Find fit indices cutoff for non-nested model comparison given a priori alpha level

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Description

Extract fit indices information from the simulation of two models fitting on the datasets created from both models and getCutoff of fit indices given a priori alpha level

Usage

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getCutoffNonNested(dat1Mod1, dat1Mod2, dat2Mod1=NULL, dat2Mod2=NULL, 
alpha=.05, usedFit=NULL, onetailed=FALSE, nVal = NULL, pmMCARval = NULL, 
pmMARval = NULL, df = 0)

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

usedFit

Vector of names of fit indices that researchers wish to get cutoffs from. The default is to get cutoffs of all fit indices.

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.

nVal

The sample size value that researchers wish to find the fit indices cutoffs from.

pmMCARval

The percent missing completely at random value that researchers wish to find the fit indices cutoffs from.

pmMARval

The percent missing at random value that researchers wish to find the fit indices cutoffs from.

df

The degree of freedom used in spline method in predicting the fit indices by the predictors. If df is 0, the spline method will not be applied.

Value

One- or two-tailed cutoffs of several fit indices with a priori alpha level. The cutoff is based on the fit indices from Model 1 subtracted by the fit indices from Model 2.

Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com)

See Also

SimResult for a detail of simResult getCutoff for a detail of finding cutoffs for absolute fit getCutoffNested for a detail of finding cutoffs for nested model comparison plotCutoffNonNested Plot cutoffs for non-nested model comparison

Examples

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## Not run: 
# Model A: Factor 1 with items 1-3 and Factor 2 with 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 with items 1-4 and Factor 2 with 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)

# Find the cutoffs from the sampling distribution to reject model A (model 1)
# and to reject model B (model 2)
getCutoffNonNested(Output.A.A, Output.A.B, Output.B.A, Output.B.B)

# Find the cutoffs from the sampling distribution to reject model A (model 1)
getCutoffNonNested(Output.A.A, Output.A.B)

# Find the cutoffs from the sampling distribution to reject model B (model 1)
getCutoffNonNested(Output.B.B, Output.B.A)

## End(Not run)

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