Plot sampling distributions of the differences in fit indices between nested models with fit indices cutoffs
This function will plot sampling distributions of the differences in fit indices between nested models if the nested model is true. The users may add cutoffs by specifying the alpha
level.
plotCutoffNested(nested, parent, alpha = 0.05, cutoff = NULL, usedFit = NULL, useContour = T)
nested |
|
parent |
|
alpha |
A priori alpha level |
cutoff |
A priori cutoffs for fit indices, saved in a vector |
usedFit |
Vector of names of fit indices that researchers wish to plot the sampling distribution. |
useContour |
If there are two of sample size, percent completely at random, and percent missing at random are varying, the |
NONE. Only plot the fit indices distributions.
Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult
for simResult that used in this function.
getCutoffNested
to find the difference in fit indices cutoffs
## Not run: # Nested model: One factor loading.null <- matrix(0, 6, 1) loading.null[1:6, 1] <- NA LY.NULL <- bind(loading.null, 0.7) RPS.NULL <- binds(diag(1)) RTE <- binds(diag(6)) CFA.Model.NULL <- model(LY = LY.NULL, RPS = RPS.NULL, RTE = RTE, modelType="CFA") # Parent model: two factors loading.alt <- matrix(0, 6, 2) loading.alt[1:3, 1] <- NA loading.alt[4:6, 2] <- NA LY.ALT <- bind(loading.alt, 0.7) latent.cor.alt <- matrix(NA, 2, 2) diag(latent.cor.alt) <- 1 RPS.ALT <- binds(latent.cor.alt, "runif(1, 0.7, 0.9)") CFA.Model.ALT <- model(LY = LY.ALT, RPS = RPS.ALT, RTE = RTE, modelType="CFA") # The actual number of replications should be greater than 10. Output.NULL.NULL <- sim(10, n=500, model=CFA.Model.NULL) Output.NULL.ALT <- sim(10, n=500, model=CFA.Model.ALT, generate=CFA.Model.NULL) # Plot the cutoffs in nested model comparison plotCutoffNested(Output.NULL.NULL, Output.NULL.ALT, alpha=0.05) ## End(Not run)
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