# Plot sampling distributions of fit indices that visualize power of rejecting datasets underlying misspecified models This function will plot sampling distributions of fit indices that visualize power in rejecting the misspecified models ```r plotPowerFit(altObject, nullObject = NULL, cutoff = NULL, usedFit = NULL, alpha = 0.05, contN = TRUE, contMCAR = TRUE, contMAR = TRUE, useContour = TRUE, logistic = TRUE) ``` ## Arguments - `altObject`: The result object (`SimResult`) saves the simulation result of fitting the hypothesized model when the hypothesized model is `FALSE`. - `nullObject`: The result object (`SimResult`) saves the simulation result of fitting the hypothesized model when the hypothesized model is `TRUE`. This argument may be not specified if the `cutoff` is specified. - `cutoff`: A vector of priori cutoffs for 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. * `getCutoff` to find values of cutoffs based on null hypothesis sampling distributions only * `getPowerFit` to find power of rejecting the hypothesized model when the hypothesized model is `FALSE`. ## 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") # We make the examples running only 5 replications to save time. # In reality, more replications are needed. Output.NULL <- sim(50, n=50, model=CFA.Model.NULL, generate=CFA.Model.NULL) # 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, 0.5) CFA.Model.ALT <- model(LY = LY.ALT, RPS = RPS.ALT, RTE = RTE, modelType="CFA") Output.ALT <- sim(50, n=50, model=CFA.Model.NULL, generate=CFA.Model.ALT) # Plot the power based on derived cutoff from the null model using four fit indices plotPowerFit(Output.ALT, nullObject=Output.NULL, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR")) # Plot the power of rejecting null model when the rule of thumb from Hu & Bentler (1999) is used Rule.of.thumb <- c(RMSEA=0.05, CFI=0.95, TLI=0.95, SRMR=0.06) plotPowerFit(Output.ALT, cutoff=Rule.of.thumb, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR")) # 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.NULL2 <- sim(NULL, n=seq(50, 250, 25), model=CFA.Model.NULL, generate=CFA.Model.NULL) Output.ALT2 <- sim(NULL, n=seq(50, 250, 25), model=CFA.Model.NULL, generate=CFA.Model.ALT) # Plot the power based on derived cutoff from the null model using four fit indices # along sample size plotPowerFit(Output.ALT2, nullObject=Output.NULL2, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR")) # Plot the power based on rule of thumb along sample size plotPowerFit(Output.ALT2, cutoff=Rule.of.thumb, alpha=0.05, usedFit=c("RMSEA", "CFI", "TLI", "SRMR")) ## End(Not run) ```

plotPowerFit function