Publication date: 05/24/2021

By default, the Generalized Linear Model Fit report contains details about the model specification as well as the following reports:

Singularity Details

(Appears only when there are linear dependencies among the model terms.) Shows a report that contains the linear functions that the model terms satisfy.

Regression Plot

(Appears only when there is one continuous predictor and no more than one categorical predictor.) Shows a plot of the response on the vertical axis and the continuous predictor on the horizontal axis. A regression line is shown over the points. If there is a categorical predictor in the model, each level of the categorical predictor has a separate regression line and a legend appears next to the plot.

Whole Model Test

Shows tests that compare the whole-model fit to the model that omits all the effects except the intercept parameters. This report also contains goodness-of-fit statistics and the corrected Akaike’s Information Criterion (AICc) value. See Whole Model Test.

Effect Summary

An interactive report that enables you to add or remove effects from the model. See Effect Summary Report.

Effect Tests

The Effect Tests are joint tests that all the parameters for an individual effect are zero. If an effect has only one parameter, as with continuous effects, then the tests are the same as the tests in the Parameter Estimates table.

Note: Even if the Firth adjustment is used, the Effect Tests are based on the non-penalized likelihood function.

Parameter Estimates

Shows the parameter estimates, standard errors, and associated hypothesis tests and confidence limits. Simple continuous effects have only one parameter. Models with complex classification effects have a parameter for each anticipated degree of freedom.

Note: If there are more than 1,000 observations, Wald-based confidence intervals are shown. Otherwise, profile-likelihood confidence intervals are shown.

Studentized Deviance Residual by Predicted

Shows a plot of studentized deviance residuals on the vertical axis and the predicted response values on the horizontal axis.

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).