Find fit indices cutoff given a priori alpha level
Extract fit indices information from the SimResult and get the cutoffs of fit indices given a priori alpha level
getCutoff(object, alpha, revDirec = FALSE, usedFit = NULL, nVal = NULL, pmMCARval = NULL, pmMARval = NULL, df = 0)
object |
|
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 |
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 |
pmMCARval |
The percent missing completely at random value that researchers wish to find the fit indices cutoffs from. This argument is applicable for |
pmMARval |
The percent missing at random value that researchers wish to find the fit indices cutoffs from. This argument is applicable for |
df |
The degree of freedom used in spline method in predicting the fit indices by the predictors. If |
One-tailed cutoffs of several fit indices with a priori alpha level
Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult for a detail of simResult
## 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) ## End(Not run)
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