Basic Analysis and Graphing - SAS

Chapter 7 Performing Simple Logistic Regression 223
The Logistic Report
Table 7.1 Description of the Whole Model Test (Continued)
Measure
The available measures of fit are as follows:
Entropy RSquare compares the log-likelihoods from the fitted model and
the constant probability model.
Generalized RSquare is a generalization of the Rsquare measure that
simplifies to the regular Rsquare for continuous normal responses. It is
similar to the Entropy RSquare, but instead of using the log-likelihood,
it uses the 2/n root of the likelihood.
Mean -Log p is the average of -log(p), where p is the fitted probability
associated with the event that occurred.
RMSE is the root mean square error, where the differences are between the
response and p (the fitted probability for the event that actually
occurred).
Mean Abs Dev is the average of the absolute values of the differences
between the response and p (the fitted probability for the event that
actually occurred).
Misclassification Rate is the rate for which the response category with
the highest fitted probability is not the observed category.
For Entropy RSquare and Generalized RSquare, values closer to 1 indicate a
better fit. For Mean -Log p, RMSE, Mean Abs Dev, and Misclassification
Rate, smaller values indicate a better fit.
Training
Definition
The value of the measure of fit.
The algebraic definition of the measure of fit.
Parameter Estimates
The nominal logistic model fits a parameter for the intercept and slope for each of k – 1 logistic
comparisons, where k is the number of response levels. The Parameter Estimates report lists these estimates.
Each parameter estimate can be examined and tested individually, although this is seldom of much interest.
Table 7.2 Description of the Parameter Estimates Report
Term
Estimate
Std Error
Lists each parameter in the logistic model. There is an intercept and a slope term for
the factor at each level of the response variable, except the last level.
Lists the parameter estimates given by the logistic model.
Lists the standard error of each parameter estimate. They are used to compute the
statistical tests that compare each term to zero.