simsem (version 0.5-16)

getCutoff: Find fit indices cutoff given a priori alpha level

Description

Extract fit indices information from the '>SimResult and get the cutoffs of fit indices given a priori alpha level

Usage

getCutoff(object, alpha, revDirec = FALSE, usedFit = NULL, nVal = NULL, 
	pmMCARval = NULL, pmMARval = NULL, df = 0)

Arguments

object

'>SimResult that saves the analysis results from multiple replications

alpha

A priori alpha level

revDirec

The default is to find criticl point on the side that indicates worse fit (the right side of RMSEA or the left side of CFI). If specifying as TRUE, the directions are reversed.

usedFit

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

nVal

The sample size value that researchers wish to find the fit indices cutoffs from. This argument is applicable for '>SimResult with varying sample sizes or percent missing across replications

pmMCARval

The percent missing completely at random value that researchers wish to find the fit indices cutoffs from. This argument is applicable for '>SimResult with varying sample sizes or percent missing across replications

pmMARval

The percent missing at random value that researchers wish to find the fit indices cutoffs from. This argument is applicable for '>SimResult with varying sample sizes or percent missing across replications

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. This argument is applicable for '>SimResult with varying sample sizes or percent missing across replications

Value

One-tailed cutoffs of several fit indices with a priori alpha level

See Also

'>SimResult for a detail of simResult

Examples

# NOT RUN {
loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loadingValues <- matrix(0, 6, 2)
loadingValues[1:3, 1] <- 0.7
loadingValues[4:6, 2] <- 0.7
LY <- bind(loading, loadingValues)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, 0.5)
error.cor <- matrix(0, 6, 6)
diag(error.cor) <- 1
RTE <- binds(error.cor)
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA")

# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
Output <- sim(5, n = 200, model=CFA.Model)

# Get the cutoff (critical value) when alpha is 0.05
getCutoff(Output, 0.05)

# Finding the cutoff when the sample size is varied. Note that more fine-grained 
# values of n is needed, e.g., n=seq(50, 500, 1)
Output2 <- sim(NULL, model=CFA.Model, n=seq(50, 100, 10))

# Get the fit index cutoff when sample size is 75.
getCutoff(Output2, 0.05, nVal = 75)
# }