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
If you request the iteration history (ITPRINT), PROC PROBIT displays the following:
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the current value of the log likelihood
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the ridging parameter for the modified Newton-Raphson optimization process
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the current estimate of the parameters
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the current estimate of the parameter C for a natural (threshold) model
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the values of the gradient and the Hessian on the last iteration
If you include classification variables, PROC PROBIT displays the following:
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the numbers of levels for each classification variable
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the (ordered) values of the levels
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the number of observations used
After the model is fit, PROC PROBIT displays the following:
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the name of the input data set
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the name of the dependent variables
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the number of observations used
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the number of events and the number of trials
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the final value of the log-likelihood function
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the parameter estimates
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the standard error estimates of the parameter estimates
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approximate chi-square test statistics for the test
If you specify the COVB or CORRB options, PROC PROBIT displays the following:
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the estimated covariance matrix for the parameter estimates
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the estimated correlation matrix for the parameter estimates
If you specify the LACKFIT option, PROC PROBIT displays the following:
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a count of the number of levels of the response and the number of distinct sets of independent variables
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a goodness-of-fit test based on the Pearson’s chi-square
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a goodness-of-fit test based on the likelihood-ratio chi-square
If you specify only one independent variable, the normal distribution is used to model the probabilities, and the response is binary, then PROC PROBIT displays the following:
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the mean MU of the stimulus tolerance
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the scale parameter SIGMA of the stimulus tolerance
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the covariance matrix for MU, SIGMA, and the natural response parameter C
If you specify the INVERSECL options, PROC PROBIT also displays the following:
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the estimated dose along with the 95% fiducial limits for probability levels 0.01 to 0.10, 0.15 to 0.85 by 0.05, and 0.90 to 0.99