Choice Modeling - Save Variable(s) - Utilities (Min 0)

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 lowest utility of any level is zero.

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 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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Customizing the QScript

This QScript is written in JavaScript and can be customized by copying and modifying the JavaScript.

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Modify the JavaScript (see QScripts for more detail on this).
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Customizing QScripts in older versions

Copy the JavaScript shown on this page.
Create a new text file, giving it a file extension of .QScript. See here for more information about how to do this.
Modify the JavaScript (see QScripts for more detail on this).
Run the file using Automate > Run QScript (Macro) from File.

JavaScript

includeWeb('QScript R Output Functions');

saveVariables('Utilities (min 0)', '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, scaling = "Min = 0", attr.order = "As is", output = "Data")',
              null, null, 'util.min0', ['FitChoice', 'ChoiceEnsemble']);