The following sections describe the new features of this version of PROC CALIS.
Residual analysis at the case level or observation level is available when you provide raw data input. The main features include graphical and nongraphical residual diagnostic tools for identifying outliers and leverage observations, for detecting departures from the theoretical residual distribution, and for studying the distribution of residuals over the range of predicted values of the dependent observed variables. Use the RESIDUAL option to request nongraphical output for residual diagnostics. Use the PLOTS= option to request graphical plots for residual diagnostics. See the section Case-Level Residuals, Outliers, Leverage Observations, and Residual Diagnostics for details about these new features. See Example 27.14 for an example that illustrates the use of the residual diagnostic tools provided by PROC CALIS.
Robust estimation is available with the ROBUST option. Robust estimation methods downweight the model outliers during the estimation process so that estimates are not contaminated by outliers. Two robust methods are implemented in PROC CALIS. The direct robust method applies individual (casewise) weights to observations during the model estimation. This is the default robust method that you get when you specify the ROBUST option. The two-stage robust method estimates robust covariance and mean matrices in the first stage, and then it does an ML estimation of the model by inputting the robust covariance and mean matrices in the second stage. You can specify this method with the ROBUST=SAT option. See the ROBUST option for details about variants of the robust methods. See the section Robust Estimation for technical details of the robust estimation implemented in PROC CALIS.
A class of fit indices, called incremental fit indices (see the section Incremental Indices for details), measures model fit by comparing the relative fit of the target model to that of a baseline model. Traditionally, this baseline model is the uncorrelatedness model, where the covariances of observed variables are all zero. PROC CALIS follows this tradition and uses the uncorrelatedness model as the baseline model when computing the incremental fit indices. However, if you have a situation where the uncorrelatedness model is not desirable, you can now define and fit your own baseline model in a separate analysis. PROC CALIS provides tools for you to input the fit information of your customized baseline model. If you use PROC CALIS to fit your customized baseline model, you can save the fit information in an OUTFIT= data set and then specify the data set as a BASEFIT= data set when fitting your target model. PROC CALIS can extract the fit information of the customized baseline model for the computations of incremental fit indices (and a few other indices that assume the degrees of freedom of a baseline model). Alternatively, you can directly input the fit function value and the degrees of freedom of your customized baseline model by using the BASEFUNC= option. See the BASEFIT= and the BASEFUNC= options for details.