Dear all! Sometimes, when using plain lm(), if I have highly correlated numeric predictors (covariates), I firstly ran lm() to get residuals of one from the other. This way, they became uncorrelated (orthogonal), and I can check if the residualized one contributes to prediction, over and above the other. I do that if and only if there is "theoretical" sense/ground for such step. Now, I would need to run binomial glm, but I think that those residuals may "trick" me. Logic of glm's various types of residuals tells me that the "response" should be a match to those which I would get from simple lm(). However, I do not want to wander blindly into this. Please, does anyone have experience with using glm.residuals on the right-hand side of main lm() model? What to use, how and why? Any advice on literature?
Best, Petar ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
