simsem (version 0.2-8)

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, perspect

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

• SimResultfor simResult that used in this function.
• getCutoffNonNestedto find the difference in fit indices cutoffs for non-nested model comparison

Examples

n1 <- simNorm(0, 0.1)
u79 <- simUnif(0.7, 0.9)

loading.A <- matrix(0, 8, 2)
loading.A[1:3, 1] <- NA
loading.A[4:8, 2] <- NA
LX.A <- simMatrix(loading.A, 0.7)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPH <- symMatrix(latent.cor, "u79")
RTD <- symMatrix(diag(8))
CFA.Model.A <- simSetCFA(LY = LX.A, RPS = RPH, RTE = RTD)

error.cor.mis <- matrix(NA, 8, 8)
diag(error.cor.mis) <- 1
RTD.Mis <- symMatrix(error.cor.mis, "n1")
CFA.Model.A.Mis <- simMisspecCFA(RTE = RTD.Mis)

loading.B <- matrix(0, 8, 2)
loading.B[1:4, 1] <- NA
loading.B[5:8, 2] <- NA
LX.B <- simMatrix(loading.B, 0.7)
CFA.Model.B <- simSetCFA(LY = LX.B, RPS = RPH, RTE = RTD)

SimData.A <- simData(CFA.Model.A, 500)
SimData.B <- simData(CFA.Model.B, 500)

SimModel.A <- simModel(CFA.Model.A)
SimModel.B <- simModel(CFA.Model.B)

# The actual number of replications should be greater than 10.
Output.A.A <- simResult(10, SimData.A, SimModel.A)
Output.A.B <- simResult(10, SimData.A, SimModel.B)
Output.B.A <- simResult(10, SimData.B, SimModel.A)
Output.B.B <- simResult(10, SimData.B, SimModel.B)

plotCutoffNonNested(Output.A.A, Output.A.B, Output.B.A, Output.B.B)
plotCutoffNonNested(Output.A.A, Output.A.B)
plotCutoffNonNested(Output.A.A, Output.A.B, onetailed=TRUE)