simsem (version 0.5-16)

plotCutoffNonNested: Plot sampling distributions of the differences in fit indices between non-nested models with fit indices cutoffs

Description

This function will plot sampling distributions of the differences in fit indices between non-nested models. The users may add cutoffs by specifying the alpha level.

Usage

plotCutoffNonNested(dat1Mod1, dat1Mod2, dat2Mod1=NULL, dat2Mod2=NULL, 
alpha=0.05, cutoff = NULL, usedFit = NULL, useContour = T, onetailed=FALSE)

Arguments

dat1Mod1

'>SimResult that saves the simulation of analyzing Model 1 by datasets created from Model 1

dat1Mod2

'>SimResult that saves the simulation of analyzing Model 2 by datasets created from Model 1

dat2Mod1

'>SimResult that saves the simulation of analyzing Model 1 by datasets created from Model 2

dat2Mod2

'>SimResult that saves the simulation of analyzing Model 2 by datasets created from Model 2

alpha

A priori alpha level

cutoff

A priori cutoffs for fit indices, saved in a vector

usedFit

Vector of names of fit indices that researchers wish to plot the sampling distribution.

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.

onetailed

If TRUE, the function will find the cutoff from one-tail test. If FALSE, the funciton will find the cutoff from two-tailed test.

Value

NONE. Only plot the fit indices distributions.

See Also

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

• getCutoffNonNested to find the difference in fit indices cutoffs for non-nested model comparison

Examples

# NOT RUN {
# Model A: Factor 1 on Items 1-3 and Factor 2 on Items 4-8
loading.A <- matrix(0, 8, 2)
loading.A[1:3, 1] <- NA
loading.A[4:8, 2] <- NA
LY.A <- bind(loading.A, 0.7)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, "runif(1, 0.7, 0.9)")
RTE <- binds(diag(8))
CFA.Model.A <- model(LY = LY.A, RPS = RPS, RTE = RTE, modelType="CFA")

# Model B: Factor 1 on Items 1-4 and Factor 2 on Items 5-8
loading.B <- matrix(0, 8, 2)
loading.B[1:4, 1] <- NA
loading.B[5:8, 2] <- NA
LY.B <- bind(loading.B, 0.7)
CFA.Model.B <- model(LY = LY.B, RPS = RPS, RTE = RTE, modelType="CFA")

# The actual number of replications should be greater than 10.
Output.A.A <- sim(10, n=500, model=CFA.Model.A, generate=CFA.Model.A)
Output.A.B <- sim(10, n=500, model=CFA.Model.B, generate=CFA.Model.A)
Output.B.A <- sim(10, n=500, model=CFA.Model.A, generate=CFA.Model.B)
Output.B.B <- sim(10, n=500, model=CFA.Model.B, generate=CFA.Model.B)

# Plot cutoffs for both model A and model B
plotCutoffNonNested(Output.A.A, Output.A.B, Output.B.A, Output.B.B)

# Plot cutoffs for the model A only
plotCutoffNonNested(Output.A.A, Output.A.B)

# Plot cutoffs for the model A with one-tailed test
plotCutoffNonNested(Output.A.A, Output.A.B, onetailed=TRUE)
# }