Since you want predicted probabilities for individual levels of your response from this ordinal model, the clearest way to get them is to create a one observation data set to be scored with DM at the desired level and then use the OUTPUT statement with the PREDPROBS=INDIVIDUAL option. This option produces the predicted probabilities of the individual response levels. To illustrate, use the DocVisit data set in the example titled "Partial Proportional Odds Model" in the PROC LOGISTIC documentation. To simplify the example, the following statements combine all the higher response levels into level 3, so that the new response variable, DV, has levels 0, 1, 2, or 3.
data dv; set docvisit;
dv=dvisits; if dvisits>2 then dv=3;
run;
and then create a one observation data set with the predictor, INCOME, set to the desired level (let's use 0.25) and the response set to missing and add it to the original data so that this observation does not get used when fitting the model. The SCORE variable is created so that this observation can be singled out for saving by the WHERE clause in the PROC LOGISTIC OUTPUT statement.
data score; income=0.25; dv=.; score=1; run;
data dv2; set score dv; run;
Now, fit the ordinal model using INCOME as the predictor. The EFFECTPLOT gives you a visual image of how the individual response level probabilities change with INCOME. The OUTPUT statement produces the individual response level probabilities and the probabilities cumulated over the lower response levels (0, up to 1, up to 2, and up to 3). The ESTIMATE statement with the ILINK option can only produce the cumulative probabilities. The CATEGORY=JOINT option gives each of the cumulative probabilities in a single table.
proc logistic data=dv2;
model dv=income;
effectplot / individual;
output out=out(where=(score=1)) predprobs=(cumulative individual);
estimate 'P(dv=1 @ .25)' intercept 1 income .25 / ilink category=joint;
run;
These statements print the one observation at INCOME=0.25 and its cumulative (beginning with CP_) and individual level (beginning with IP_) predicted probabilities.
proc print data=out;
id income; var ip: cp:;
run;
Notice that the cumulative predicted probabilities are the same as those from the ESTIMATE statement. The differences between successive cumulative predicted probabilities are the individual predicted probabilities.
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