The ordering of the variables on the VAR statement effects the way in which the imputation models are configured because of the montonicity. It assumes that the variables are listed such that they exhibit a monotone missing pattern from left to right. In other words, the left-most variable has no missing values, the second has fewer then the third and so on. This means that the imputation models are built such that only a variable that is to the left of a given variable may be used in its imputation model
Specifically there is a problem with this MONOTONE statement:
monotone discrim(Z_1= y1 X_1 y2 X_2 y3 X_3 y4 X_4 y5 X_5 y6 X_6 y7 X_7 /classeffects=include);
Since y2 (and several others) comes in the VAR list after Z_1 then it cannot be used as a covariate. Only those variables listed to the left of Z_1 on the VAR statement could be used. If you want to use models that include other variables then you will need to relax the monotone assumption and use the FCS approach instead.
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