# Plot sampling distributions of the differences in fit indices between non-nested models with fit indices cutoffs This function will plot sampling distributions of the differences in fit indices between non-nested models. The users may add cutoffs by specifying the `alpha` level. ```r plotCutoffNonNested(dat1Mod1, dat1Mod2, dat2Mod1=NULL, dat2Mod2=NULL, alpha=0.05, cutoff = NULL, usedFit = NULL, useContour = T, onetailed=FALSE) ``` ## 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 - `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. - `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. ## 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. * `getCutoffNonNested` to find the difference in fit indices cutoffs for non-nested model comparison ## Examples ```r ## Not run: # Model A: Factor 1 on Items 1-3 and Factor 2 on 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 on Items 1-4 and Factor 2 on 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) # Plot cutoffs for both model A and model B plotCutoffNonNested(Output.A.A, Output.A.B, Output.B.A, Output.B.B) # Plot cutoffs for the model A only plotCutoffNonNested(Output.A.A, Output.A.B) # Plot cutoffs for the model A with one-tailed test plotCutoffNonNested(Output.A.A, Output.A.B, onetailed=TRUE) ## End(Not run) ```

plotCutoffNonNested function