Multinomial Logistic Regression

1. Multinomial Logistic Regression

2. Read the data • use http://www.ats.ucla.edu/stat/stata/dae/mlogit, clear • Describe the data • Codebook • Summarize • Tabulate • Make graphs

6. The outcome variable is brand. The variable female is coded as 0 for male and 1 for female. Let's start with some descriptive statistics of the variables of our interest.

12. Using the Multinomial Logit Model Now we have warmed up to building our model. Our goal is to associate the brand choices with age and gender. We will assume a linear relationship between the transformed outcome variable and our predictor variables female and age. Since there are multiple categories, we will choose a base category as the comparison group. Here our choice is the first brand (brand=1).

16. The output above has two parts, labeled with the categories of the outcome variable brand. log(P(brand=2)/P(brand=1)) = b_10 + b_11*female + b_12*agelog(P(brand=3)/P(brand=1)) = b_20 + b_21*female + b_22*age, with b's being the raw regression coefficients from the output.

17. For example, we can say that for one unit change in the variable age, the log of the ratio of the two probabilities, P(brand=2)/P(brand=1), will be increased by 0.368, and the log of the ratio of the two probabilities P(brand=3)/P(brand=1) will be increased by 0.686. • Therefore, we can say that, in general, the older a person is, the more he/she will prefer brand 2 or 3.

18. The ratio of the probability of choosing one outcome category over the probability of choosing the reference category is often referred as relative risk (and it is also sometimes referred as odds).  • We can use the rrr option for mlogit command to display the regression results in the language of risk.

23. We can also present the regression result graphically. For example, we can create three variables p1, p2 and p3 for the predicted probabilities and plot them against a predictor variable. In the example below, we plot p1 against age separated by the variable female.