Choice Modeling - Save Variable(s) - Utilities (Min 0, Max range 100)

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Create new variables which contain the utilities from a Choice Modeling - Latent Class Analysis, Choice Modeling - Hierarchical Bayes or Choice Modeling - Ensemble output. You should select the output before running this script.

Utilities are the individual-level coefficients scaled and shifted so that, for each individual and attribute, the lowest utility of any level is zero and the greatest utility of any level 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.

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/.

How to apply this QScript

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OR

  • Select Automate > Browse Online Library.
  • Select this QScript from the list.

Customizing the QScript

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

Customizing QScripts in Q4.11 and more recent versions

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  • Press Edit a Copy (bottom-left corner of the preview).
  • Modify the JavaScript (see QScripts for more detail on this).
  • Either:
    • Run the QScript, by pressing the blue triangle button.
    • Save the QScript and run it at a later time, using Automate > Run QScript (Macro) from File.

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");

main();
 
function main() {
    saveVariables("Utilities (min 0 max 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, scaling = 'Min = 0; Max range = 100', attr.order = 'As is', output = 'Data')", null);
}


See also