# 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. ```r plotCutoffNested(nested, parent, alpha = 0.05, cutoff = NULL, usedFit = NULL, useContour = T) ``` ## Arguments - `nested`: `SimResult` that saves the analysis results of nested model from multiple replications - `parent`: `SimResult` that saves the analysis results of parent model from multiple replications - `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 `plotCutoff` function will provide 3D graph. Contour graph is a default. However, if this is specified as `FALSE`, perspective plot is used. ## Returns NONE. Only plot the fit indices distributions. ## Author(s) Sunthud Pornprasertmanit (psunthud@gmail.com ) ## See Also * `SimResult` for simResult that used in this function. * `getCutoffNested` to find the difference in fit indices cutoffs ## Examples ```r ## 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) ```

plotCutoffNested function