R Statistics Cookbook

How to do it...

In this recipe, we will investigate how robust logistic regression handles extreme observations. The mechanics will be the same that we implemented for robust linear regression (we will generate data, contaminate it, and see how our estimates change).

1. First, load the robust package:

library(robust)

2. We first generate 1000 observations for logistic regression, and we try both logistic regression and robust logistic regression. Of course, both methods yield correct results in the absence of contamination:

set.seed(1000)x1 = rnorm(1000)  x2 = rnorm(1000)link_val = 2 + 2*x1 + 5*x2  pr = 1/(1+exp(-link_val))     y = rbinom(1000,1,pr)  df = data.frame(y=y,x1=x1,x2=x2)glm(y~x1+x2,data=df,family="binomial")robust::glmRob(y~x1+x2,data=df,family="binomial") ...