getCutoffNonNested: Find fit indices cutoff for non-nested model comparison given...
In simsem: SIMulated Structural Equation Modeling

Description Usage Arguments Value Author(s) See Also Examples

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

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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)

`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.

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.

Sunthud Pornprasertmanit (psunthud@gmail.com)

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

## 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)