The PROBIT Procedure
- Overview
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Getting Started
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SyntaxPROC PROBIT StatementBY StatementCDFPLOT StatementCLASS StatementEFFECTPLOT StatementESTIMATE StatementINSET StatementIPPPLOT StatementLPREDPLOT StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementOUTPUT StatementPREDPPLOT StatementSLICE StatementSTORE StatementTEST StatementWEIGHT Statement
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DetailsMissing ValuesResponse Level OrderingComputational MethodDistributionsINEST= SAS-data-setModel SpecificationLack-of-Fit TestsRescaling the Covariance MatrixTolerance DistributionInverse Confidence LimitsOUTEST= SAS-data-setXDATA= SAS-data-setTraditional High-Resolution GraphicsDisplayed OutputODS Table NamesODS Graphics
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Examples
- References
The OUTEST= data set contains parameter estimates and the log likelihood for the model. You can specify a label in the MODEL statement to distinguish between the estimates for different models used by the PROBIT procedure. If you specify the COVOUT option, the OUTEST= data set also contains the estimated covariance matrix of the parameter estimates.
The OUTEST= data set contains each variable used as a dependent or independent variable in any MODEL statement. One observation consists of parameter values for the model with the dependent variable having the value -1. If you specify the COVOUT option, there are additional observations containing the rows of the estimated covariance matrix. For these observations, the dependent variable contains the parameter estimate for the corresponding row variable. The following variables are also added to the data set:
_MODEL_-
a character variable containing the label of the MODEL statement, if present, or blank otherwise
_NAME_-
a character variable containing the name of the dependent variable for the parameter estimates observations or the name of the row for the covariance matrix estimates
_TYPE_-
a character variable containing the type of the observation, either PARMS for parameter estimates or COV for covariance estimates
_DIST_-
a character variable containing the name of the distribution modeled
_LNLIKE_-
a numeric variable containing the last computed value of the log likelihood
_C_-
a numeric variable containing the estimated threshold parameter
INTERCEPT-
a numeric variable containing the intercept parameter estimates and covariances
Any BY variables specified are also added to the OUTEST= data set.