Remove the intercept

In the last exercise, we forgot to exclude the intercept. When we estimate a model with the intercept, it estimates parameters for each of the alternatives. For this conjoint data, the alternative number just indicates which chocolate bar was presented on the left, middle or right in the survey question. It doesn't make much sense to assume that the position would affect the choice.

This exercise is part of the course

Choice Modeling for Marketing in R

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Exercise instructions

The choc_m1 object is still loaded. Use summary(choc_m1) to view the output. Find the parameters for the intercepts. They are close to zero and non-significant, so we can remove them.
Modify the call to mlogit() to exclude the intercept by adding 0 + just after the ~ in the formula. Assign the output to choc_m2. You can also remove the print.level = 3 option.
Call summary(choc_m2) to confirm that the intercepts are removed. Did the other parameters change much?

Hands-on interactive exercise

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

# summarize the choc_m1 model that includes the intercepts


# modify the call to mlogit to exclude the intercept
choc_m2 <- mlogit(Selection ~ Brand + Type + Price, data = chocolate, print.level=3)

# summarize the choc_m2 model

Edit and Run Code

Choice Modeling for Marketing in R

AdvancedSkill Level

4.8+

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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