Bounded predictions

The glm() function is used because you need a response function that bounds the model predictions between zero and one. Let's illustrate this effect in a graph. But this time you need to draw a curve (instead of a straight line)!

You start with plotting the relation HOPPINESS ~ price.ratio. You can add the logistic function to the plot by using curve(). The curve() function is used to evaluate another function at x data points. Here, this function is predict()! The predict() function retrieves the coefficients of the logistic.model to make predictions about some values provided in a data frame object. The trick is to set price.ratio = x in the data.frame argument. This fits a curve through the predicted data values.

Display the relation between HOPPINESS and price.ratio by using the function plot().
Obtain the purchase probabilities for Hoppiness by applying the function predict() on the logistic.model with price.ratio in the data frame argument.
Fit a curve through the predicted data values by using the function curve().

Exercise

Bounded predictions

Instructions