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 request any of the three given types of predicted probabilities. For example, you can request both the individual predicted probabilities and the cross validated probabilities by specifying PREDPROBS=(I X).
When you specify the PREDPROBS= option, two automatic variables _FROM_ and _INTO_ are included for the single-trial syntax and only one variable, _INTO_, is included for the events/trials syntax. The _FROM_ variable contains the formatted value of the observed response. The variable _INTO_ contains the formatted value of the response level with the largest individual predicted probability.
If you specify PREDPROBS=INDIVIDUAL, the OUTPUT data set contains k additional variables representing the individual probabilities, one for each response level, where k is the maximum number of response levels across all BY groups. The names of these variables have the form IP_xxx, where xxx represents the particular level. The representation depends on the following situations:
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If you specify the events/trials syntax, xxx is either Event or Nonevent. Thus, the variable that contains the event probabilities is named
IP_Eventand the variable containing the nonevent probabilities is namedIP_Nonevent. -
If you specify the single-trial syntax with more than one BY group, xxx is 1 for the first ordered level of the response, 2 for the second ordered level of the response, and so forth, as given in the “Response Profile” table. The variable that contains the predicted probabilities Pr(
Y=1) is namedIP_1, whereYis the response variable. Similarly,IP_2is the name of the variable containing the predicted probabilities Pr(Y=2), and so on. -
If you specify the single-trial syntax with no BY-group processing, xxx is the left-justified formatted value of the response level (the value can be truncated so that
IP_xxx does not exceed 32 characters). For example, ifYis the response variable with response levels 'None,' 'Mild,' and 'Severe,' the variables representing individual probabilities Pr(Y='None'), Pr(Y='Mild'), and Pr(Y='Severe') are namedIP_None,IP_Mild, andIP_Severe, respectively.
If you specify PREDPROBS=CUMULATIVE, the OUTPUT data set contains k additional variables that represent the cumulative probabilities, one for each response level, where k is the maximum number of response levels across all BY groups. The names of these variables have the form CP_xxx, where xxx represents the particular response level. The naming convention is similar to that given by PREDPROBS=INDIVIDUAL. The PREDPROBS=CUMULATIVE
values are the same as those output by the PREDICT=keyword, but they are arranged in variables in each output observation
rather than in multiple output observations.
If you specify PREDPROBS=CROSSVALIDATE, the OUTPUT data set contains k additional variables representing the cross validated predicted probabilities of the k response levels, where k is the maximum number of response levels across all BY groups. The names of these variables have the form XP_xxx, where xxx represents the particular level. The representation is the same as that given by PREDPROBS=INDIVIDUAL, except that for the
events/trials syntax there are four variables for the cross validated predicted probabilities instead of two:
XP_EVENT_R1E-
is the cross validated predicted probability of an event when a current event trial is removed.
XP_NONEVENT_R1E-
is the cross validated predicted probability of a nonevent when a current event trial is removed.
XP_EVENT_R1N-
is the cross validated predicted probability of an event when a current nonevent trial is removed.
XP_NONEVENT_R1N-
is the cross validated predicted probability of a nonevent when a current nonevent trial is removed.