The fact that random effects can be modeled directly in the RANDOM statement might make the specification of nested effects in the MODEL statement unnecessary.Īn example that combines all the effects is X1* X2* A* B* C ( D E). That is, they usually indicate random effects within a fixed-effects framework. Note that nested effects are often distinguished from interaction effects by the implied randomization structure of the design. The order of the columns is such that variables outside the parentheses index faster than those inside the parentheses, and the rightmost nested variables index faster than the leftmost variables ( Table 56.18). The order of the variables within nesting parentheses is made to correspond to the order of these variables in the CLASS statement. Nested effects are typically characterized by the property that the nested variables never appear as main effects. The nesting operator in PROC MIXED is more a notational convenience than an operation distinct from crossing. For instance, A|B|C|D expands to A B A* B C A* C B* C D A* D B* D C* D. To eliminate higher-order interaction effects, use the at sign in conjunction with the bar operator. For example, A|B|C expands to A B A* B C A* C B* C A* B* C. The bar operator generates all possible interaction effects.
#INTRACTION SUBSUME MAIN EFFECT CODE#
When your model contains many interaction effects, you might be able to code them more parsimoniously by using the bar operator ( | ). In the preceding matrix, main-effects columns are not linearly independent of crossed-effects columns in fact, the column space for the crossed effects contains the space of the main effect. Table 56.17 Example of Interaction Effects Empty columns (that would contain all 0s) are not generated for, but they are for. The order of the columns is such that the rightmost variables in the cross index faster than the leftmost variables ( Table 56.17). Then, PROC MIXED generates columns for all combinations of levels that occur in the data. Thus, B* A becomes A* B if A precedes B in the CLASS statement. With an interaction, PROC MIXED first reorders the terms to correspond to the order of the variables in the CLASS statement. Often a model includes interaction (crossed) effects.
0 kommentar(er)
