FIXED Subcommand (GENLINMIXED command)

The FIXED subcommand is used to specify fixed model effects, including the intercept.

  • This subcommand is optional. If not specified, then the procedure builds an intercept-only model.

EFFECTS. The effect list includes all effects to be included in the fixed model except for the intercept, which is specified using the USE_INTERCEPT keyword.

  • To include a term for the main effect of a factor (categorical predictor) or covariate (continuous predictor), enter its field name. Whether a field is treated as a factor or covariate depends upon its measurement level. Fields with categorical (flag, nominal, or ordinal) measurement level are treated as factors while fields with continuous (scale) measurement level are treated as covariates.
  • To include a term for an interaction between factors, use the keyword BY or an asterisk (*) to join the factors involved in the interaction. For example, A*B means a two-way interaction effect of A and B, where A and B are factors. A*A is not allowed because factors in an interaction effect must be distinct.
  • To include a term for nesting one effect within another, use a pair of parentheses. For example, A(B) means that A is nested within B.
  • Multiple nesting is allowed. For example, A(B(C)) means that B is nested within C, and A is nested within B(C). When more than one pair of parentheses is present, each pair of parentheses must be enclosed or nested within another pair of parentheses. Thus, A(B)(C) is not valid.
  • Interactions between nested effects are not valid. For example, neither A(C)*B(C) nor A(C)*B(D) is valid.
  • Covariates can be connected, but not nested, through the * operator to form another covariate effect. Interactions among covariates such as X1*X1 and X1*X2 are valid, but X1(X2) is not.
  • Factor and covariate effects can be connected only by the * operator. Suppose A and B are factors, and X1 and X2 are covariates. Examples of valid factor-by-covariate interaction effects are A*X1, A*B*X1, X1*A(B), A*X1*X1, and B*X1*X2.

USE_INTERCEPT = TRUE** | FALSE. This keyword controls whether an intercept term is included in the model. Specifying USE_INTERCEPT=FALSE when there are no effects in the model fits a "null" model. The default is TRUE.