This example shows how to use PROC TRANSREG to code and fit a main-effects ANOVA model. PROC TRANSREG has very extensive and versatile options for coding or creating so-called dummy variables. PROC TRANSREG is commonly used to code classification variables before they are used for analysis in other procedures. See the sections Using the DESIGN Output Option and Discrete Choice Experiments: DESIGN, NORESTORE, NOZERO. In this example, the input data set contains the dependent variables y, factors x1 and x2, and 12 observations. PROC TRANSREG can be useful for coding even before running procedures with a CLASS statement because of its detailed options that enable you to control how the coded variable names and labels are constructed. The following statements perform a main-effects ANOVA and display the results in Figure 90.12 and Figure 90.13:

   title 'Introductory Main-Effects ANOVA Example';
   
   data a;
      input y x1 $ x2 $;
      datalines;
   8 a a
   7 a a
   4 a b
   3 a b
   5 b a
   4 b a
   2 b b
   1 b b
   8 c a
   7 c a
   5 c b
   2 c b
   ;

   * Fit a main-effects ANOVA model with 1, 0, -1 coding;
   proc transreg ss2;
      model identity(y) = class(x1 x2 / effects);
      output coefficients replace;
   run;
   
   * Display TRANSREG output data set;
   proc print label;
      format intercept -- x2a 5.2;
   run;

The SS2 a-option requests results based on Type II sums of squares. The simple ANOVA model is fit by designating y as an IDENTITY variable, which specifies no transformation. The independent variables are specified with a CLASS expansion, which replaces them with coded variables. There are coded variables created by the CLASS specification, since the two CLASS variables have 3 and 2 different values or levels. In this case, the EFFECTS t-option is specified. This option requests an effects coding (displayed in Figure 90.13), which is also called a deviations from means or 0, 1, –1 coding. The OUTPUT statement requests an output data set with the data and coded variables. The COEFFICIENTS output option, or o-option, adds the parameter estimates and marginal means to the data set. The REPLACE o-option specifies that the transformed variables should replace the original variables in the output data set. The output data set variable names are the same as the original variable name. In an example like this, there are no nonlinear transformations; the transformed variables are the same as the original variables. The REPLACE o-option is used to eliminate unnecessary and redundant transformed variables from the output data set. The results of the PROC TRANSREG step are shown in Figure 90.12.