simsem: getCutoffNonNested – R documentation

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getCutoffNonNested

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

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

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)

Examples

## 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)

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