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
You can use the Output Delivery System (ODS) to create a SAS data set from any piece of PROC SURVEYLOGISTIC output. See the section ODS Table Names for more information. For a more detailed description of using ODS, see Chapter 20: Using the Output Delivery System.
PROC SURVEYLOGISTIC also provides an OUTPUT statement to create a data set that contains estimated linear predictors, the estimates of the cumulative or individual response probabilities, and their confidence limits.
If you use BRR or jackknife variance estimation, PROC SURVEYLOGISTIC provides an output data set that stores the replicate weights and an output data set that stores the jackknife coefficients for jackknife variance estimation.
The OUT= data set in the OUTPUT
statement contains all the variables in the input data set along with statistics you request by using keyword=name options or the PREDPROBS= option in the OUTPUT statement. In addition, if you use the single-trial syntax and you request
any of the XBETA=, STDXBETA=, PREDICTED=, LCL=, and UCL= options, the OUT= data set contains the automatic variable _LEVEL_. The value of _LEVEL_ identifies the response category upon which the computed values of XBETA=, STDXBETA=, PREDICTED=, LCL=, and UCL= are based.
When there are more than two response levels, only variables named by the XBETA=, STDXBETA=, PREDICTED=, LOWER=, and UPPER= options and the variables given by PREDPROBS=(INDIVIDUAL CUMULATIVE) have their values computed; the other variables have missing values. If you fit a generalized logit model, the cumulative predicted probabilities are not computed.
When there are only two response categories, each input observation produces one observation in the OUT= data set.
If there are more than two response categories and you specify only the PREDPROBS= option, then each input observation produces one observation in the OUT= data set. However, if you fit an ordinal (cumulative) model and specify options other than the PREDPROBS= options, each input observation generates as many output observations as one fewer than the number of response levels, and the predicted probabilities and their confidence limits correspond to the cumulative predicted probabilities. If you fit a generalized logit model and specify options other than the PREDPROBS= options, each input observation generates as many output observations as the number of response categories; the predicted probabilities and their confidence limits correspond to the probabilities of individual response categories.
For observations in which only the response variable is missing, values of the XBETA=, STDXBETA=, PREDICTED=, UPPER=, LOWER=, and PREDPROBS= options are computed even though these observations do not affect the model fit. This enables, for instance, predicted probabilities to be computed for new observations.
If you specify the OUTWEIGHTS= method-option for VARMETHOD=BRR or VARMETHOD=JACKKNIFE , PROC SURVEYLOGISTIC stores the replicate weights in an output data set. The OUTWEIGHTS= output data set contains all observations from the DATA= input data set that are valid (used in the analysis). (A valid observation is an observation that has a positive value of the WEIGHT variable. Valid observations must also have nonmissing values of the STRATA and CLUSTER variables, unless you specify the MISSING option.)
The OUTWEIGHTS= data set contains the following variables:
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all variables in the DATA= input data set
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RepWt_1,RepWt_2,
, RepWt_n, which are the replicate weight variables
where n is the total number of replicates in the analysis. Each replicate weight variable contains the replicate weights for the corresponding replicate. Replicate weights equal zero for those observations not included in the replicate.
After the procedure creates replicate weights for a particular input data set and survey design, you can use the OUTWEIGHTS= method-option to store these replicate weights and then use them again in subsequent analyses, either in PROC SURVEYLOGISTIC or in the other survey procedures. You can use the REPWEIGHTS statement to provide replicate weights for the procedure.
If you specify the OUTJKCOEFS= method-option for VARMETHOD=JACKKNIFE , PROC SURVEYLOGISTIC stores the jackknife coefficients in an output data set. The OUTJKCOEFS= output data set contains one observation for each replicate. The OUTJKCOEFS= data set contains the following variables:
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Replicate, which is the replicate number for the jackknife coefficient -
JKCoefficient, which is the jackknife coefficient -
DonorStratum, which is the stratum of the PSU that was deleted to construct the replicate, if you specify a STRATA statement
After the procedure creates jackknife coefficients for a particular input data set and survey design, you can use the OUTJKCOEFS= method-option to store these coefficients and then use them again in subsequent analyses, either in PROC SURVEYLOGISTIC or in the other survey procedures. You can use the JKCOEFS= option in the REPWEIGHTS statement to provide jackknife coefficients for the procedure.