# Find fit indices cutoff for non-nested model comparison given a priori alpha level Extract fit indices information from the simulation of two models fitting on the datasets created from both models and getCutoff of fit indices given a priori alpha level ```r getCutoffNonNested(dat1Mod1, dat1Mod2, dat2Mod1=NULL, dat2Mod2=NULL, alpha=.05, usedFit=NULL, onetailed=FALSE, nVal = NULL, pmMCARval = NULL, pmMARval = NULL, df = 0) ``` ## 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 - `usedFit`: Vector of names of fit indices that researchers wish to get cutoffs from. The default is to get cutoffs of all fit indices. - `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. - `nVal`: The sample size value that researchers wish to find the fit indices cutoffs from. - `pmMCARval`: The percent missing completely at random value that researchers wish to find the fit indices cutoffs from. - `pmMARval`: The percent missing at random value that researchers wish to find the fit indices cutoffs from. - `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. ## Returns One- or two-tailed cutoffs of several fit indices with a priori alpha level. The cutoff is based on the fit indices from Model 1 subtracted by the fit indices from Model 2. ## Author(s) Sunthud Pornprasertmanit (psunthud@gmail.com ) ## See Also `SimResult` for a detail of simResult `getCutoff` for a detail of finding cutoffs for absolute fit `getCutoffNested` for a detail of finding cutoffs for nested model comparison `plotCutoffNonNested` Plot cutoffs for non-nested model comparison ## Examples ```r ## Not run: # Model A: Factor 1 with items 1-3 and Factor 2 with 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 with items 1-4 and Factor 2 with 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) # Find the cutoffs from the sampling distribution to reject model A (model 1) # and to reject model B (model 2) getCutoffNonNested(Output.A.A, Output.A.B, Output.B.A, Output.B.B) # Find the cutoffs from the sampling distribution to reject model A (model 1) getCutoffNonNested(Output.A.A, Output.A.B) # Find the cutoffs from the sampling distribution to reject model B (model 1) getCutoffNonNested(Output.B.B, Output.B.A) ## End(Not run) ```

getCutoffNonNested function