simsem (version 0.2-8)

plotPowerFit: Plot sampling distributions of fit indices that visualize power of rejecting datasets underlying misspecified models

Description

This function will plot sampling distributions of fit indices that visualize power in rejecting the misspecified models

Usage

plotPowerFit(altObject, nullObject = NULL, cutoff = NULL, usedFit = NULL, 
alpha = 0.05, contN = TRUE, contMCAR = TRUE, contMAR = TRUE, 
useContour = TRUE, logistic = TRUE)

Arguments

altObject

The result object (SimResult) saves the simulation result of fitting the hypothesized model when the hypothesized model is FALSE.

nullObject

The result object (SimResult) saves the simulation result of fitting the hypothesized model when the hypothesized model is TRUE. This argument may be not specified if the cutoff is specifie

cutoff

A vector of priori cutoffs for fit indices.

usedFit

Vector of names of fit indices that researchers wish to plot.

alpha

A priori alpha level

contN

Include the varying sample size in the power plot if available

contMCAR

Include the varying MCAR (missing completely at random percentage) in the power plot if available

contMAR

Include the varying MAR (missing at random percentage) in the power plot if available

useContour

If there are two of sample size, percent completely at random, and percent missing at random are varying, the plotCutoff function will provide 3D graph. Contour graph is a default. However, if this is specified as FALSE, perspect

logistic

If logistic is TRUE and the varying parameter exists (e.g., sample size or percent missing), the plot based on logistic regression predicting the significance by the varying parameters is preferred. If FALSE, the ove

Value

• NONE. Only plot the fit indices distributions.

See Also

• SimResultfor simResult that used in this function.
• getCutoffto find values of cutoffs based on null hypothesis sampling distributions only
• getPowerFitto find power of rejecting the hypothesized model when the hypothesized model isFALSE.

Examples

loading.null <- matrix(0, 6, 1)
loading.null[1:6, 1] <- NA
LX.NULL <- simMatrix(loading.null, 0.7)
RPH.NULL <- symMatrix(diag(1))
RTD <- symMatrix(diag(6))
CFA.Model.NULL <- simSetCFA(LY = LX.NULL, RPS = RPH.NULL, RTE = RTD)
SimData.NULL <- simData(CFA.Model.NULL, 500)
SimModel <- simModel(CFA.Model.NULL)
# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
Output.NULL <- simResult(5, SimData.NULL, SimModel)

loading.alt <- matrix(0, 6, 2)
loading.alt[1:3, 1] <- NA
loading.alt[4:6, 2] <- NA
LX.ALT <- simMatrix(loading.alt, 0.7)
latent.cor.alt <- matrix(NA, 2, 2)
diag(latent.cor.alt) <- 1
RPH.ALT <- symMatrix(latent.cor.alt, 0.5)
CFA.Model.ALT <- simSetCFA(LY = LX.ALT, RPS = RPH.ALT, RTE = RTD)
SimData.ALT <- simData(CFA.Model.ALT, 500)
Output.ALT <- simResult(5, SimData.ALT, SimModel)
plotPowerFit(Output.ALT, nullObject=Output.NULL, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))
Rule.of.thumb <- c(RMSEA=0.05, CFI=0.95, TLI=0.95, SRMR=0.06)
plotPowerFit(Output.ALT, cutoff=Rule.of.thumb, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))

Output.NULL2 <- simResult(NULL, SimData.NULL, SimModel, n=seq(50, 250, 25))
Output.ALT2 <- simResult(NULL, SimData.ALT, SimModel, n=seq(50, 250, 25))

plotPowerFit(Output.ALT2, nullObject=Output.NULL2, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))
plotPowerFit(Output.ALT2, cutoff=Rule.of.thumb, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR"))