The PROBIT Procedure

## Displayed Output

If you request the iteration history (ITPRINT), PROC PROBIT displays

• the current value of the log likelihood
• the ridging parameter for the modified Newton-Raphson optimization process
• the current estimate of the parameters
• the current estimate of the parameter C for a natural (threshold) model
• the values of the gradient and the Hessian on the last iteration

If you include CLASS variables, PROC PROBIT displays

• the numbers of levels for each CLASS variable
• the (ordered) values of the levels
• the number of observations used

After the model is fit, PROC PROBIT displays

• the name of the input data set
• the name of the dependent variables
• the number of observations used
• the number of events and the number of trials
• the final value of the log-likelihood function
• the parameter estimates
• the standard error estimates of the parameter estimates
• approximate chi-square test statistics for the test

If you specify the COVB or CORRB options, PROC PROBIT displays

• the estimated covariance matrix for the parameter estimates
• the estimated correlation matrix for the parameter estimates

If you specify the LACKFIT option, PROC PROBIT displays

• a count of the number of levels of the response and the number of distinct sets of independent variables
• a goodness-of-fit test based on the Pearson chi-square
• 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, PROC PROBIT displays

• the mean MU of the stimulus tolerance
• the scale parameter SIGMA of the stimulus tolerance
• the covariance matrix for MU, SIGMA, and the natural response parameter C

If you specify the INVERSECL options, PROC PROBIT also displays

:::START=25:::
• 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