For testing purposes I made up some correlated data in R like this:
mydata = data.frame( outcome = c(1, 0, 1, 0, 0, 1, 1, 0, 1, 1), predictor = c(0.1, -0.2, 0, 0.1, -0.3, 0.3, 0.2, -0.1, 0.1, 0.1) )
Then I did this in order to create a logistic model that modeled this data:
model1 = glm(family = binomial, formula = outcome ~ predictor, data = mydata)
plot(model1) yields the following plots:
I need answers to some questions in order to understand how to perform diagnostics on such a logistic model. As someone with only an introductory course in statistics I'm having trouble gathering knowledge on how to interpret the plots.
- What do the "Predicted Values" in the first plot represent?
- What does residual mean in the context of logistic regression?
- Which of these plots can in any way be useful for model diagnostics based on real data? How?