The error is undoubtedly due to every observation in your data set containing a missing value in one or more of the variables you specified in your PROC LOGISTIC step. As mentioned, to see how many observations are omitted due to missing values on one or more variables involved in the model, list all variables specified in the modeling procedure (response, predictors, weight, freq, offset, and so on) in the VAR statement in the PROC MI step below. Note that you should never include any variables in the CLASS statement of PROC LOGISTIC that are not specified elsewhere in the procedure. The results include a table that shows each pattern of missing values found in the data and the number of observations with each pattern. In the table, X means a value is present, O or . means it is missing. Only those observations in Group 1, with no missing values in any variable, can be used.
proc mi data=<your_data> nimpute=0 displaypattern=nomeans;
class <all CLASS variables>;
var <all variables>;
fcs logistic;
ods select MissPattern;
run;
If the input data is constructed to only contain the variables specified in PROC LOGISTIC, then this code can be used.
proc mi data=<your_data> nimpute=0 displaypattern=nomeans;
class _character_;
var _all_;
fcs logistic;
ods select MissPattern;
run;
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