Passive Observations |
Observations can be excluded from the analysis for several reasons, including zero weight, zero frequency, missing values in variables designated as IDENTITY, or missing values with the NOMISS option specified. These observations are passive in that they do not contribute to determining transformations, R square, total variance, and so on. However, some information can be computed for them, such as approximations, principal component scores, and transformed values. Passive observations in the output data set have a blank value for the variable _TYPE_.
Missing value estimates for passive observations might converge slowly with METHOD=MTV. In the following example, the missing value estimates should be 2, 5, and 8. Since the nonpassive observations do not change, the procedure converges in one iteration but the missing value estimates do not converge. The extra iterations produced by specifying CONVERGE=–1 and CCONVERGE=–1, as shown in the second PROC PRINQUAL step that follows, generate the expected results.
data A; input X Y; datalines; 1 1 2 . 3 3 4 4 5 . 6 6 7 7 8 . 9 9 ; proc prinqual nomiss data=A nomiss n=1 out=B method=mtv; transform lin(X Y); run; proc print; run; proc prinqual nomiss data=A nomiss n=1 out=B method=mtv converge=-1 cconverge=-1; transform lin(X Y); run; proc print; run;