Fitting and interpreting a choice model

Choice Modeling for Marketing in R

Elea McDonnell Feit

Assistant Professor of Marketing, Drexel University

Fitting a linear model with lm()

To fit a linear regression model:

my_model <- lm(y ~ x1 + x2 + x3, data = lm_data)
summary(my_model)

lm_data data frame

y	x1	x2	x3
3	2	7	2
1	1	7	8
3	2	4	6

library(mlogit)
mymodel <- mlogit(choice ~ feature1 + feature2 + feature3, 
                  data = choice_data)

choice_data

ques	alt	choice	feature1	feature2	feature3
1	1	1	low	high	low
1	2	0	low	high	high
1	3	0	high	high	low
2	1	0	high	low	high
2	2	1	high	high	low
2	3	0	low	low	low

Summary of mlogit() model object

summary(mymodel)

...

Coefficients :
                Estimate Std. Error  t-value  Pr(>|t|)    
feature1low    -0.0322059  0.0740839  -0.4347    0.6638    
feature2low     0.4546283  0.0727445   6.2497 4.114e-10 ***
feature3low    -1.2926911  0.0648649 -19.9290 < 2.2e-16 ***

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
...

Let's find out how people value the features of sports cars.

Choice Modeling for Marketing in R