Choice Modeling - Save Variable(s) - Utilities (Mean 0, Mean Range 100)

Create new variables which contain the utilities from a Choice Modeling - Latent Class Analysis, Choice Modeling - Hierarchical Bayes or Choice Modeling - Ensemble of Models output.

Utilities are the individual-level coefficients shifted so that, for each individual and attribute, the mean utility across the levels is zero. Utilities are then all multiplied by a scaling factor per individual, so that the average range of utilities per individual across the levels of each attribute is 100.

Individual-level coefficients are not able to be saved to variables for models with simulated data and models created using a CHO data file where respondent IDs were not specified.

If the choice model contains a "None of these" alternative, the utility of the "None of these" parameter will need to be adjusted in order to take into account the changes made to the utilities of the other attributes. For example, if there are 3 attributes in the model (not including the Alternative attribute), and their utilities needed to be shifted by +0.3, -0.5, +0.8 respectively, then the "None of these" utility will need to be shifted by +0.6 (= 0.3 - 0.5 + 0.8), in addition to any shifting and scaling done as part of the Alternative attribute.

Output

Running this QScript will produce a new variable, which will appear in your data set for use in further analyses.

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/.

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JavaScript

includeWeb('QScript R Output Functions');

saveVariables('Utilities (mean 0 mean range 100)',
              'Latent Class Analysis, Hierarchical Bayes or Ensemble Choice Model',
              'input.choicemodel = ',
              '\nif (!is.null(input.choicemodel$simulated.respondent.parameters)) stop()\n' +
              'flipChoice::Utilities(input.choicemodel,\n'+
              '                      scaling = "Mean = 0; Mean range = 100", attr.order = "As is", output = "Data")',
              null, null, 'util.mean0.meanrng100', ['FitChoice', 'ChoiceEnsemble']);