*by David Lillis, Ph.D.*

In our last article, we learned about model fit in Generalized Linear Models on binary data using the glm() command. We continue with the same glm on the mtcars data set (regressing the *vs* variable on the *weight* and *engine displacement*).

Now we want to plot our model, along with the observed data.

Although we ran a model with multiple predictors, it can help interpretation to plot the predicted probability that *vs*=1 against each predictor separately. So first we fit a glm for only [Read more…] about Generalized Linear Models in R, Part 3: Plotting Predicted Probabilities