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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.

Diese Übung ist Teil des Kurses

Choice Modeling for Marketing in R

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Anleitung zur Übung

  • 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).

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# add id.var input to mlogit.data call
chocolate <- mlogit.data(chocolate, choice = "Selection", shape="long", 
                         varying=6:8, alt.var = "Alt", ____)
                         
# add rpar and panel inputs to mlogit call
choc_m6 <- mlogit(Selection ~ 0 + Brand + Type + Price, data = chocolate, 
                  ____, ____)

# plot the model
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