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Exercise

Chocolate model with random price coefficient

Okay, we're ready to fit a hierarchical model to the chocolate data. Let's start with the code we had before to estimate a non-hierarchical choice model and modify it to estimate a model where the Price parameter is normally distributed. The chocolate data is still loaded.

Instructions
100 XP
  • Add the input id.var = "Subject" to mlogit.data(). This tells mlogit.data() which person answered each question.
  • Add the rpar input to mlogit(). It should be equal to c(Price = "n") to indicate that you want the coefficient for Price to be normally distributed.
  • Add the panel = TRUE input to mlogit() to tell it that you want to assume each Subject has his or her own Price coefficient.
  • Plot the the hierarchical model by typing plot(choc_m6).
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