R Tutorial: Fitting and interpreting a choice model

Want to learn more? Take the full course at https://learn.datacamp.com/courses/choice-modeling-for-marketing-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that we've inspected the data, we are ready to fit a choice model. The process is very similar to fitting a regression model, so let's start with a quick refresher on that. To fit a linear regression model, we use the function lm(). When we type this command, we are telling R to fit a model to predict y as a function of x1, x2, and x3 using the data in the my_data data frame. If lm_data doesn't include columns named y, x1, x2, and x3 you will get an error. We usually take the output of lm() and assign it to a model object that we can use later. Here we are assigning it to my_model. Once we have the my_model object we can see a summary of the model by typing summary of my_model. The process for fitting a choice model is very similar to fitting a linear regression model, except that we use a different function called mlogit(). Multinomial logit models are somewhat specialized, so you can't estimate them with lm() or even with the glm() function that you may have used before. Instead, we use the mlogit() function from the mlogit package. Just as with lm(), there are two key inputs to mlogit(): a formula and the name of the data frame where the data is stored. The data input is pretty straightforward, but the data has to be choice data. That means it has to have a column that indicates which choice observation each alternative belongs to. Here that is the ques column. It also has to have a column of 0's and 1's indicating which option was chosen, and here that is labeled choice. The formula that we use should always begin with the name of the column that indicates the choice because we want to predict the choice. Then we type a tilde and after the tilde we list the names of the product features we want to use to predict the choice