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.

This exercise is part of the course

Choice Modeling for Marketing in R

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Exercise instructions

A hierarchical model needs to know which person made each choice. Add the Subject column to the first index in dfidx() so that each choice question is linked to the person who answered it.
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 hierarchical model by typing plot(choc_m6).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# add Subject to the first index of dfidx()
chocolate <- dfidx(chocolate, idx = list(c("chid", ____), "Alt"), 
                   choice = "Selection")

# add rpar and panel inputs to mlogit call
choc_m6 <- mlogit(Selection ~ 0 + Brand + Type + Price, data = chocolate, 
                  ____, ____)

# plot the model

Edit and Run Code

Choice Modeling for Marketing in R

AdvancedSkill Level

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Our goal for this chapter is to get you through the entire choice modeling process as quickly as possible, so that you get a broad understanding of what we can do with choice models and how the choice modeling process works. The main idea here is that we can use a choice model to understand how customers' product choices depend on the features of those products. Do sportscar buyers prefer manual transmissions to automatic? By how much? In order to give you an overview, we will skip over many of the details. In later chapters, we will go back and cover important issues in preparing data, specifying and interpreting models and reporting your findings, so that you are fully prepared to use these methods with your own choice data.

Exercise 1: Why choice?Exercise 2: Choice data Exercise 3: Inspecting choice data Exercise 4: Finding the levels of a factor Exercise 5: Inspecting a choice observation Exercise 6: What did people choose?Exercise 7: Fitting and interpreting a choice model Exercise 8: Fitting a choice model Exercise 9: Interpreting parameters Exercise 10: Using choice models to make decisions Exercise 11: Predicting choice shares Exercise 12: Plotting choice shares