Show MLE Parameter Estimates(Not available if Hierarchical Bayes is selected on the launch window.) Tests each factor in the model by constructing a likelihood ratio test for all the effects involving that factor. For more information about Joint Factor Tests, see Joint Factor Tests in the Fitting Linear Models book.
Performs comparisons between specific alternative choice profiles. Enables you to select factor values and the values that you want to compare. You can compare specific configurations, including comparing all settings on the left or right by selecting the Any check boxes. Using Any does not compare all combinations across features, but rather all combinations of comparisons, one feature at a time, using the left settings as the settings for the other factors. See Comparisons Report.
The Wald p-values are colored. A saturated blue (respectively, red) color indicates that the Difference (Row - Column) is negative (respectively positive). The intensity of the red and blue coloring indicates the degree of significance, with a highly saturated red or blue meaning that the difference is highly significant.
Constructs a new table that has a row for each subject containing the average (Hessian-scaled-gradient) steps for the likelihood function on each parameter. This corresponds to using a Lagrangian multiplier test for separating that subject from the remaining subjects. These values can later be clustered, using the built-in-script, to indicate unique market segments represented in the data. For more details, see Example of Segmentation in Choice Models.
Save Subject Estimates(Available only if Hierarchical Bayes is selected.) Creates a table where each row contains the subject-specific parameter estimates for each effect. The distribution of subject-specific parameter effects for each effect is centered at the estimate for the term given in the Bayesian Parameter Estimates report. The Subject Acceptance Rate gives the rate of acceptance for draws of new parameter estimates during the Metropolis-Hastings step. Generally, an acceptance rate of 0.20 is considered to be good. See Bayesian Parameter Estimates.
Save Bayes Chain(Available only if Hierarchical Bayes is selected.) Creates a table that gives information on the chain of iterations used in computing subject-specific Bayesian estimates. See Save Bayes Chain.
Shows the MaxDiff launch window that resulted in the current analysis, which can be used to modify and re-fit the model. You can specify new data sets, new IDs, and new model effects.
See Redo Menus and Save Script Menus in the Using JMP book for more information about the following options: