Logistic response models

Building Response Models in R

Kathrin Gruber

Assistant Professor of Econometrics Erasmus University Rotterdam

Logistic response function

Bounds model predictions between 0 and 1.
Assumes a curved relationship.

A logistic model for beer demand

logistic.model <- glm(HOPPINESS ~ price.ratio, family = binomial, 
                      data = choice.data)
coef(logistic.model)

(Intercept)  price.ratio 
  -3.572678   -6.738768

Bounded predictions

plot(HOPPINESS ~ price.ratio, data = choice.data)
curve(predict(logistic.model, data.frame(price.ratio = x), 
              type = "response"), add = TRUE)

Average marginal effects

Nonlinear logistic model:

margins(logistic.model)

price.ratio
    -0.4585

Linear probability model

coef(probability.model)

(Intercept)  price.ratio 
 0.09700236  -0.29594939

Effect plots

x <- seq(-1.25, 1.25, by = 0.25)
cplot(logistic.model, "price.ratio", xvals = x)

Let's practice!

Building Response Models in R