Question

I’m trying to run a logit model and plot the probability curve for a number of the important predictors. I’m trying to do this
with the Effects package.

df=data.frame(income=c(5,5,3,3,6,5),
              won=c(0,0,1,1,1,0),
              age=c(18,18,23,50,19,39),
              home=c(0,0,1,0,0,1))
str(df)

md1 = glm(factor(won) ~ income + age + home, 
          data=df, family=binomial(link="logit"))

summary(md1)

plot(effect("income", md1), grid=TRUE)

But I want to know how to plot a graph so that it shows the probability of won (response) based on income (or any of the other predictors).

However, what I want to do is generate the same plot, with won on the y axis and income on x axis, but the curves showing the probabilities for age and home (in separate plots).

Not seeing how to do this in the effects documentation. Help!

Thanks.

Answer 1

If I understand you correctly, you’ll need to introduce an interaction term into the model. For example,

md1 <- glm(factor(won) ~ income*age + home,
  data=df, family=binomial(link="logit"))

with(df, mean(age))
#[1] 27.83333
with(df, mean(age)-sd(age))
#[1] 14.33642
with(df, mean(age)+sd(age))
#[1] 41.33025

plot(effect("income*age", md1, 
  xlevels=list(income=0:10, age=c(14.3, 27.8, 41.3))), multiline=TRUE)

Will show you the effect of income on won at different values of age.