Basic Analysis and Graphing - SAS

Chapter 7 Performing Simple Logistic Regression 221
The Logistic Report
Iterations
Replace variables in the plot in one of two ways: swap existing variables by dragging and dropping a variable
from one axis to the other axis; or, click on a variable in the Columns panel of the associated data table and
drag it onto an axis.
Related Information
• “Additional Example of a Logistic Plot” on page 228
The Iterations report shows each iteration and the evaluated criteria that determine whether the model has
converged. Iterations appear only for nominal logistic regression.
Whole Model Test
The Whole Model Test report shows if the model fits better than constant response probabilities. This
report is analogous to the Analysis of Variance report for a continuous response model. It is a specific
likelihood-ratio Chi-square test that evaluates how well the categorical model fits the data. The negative sum
of natural logs of the observed probabilities is called the negative log-likelihood (–LogLikelihood). The
negative log-likelihood for categorical data plays the same role as sums of squares in continuous data. Twice
the difference in the negative log-likelihood from the model fitted by the data and the model with equal
probabilities is a Chi-square statistic. This test statistic examines the hypothesis that the x variable has no
effect on the responses.
Values of the Rsquare (U) (sometimes denoted as R 2 ) range from 0 to 1. High R 2 values are indicative of a
good model fit, and are rare in categorical models.
Table 7.1 Description of the Whole Model Test
Model
(sometimes called
Source)
DF
• The Reduced model only contains an intercept.
• The Full model contains all of the effects as well as the intercept.
• The Difference is the difference of the log likelihoods of the full and
reduced models.
Records the degrees of freedom associated with the model.