The SURVEYLOGISTIC Procedure
- Overview
- Getting Started
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Syntax
PROC SURVEYLOGISTIC StatementBY StatementCLASS StatementCLUSTER StatementCONTRAST StatementDOMAIN StatementEFFECT StatementESTIMATE StatementFREQ StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementOUTPUT StatementREPWEIGHTS StatementSLICE StatementSTORE StatementSTRATA StatementTEST StatementUNITS StatementWEIGHT Statement -
DetailsMissing ValuesModel SpecificationModel FittingSurvey Design InformationLogistic Regression Models and ParametersVariance EstimationDomain AnalysisHypothesis Testing and EstimationLinear Predictor, Predicted Probability, and Confidence LimitsOutput Data SetsDisplayed OutputODS Table NamesODS Graphics
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Examples
- References
Logistic Regression Models
If the response categories of the response variable Y can be restricted to a number of ordinal values, you can fit cumulative probabilities of the response categories with a cumulative
logit model, a complementary log-log model, or a probit model. Details of cumulative logit models (or proportional odds models)
can be found in McCullagh and Nelder (1989). If the response categories of Y are nominal responses without natural ordering, you can fit the response probabilities with a generalized logit model. Formulation
of the generalized logit models for nominal response variables can be found in Agresti (2002). For each model, the procedure estimates the model parameter 
by using a pseudo-log-likelihood function. The procedure obtains the pseudo-maximum likelihood estimator 
by using iterations described in the section Iterative Algorithms for Model Fitting and estimates its variance described in the section Variance Estimation.