R/summaryShort-methods.R

In simsem: SIMulated Structural Equation Modeling

# summaryShort: Provide short summary if it is available. Otherwise, it is an
# alias for summary.

setMethod("summaryShort", signature = "ANY", definition = function(object) {
    summary(object)
})

setMethod("summaryShort", signature = "SimMatrix", definition = function(object) {
    Data <- object@free
    Labels <- object@popParam
	if(!(all(dim(Labels) == 1) && is.nan(Labels))) {
		Labels[!is.free(Data)] <- as.character(Data[!is.free(Data)])
		Labels[is.free(Data)] <- paste(Data[is.free(Data)], Labels[is.free(Data)], sep = ":")
	} else {
		Labels <- Data
	}
	Mis <- object@misspec
    if (!(all(dim(Mis) == 1) && is.nan(Mis))) {
        Labels <- matrix(paste0(Labels, "+", Mis), nrow(Labels), ncol(Labels))
    }
    print(Labels)
})

setMethod("summaryShort", signature = "SimVector", definition = function(object) {
    Data <- object@free
    Labels <- object@popParam
	if(!(length(Labels) == 1 && is.nan(Labels))) {
		Labels[!is.free(Data)] <- as.character(Data[!is.free(Data)])
		Labels[is.free(Data)] <- paste(Data[is.free(Data)], Labels[is.free(Data)], sep = ":")
	} else {
		Labels <- Data
	}
    Mis <- object@misspec
    if (length(Mis) != 0 && !(length(Mis) == 1 && is.nan(Mis))) {
        Labels <- paste0(Labels, "+", Mis)
    }
    print(Labels)
}) 

setMethod("summaryShort", signature = "SimResult", definition = function(object, alpha=0.05, digits=3) {
    cat("RESULT OBJECT\n")
    cat(paste("Model Type:", print(object@modelType),"\n"))
	if(object@paramOnly) {
		cat("This object contains only real and misspecified parameter values.\nUsers may use 'summaryPopulation', 'summaryMisspec', and 'plotMisfit' functions to investigate the parmeter values.\n")
	} else {
		cleanObj <- clean(object)
		cat(paste("Convergence", sum(object@converged == 0), "/", object@nRep, "\n"))
		if (length(unique(object@n)) > 1) {
			cat(paste("Sample size:", min(object@n), "to", max(object@n),"\n"))
		} else {
			cat(paste("Sample size:", unique(object@n),"\n"))
		}
		if (length(unique(object@pmMCAR)) > 1) {
			cat(paste("Percent Completely Missing at Random:", min(object@pmMCAR), "to", max(object@pmMCAR),"\n"))
		} else {
			cat(paste("Percent Completely Missing at Random:", unique(object@pmMCAR),"\n"))
		}
		if (length(unique(object@pmMAR)) > 1) {
			cat(paste("Percent Missing at Random:", min(object@pmMAR), "to", max(object@pmMAR),"\n"))
		} else {
			cat(paste("Percent Missing at Random:", unique(object@pmMAR),"\n"))
		}
		haveFit <- length(colnames(object@fit)) > 0
		if(haveFit) {
			cat("========= Fit Indices Cutoffs ============\n")
			print(round(summaryFit(object, alpha = alpha), digits))
		}
		if (!is.null(object@paramValue)) {
			targetVar <- match(colnames(object@coef), colnames(object@paramValue))
			targetVar <- targetVar[!is.na(targetVar)]
			targetVar <- colnames(object@paramValue)[targetVar]
			if ((ncol(object@coef) != length(targetVar)) || !all(colnames(object@coef) == targetVar)) 
				cat("NOTE: The data generation model is not the same as the analysis model. See the summary of the population underlying data generation by the summaryPopulation function.\n")
		}
	}
})