# Example Probit Regression

I try to set up a probit model in R. At first I want to model the typical example of commuters deciding between driving by car or using the train instead. There are the following coefficients:

$\beta_1$ : 1, if train is used; 0, otherwise

$\beta_2$ : Travel time (min)

$\beta_3$ : Cost (Euro)

$\beta_4$ : Income - 0, if train is used; yearly income value, otherwise

How does the underlying dataset have to look like? Is this example valid?

NR | CHOICE | TIME | COST | INCOME
1  | 1      | 30   | 4    | 0
2  | 0      | 20   | 6    | 24000
3  | 1      | 23   | 3    | 0
4  | 0      | 34   | 7    | 19500
...| ...    | ...  | ...  | ...


If not, can someone provide an example for a dataset that would be valid for an R model?

If this example is correct, can I go on with the estimation like this?

probit <- glm (CHOICE ~ TIME + COST + INCOME,
family = binomial(link = "probit"), data = dataset)


I know that there are much more possible factors for the estimation, but I tried to keep the reproducible example short and simple. Thanks for your help.

EDIT: My example is a slightly altered version of an example in a book. $\beta_1$ (CHOICE) is explicitely an independent variable there! Called an alternative-specific constant. The actual context would be

probit <- glm (NEW_DEPENDENT_VARIABLE ~ CHOICE + TIME + COST + INCOME,
family = binomial(link = "probit"), data = dataset)

• Here is an example for a probit Regression in R. stats.idre.ucla.edu/r/dae/probit-regression – Ferdi Mar 21 '17 at 8:25
• I already found this article, but I was a bit confused about the example in the book, so I wanted to validate that special example. Thanks for your help anyway @Ferdi! :) – Felix Grossmann Mar 21 '17 at 8:31

Yes, this is a valid setup. The only really crucial thing is that the dependent variable here does in fact only ever take on a zero or a one. Especially in R, you generally don't need to restructure your data in order to fit a model; of more concern is when you want to generate meaningful predictions using your model, especially with nonlinear models like this one where the marginal effects of any changes in the IVs depend on the values of the dependent variable and all the other IVs. Lastly, if and when you generate predictions, make sure you specify type="response" or you'll instead generate changes in the latent variable, which you probably don't care about. And of course, I assume you know that you do need to generate predictions as such, because the coefficients are only directly interpretable insofar as their signs and significance levels are concerned.