULL, usedFit = NULL, alpha = 0.05, contN = TRUE, contMCAR = TRUE, contMAR = TRUE, 
    useContour = TRUE, logistic = TRUE) {
    if (is.null(nullNested) & is.null(nullParent)) {
        mod <- clean(altNested, altParent)
        altNested <- mod[[1]]
        altParent <- mod[[2]]
        if (!isTRUE(all.equal(unique(altNested@paramValue), unique(altParent@paramValue)))) 
            stop("Models are based on different data and cannot be compared, check your random seed")
        if (!isTRUE(all.equal(unique(altNested@n), unique(altParent@n)))) 
            stop("Models are based on different values of sample sizes")
        if (!isTRUE(all.equal(unique(altNested@pmMCAR), unique(altParent@pmMCAR)))) 
            stop("Models are based on different values of the percent completely missing at random")
        if (!isTRUE(all.equal(unique(altNested@pmMAR), unique(altParent@pmMAR)))) 
            stop("Models are based on different values of the percent missing at random")
    } else if (!is.null(nullNested) & !is.null(nullParent)) {
        mod <- clean(altNested, altParent, nullNested, nullParent)
        altNested <- mod[[1]]
        altParent <- mod[[2]]
        nullNested <- mod[[3]]
        nullParent <- mod[[4]]
        if (!isTRUE(all.equal(unique(altNested@paramValue), unique(altParent@paramValue)))) 
            stop("'altNested' and 'altParent' are based on different data and cannot be compared, check your random seed")
        if (!isTRUE(all.equal(unique(nullNested@paramValue), unique(nullParent@paramValue)))) 
            stop("'nullNested' and 'nullParent' are based on different data and cannot be compared, check your random seed")
        if (!multipleAllEqual(unique(altNested@n), unique(altParent@n), unique(nullNested@n), 
            unique(nullParent@n))) 
            stop("Models are based on different values of sample sizes")
        if (!multipleAllEqual(unique(altNested@pmMCAR), unique(altParent@pmMCAR), 
            unique(nullNested@pmMCAR), unique(nullParent@pmMCAR))) 
            stop("Models are based on different values of the percent completely missing at random")
        if (!multipleAllEqual(unique(altNested@pmMAR), unique(altParent@pmMAR), unique(nullNested@pmMAR), 
            unique(nullParent@pmMAR))) 
            stop("Models are based on different values of the percent missing at random")
    } else {
        stop("The nullNested and nullParent arguments should be both specified.")
    }
    nrep <- dim(altNested@fit)[[1]]
	usedFit <- cleanUsedFit(usedFit, colnames(altNested@fit), colnames(altParent@fit))
    if (!is.null(cutoff)) {
		names(cutoff) <- cleanUsedFit(names(cutoff))
        usedFit <- intersect(usedFit, names(cutoff))
        cutoff <- cutoff[usedFit]
    }
    # Create matrix of predictors (randomly varying params)
    x <- NULL
    pred <- NULL
    
    if ((length(unique(altNested@n)) > 1) && contN) {
        if (!length(altNested@n) == nrep) {
            stop("Number of random sample sizes is not the same as the number of replications, check to see if N varied across replications")
        }
        x <- cbind(x, N = altNested@n)
        pred$N <- min(altNested@n):max(altNested@n)
    }
    if ((length(unique(altNested@pmMCAR)) > 1) && contMCAR) {
        if (!length(altNested@pmMCAR) == nrep) {
            stop("Number of random pmMCARs is not the same as the number of replications, check to see if pmMCAR varied across replications")
        }
        x <- cbind(x, pmMCAR = altNested@pmMCAR)
        pred$MCAR <- seq(min(altNested@pmMCAR), max(altNested@pmMCAR), by = 0.01)
        
    }
    if ((length(unique(altNested@pmMAR)) > 1) && contMAR) {
        if (!length(altNested@pmMAR) == nrep) {
            stop("Number of random pmMARs is not the same as the number of replications, check to see if pmMAR varied across replications")
        }
        x <- cbind(x, pmMAR = altNested@pmMAR)
        pred$MAR <- seq(min(altNested@pmMAR), max(altNested@pmMAR), by = 0.01)
        
    }
    nullMod <- NULL
    altMod <- as.data.frame(altNested@fit - altParent@fit)
    if (!is.null(nullNested)) 
        nullMod <- as.data.frame(nullNested@fit - nullParent@fit)
    plotPowerFitDf(altMod, nullObject = nullMod, cutoff = cutoff, usedFit = usedFit, 
        alpha = alpha, x = x, xval = pred, useContour = useContour, logistic = logistic)
}