simsem (version 0.5-16)

plotPowerFitNested: Plot power of rejecting a nested model in a nested model comparison by each fit index

Description

This function will plot sampling distributions of the differences in fit indices between parent and nested models. Two sampling distributions will be compared: nested model is FALSE (alternative model) and nested model is TRUE (null model).

Usage

plotPowerFitNested(altNested, altParent, nullNested = NULL, 
nullParent = NULL, cutoff = NULL, usedFit = NULL, alpha = 0.05, 
contN = TRUE, contMCAR = TRUE, contMAR = TRUE, useContour = TRUE, 
logistic = TRUE)

Arguments

altNested

'>SimResult that saves the simulation result of the nested model when the nested model is FALSE.

altParent

'>SimResult that saves the simulation result of the parent model when the nested model is FALSE.

nullNested

'>SimResult that saves the simulation result of the nested model when the nested model is TRUE. This argument may not be specified if the cutoff is specified.

nullParent

'>SimResult that saves the simulation result of the parent model when the nested model is TRUE. This argument may not be specified if the cutoff is specified.

cutoff

A vector of priori cutoffs for the differences in 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, perspective plot is used.

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 overlaying scatterplot with a line of cutoff is plotted.

Value

NONE. Only plot the fit indices distributions.

See Also

• '>SimResult for simResult that used in this function.

• getCutoffNested to find the cutoffs of the differences in fit indices

• plotCutoffNested to visualize the cutoffs of the differences in fit indices

• getPowerFitNested to find the power in rejecting the nested model by the difference in fit indices cutoffs

Examples

# NOT RUN {
# Null model: One-factor model
loading.null <- matrix(0, 6, 1)
loading.null[1:6, 1] <- NA
LY.NULL <- bind(loading.null, 0.7)
RPS.NULL <- binds(diag(1))
RTE <- binds(diag(6))
CFA.Model.NULL <- model(LY = LY.NULL, RPS = RPS.NULL, RTE = RTE, modelType="CFA")

# Alternative model: Two-factor model
loading.alt <- matrix(0, 6, 2)
loading.alt[1:3, 1] <- NA
loading.alt[4:6, 2] <- NA
LY.ALT <- bind(loading.alt, 0.7)
latent.cor.alt <- matrix(NA, 2, 2)
diag(latent.cor.alt) <- 1
RPS.ALT <- binds(latent.cor.alt, 0.7)
CFA.Model.ALT <- model(LY = LY.ALT, RPS = RPS.ALT, RTE = RTE, modelType="CFA")

# In reality, more than 10 replications are needed
Output.NULL.NULL <- sim(10, n=500, model=CFA.Model.NULL, generate=CFA.Model.NULL) 
Output.ALT.NULL <- sim(10, n=500, model=CFA.Model.NULL, generate=CFA.Model.ALT) 
Output.NULL.ALT <- sim(10, n=500, model=CFA.Model.ALT, generate=CFA.Model.NULL) 
Output.ALT.ALT <- sim(10, n=500, model=CFA.Model.ALT, generate=CFA.Model.ALT) 

# Plot the power based on the derived cutoff from the models analyzed on the null datasets
plotPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL, 
	nullParent=Output.NULL.ALT)

# Plot the power by only CFI
plotPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL, 
	nullParent=Output.NULL.ALT, usedFit="CFI")

# The example of continous varying sample size. Note that more fine-grained 
# values of n is needed, e.g., n=seq(50, 500, 1)
Output.NULL.NULL2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.NULL, generate=CFA.Model.NULL) 
Output.ALT.NULL2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.NULL, generate=CFA.Model.ALT) 
Output.NULL.ALT2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.ALT, generate=CFA.Model.NULL) 
Output.ALT.ALT2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.ALT, generate=CFA.Model.ALT) 

# Plot logistic line for the power based on the derived cutoff from the null model 
# along sample size values
plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, nullNested=Output.NULL.NULL2, 
	nullParent=Output.NULL.ALT2)

# Plot scatterplot for the power based on the derived cutoff from the null model 
# along sample size values
plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, nullNested=Output.NULL.NULL2, 
	nullParent=Output.NULL.ALT2, logistic=FALSE)

# Plot scatterplot for the power based on the advanced CFI value
plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, cutoff=c(CFI=-0.1), logistic=FALSE)
# }