MaxDiff Platform Options
The MaxDiff Model red triangle menu contains the following options:
Show MLE Parameter Estimates
(Available for Hierarchical Bayes.) Shows non-Firth maximum likelihood estimates and standard errors for the coefficients of model terms. These estimates are used as starting values for the Hierarchical Bayes algorithm.
Joint Factor Tests
(Not available for Hierarchical Bayes.) 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 Fitting Linear Models.
Confidence Intervals
(Not available for Hierarchical Bayes) Shows or hides a confidence interval for each parameter in the Parameter Estimates report.
Confidence Limits
(Available for Hierarchical Bayes) Shows or hides confidence limits for each parameter in the Bayesian Parameter Estimates report. The limits are constructed based on the 2.5 and 97.5 quantiles of the posterior distribution.
Correlation of Estimates
If Hierarchical Bayes was not selected, shows or hides the correlations between the maximum likelihood parameter estimates.
For Hierarchical Bayes, shows or hides the correlation matrix for the posterior means of the parameter estimates. The correlations are calculated from the iterations after burn-in. The posterior means from each iteration after burn-in are treated as if they are columns in a data table. The Correlation of Estimates table is obtained by calculating the correlation matrix for these columns.
Comparisons
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.
All Levels Comparison Report
Shows the All Levels Comparison Report, which contains a table with information about all pairwise comparisons of profiles. If you are modeling subject effects, you must specify a combination of subject effects and the table is specific to that combination of subject effects. Each cell of the table shows the difference in utilities for the row level and column level, the standard error of the difference, and a Wald p-value for a test of no difference.
Caution: The p-values are not corrected for multiple comparisons. Use these results as a guide.
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.
Save Utility Formula
When the analysis is on multiple data tables, creates a new data table that contains a formula column for utility. The new data table contains a row for each subject and profile combination, and columns for the profiles and the subject effects. When the analysis is on one data table, a new Utility Formula column is added.
Save Gradients by Subject
(Not available for Hierarchical Bayes.) 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. See Example of Segmentation.
Note: When a subject gradient is non-estimable it is set to missing in the Gradient by Subject table.
Save Subject Estimates
(Available for Hierarchical Bayes.) 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 for Hierarchical Bayes.) Creates a table that gives information about the chain of iterations used in computing subject-specific Bayesian estimates. See Save Bayes Chain.
Model Dialog
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 Local Data Filters in JMP Reports, Redo Menus in JMP Reports, Group Platform, and Save Script Menus in JMP Reports in Using JMP for more information about the following options:
Local Data Filter
Shows or hides the local data filter that enables you to filter the data used in a specific report.
Redo
Contains options that enable you to repeat or relaunch the analysis. In platforms that support the feature, the Automatic Recalc option immediately reflects the changes that you make to the data table in the corresponding report window.
Platform Preferences
Contains options that enable you to view the current platform preferences or update the platform preferences to match the settings in the current JMP report.
Save Script
Contains options that enable you to save a script that reproduces the report to several destinations.
Save By-Group Script
Contains options that enable you to save a script that reproduces the platform report for all levels of a By variable to several destinations. Available only when a By variable is specified in the launch window.
Note: Additional options for this platform are available through scripting. Open the Scripting Index under the Help menu. In the Scripting Index, you can also find examples for scripting the options that are described in this section.