Choice Modeling - Diagnostic - Parameter Statistics Table

Produces a table of parameter statistics for both Choice Modeling - Latent Class Analysislatent class analysis choice model and Choice Modeling - Hierarchical Bayeshierarchical Bayes choice model outputs. With latent class analysis, this is a table containing parameter estimates and significance-testing statistics. With hierarchical Bayes, this is a table containing sample statistics of the mean and standard deviation parameters for the distribution from which individual coefficients are sampled. For more information on how to interpret this output, see this blog post, which was written for the closely-related MaxDiff analysis.

Note that with hierarchical Bayes, numeric variables are shown as scaled (this is done to improve model sampling). The prefix "Scaled" is added to numeric variable names. To descale the numeric variable coefficients, standard deviations and standard errors, multiply them by the multipliers that can be obtained by creating a new R output and typing in choice.model$numeric.scaling into the R CODE editor (choice.model needs to be replaced with the name of the choice model output) and clicking calculate. The output will contain a list of multipliers to be used for each numeric variable.

Example

The table below shows parameter statistics from a Choice Modeling - Latent Class Analysislatent class analysis choice model output:

The next table shows parameter statistics from a Choice Modeling - Hierarchical Bayeshierarchical Bayes choice model output:

Technical details

Whenever hierarchical Bayes analysis is run with multiple classes, an attempt will be made to match class labels between chains (note that it is often not possible to match class labels). If this succeeds, or if only one chain was specified, one set of mean and standard deviation parameters will be shown for each class in the parameter statistics table. In addition, class size parameters will also be displayed. If this attempt is unsuccessful, the parameter statistics table will not be able to be shown.

References

McLean, M. W. (2018, July 24). How to Use Hierarchical Bayes for Choice Modeling in Displayr [Blog post]. Accessed from https://www.displayr.com/how-to-hierarchical-bayes-choice-model-displayr/.

Yap, J. (2018, January 16). Checking Convergence When Using Hierarchical Bayes for MaxDiff [Blog post]. Accessed from https://www.displayr.com/convergence-hb-maxdiff/.

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const menu_location = "Choice Modeling > Diagnostic > Parameter Statistics Table";
createDiagnosticROutputFromSelection(menu_location);

Acknowledgements

Uses the rstan R package.