# Plot power of rejecting a nested model in a nested model comparison by each fit index This function will plot sampling distributions of the differences in fit indices between parent and nested models. Two sampling distributions will be compared: nested model is `FALSE` (alternative model) and nested model is `TRUE` (null model). ```r plotPowerFitNested(altNested, altParent, nullNested = NULL, nullParent = NULL, cutoff = NULL, usedFit = NULL, alpha = 0.05, contN = TRUE, contMCAR = TRUE, contMAR = TRUE, useContour = TRUE, logistic = TRUE) ``` ## Arguments - `altNested`: `SimResult` that saves the simulation result of the nested model when the nested model is `FALSE`. - `altParent`: `SimResult` that saves the simulation result of the parent model when the nested model is `FALSE`. - `nullNested`: `SimResult` that saves the simulation result of the nested model when the nested model is `TRUE`. This argument may not be specified if the `cutoff` is specified. - `nullParent`: `SimResult` that saves the simulation result of the parent model when the nested model is `TRUE`. This argument may not be specified if the `cutoff` is specified. - `cutoff`: A vector of priori cutoffs for the differences in fit indices. - `usedFit`: Vector of names of fit indices that researchers wish to plot. - `alpha`: A priori alpha level - `contN`: Include the varying sample size in the power plot if available - `contMCAR`: Include the varying MCAR (missing completely at random percentage) in the power plot if available - `contMAR`: Include the varying MAR (missing at random percentage) in the power plot if available - `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. - `logistic`: If `logistic` is `TRUE` and the varying parameter exists (e.g., sample size or percent missing), the plot based on logistic regression predicting the significance by the varying parameters is preferred. If `FALSE`, the overlaying scatterplot with a line of cutoff is plotted. ## 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 cutoffs of the differences in fit indices * `plotCutoffNested` to visualize the cutoffs of the differences in fit indices * `getPowerFitNested` to find the power in rejecting the nested model by the difference in fit indices cutoffs ## Examples ```r ## Not run: # Null model: One-factor model 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") # Alternative model: Two-factor model 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, 0.7) CFA.Model.ALT <- model(LY = LY.ALT, RPS = RPS.ALT, RTE = RTE, modelType="CFA") # In reality, more than 10 replications are needed Output.NULL.NULL <- sim(10, n=500, model=CFA.Model.NULL, generate=CFA.Model.NULL) Output.ALT.NULL <- sim(10, n=500, model=CFA.Model.NULL, generate=CFA.Model.ALT) Output.NULL.ALT <- sim(10, n=500, model=CFA.Model.ALT, generate=CFA.Model.NULL) Output.ALT.ALT <- sim(10, n=500, model=CFA.Model.ALT, generate=CFA.Model.ALT) # Plot the power based on the derived cutoff from the models analyzed on the null datasets plotPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL, nullParent=Output.NULL.ALT) # Plot the power by only CFI plotPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL, nullParent=Output.NULL.ALT, usedFit="CFI") # The example of continous varying sample size. Note that more fine-grained # values of n is needed, e.g., n=seq(50, 500, 1) Output.NULL.NULL2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.NULL, generate=CFA.Model.NULL) Output.ALT.NULL2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.NULL, generate=CFA.Model.ALT) Output.NULL.ALT2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.ALT, generate=CFA.Model.NULL) Output.ALT.ALT2 <- sim(NULL, n=seq(50, 500, 5), model=CFA.Model.ALT, generate=CFA.Model.ALT) # Plot logistic line for the power based on the derived cutoff from the null model # along sample size values plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, nullNested=Output.NULL.NULL2, nullParent=Output.NULL.ALT2) # Plot scatterplot for the power based on the derived cutoff from the null model # along sample size values plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, nullNested=Output.NULL.NULL2, nullParent=Output.NULL.ALT2, logistic=FALSE) # Plot scatterplot for the power based on the advanced CFI value plotPowerFitNested(Output.ALT.NULL2, Output.ALT.ALT2, cutoff=c(CFI=-0.1), logistic=FALSE) ## End(Not run) ```

plotPowerFitNested function