Create mlogit.data object

I've pre-loaded the mlogit package and a data frame called chocolate_df that has the chocolate data in long format. We want to fit a choice model to this data, but first we need to convert it to an mlogit.data object. This can be tricky, but it is vital to using mlogit() successfully. Before you start writing code, type head(chocolate_df) directly into the console so that you can refer to the column names.

This exercise is part of the course

Choice Modeling for Marketing in R

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Exercise instructions

Identify the names of the columns where the choice and the alternative numbers are stored. Fill in the names of the columns for the choice and the alt.var in the mlogit.data() command.
Identify the column numbers where the attributes Brand, Price and Type are stored. Pass these column numbers as a vector for the varying input in the mlogit command.
Use str() to confirm that you've converted the data to an mlogit.data object.

Hands-on interactive exercise

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

# use mlogit.data() to convert chocolate_df to mlogit.data
chocolate <- mlogit.data(chocolate_df, shape = "long",
                         choice = "____", alt.var = "____", 
                         varying = ____)
                         
# use str() to confirm that chocolate is an mlogit.data object

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