Fit a choice model

Okay, now that you've created the mlogit.data object called chocolate, you are ready to call mlogit() to fit a model. You might want to type head(chocolate) into the console so that you can look at the data while you fill in the inputs for the mlogit() function. The Selection column in chocolate contains the choice. It can be TRUE/FALSE, 0/1, or no/yes. The attributes are in the Brand, Type, and Price columns.

This exercise is part of the course

Choice Modeling for Marketing in R

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Exercise instructions

Fill in the inputs to the mlogit command. The first input to mlogit() is always a formula. The formula should be Selection ~ Brand + Type + Price.
The second input to mlogit() should be data = chocolate.
For the third input to mlogit() use the option print.level = 3, which tells mlogit() to report more details on the maximum likelihood algorithm.
Once you've run mlogit and assigned the output to choc_m1, use the summary function to inspect the model output.

Hands-on interactive exercise

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

# Fit a model with mlogit() and assign it to choc_m1
choc_m1 <- mlogit(___, ___, ___)

# Summarize choc_m1 with summary()

Edit and Run Code

Choice Modeling for Marketing in R

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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