By default, PROC PLS handles missing values very simply. Observations with any missing independent variables (including all classification variables) are excluded from the analysis, and no predictions are computed for such observations. Observations with no missing independent variables but any missing dependent variables are also excluded from the analysis, but predictions are computed.

However, the MISSING= option in the PROC PLS statement provides more sophisticated ways of modeling in the presence of missing values. If you specify MISSING= AVG or MISSING= EM, then all observations in the input data set contribute to both the analysis and the OUTPUT OUT= data set. With MISSING= AVG, the fit is computed by filling in missing values with the average of the nonmissing values for the corresponding variable. With MISSING= EM, the procedure first computes the model with MISSING= AVG, then fills in missing values with their predicted values based on that model and computes the model again. Alternatively, you can specify MISSING= EM(MAXITER=n) with a large value of n in order to perform this imputation/fit loop until convergence.