simsem: plotCutoffNonNested – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

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.

Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com)

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)

## End(Not run)

simsem

SIMulated Structural Equation Modeling

v0.5-16

GPL (>= 2)

Authors

Sunthud Pornprasertmanit [aut], Patrick Miller [aut], Alexander Schoemann [aut] (<https://orcid.org/0000-0002-8479-8798>), Terrence D. Jorgensen [aut, cre] (<https://orcid.org/0000-0001-5111-6773>), Corbin Quick [ctb]

Initial release

2021-03-28