It seems that the standardized deviance residulas, that one gets on plots of a glm.object x with plot(x) are calculated as
r <- residuals(x) s <- sqrt(deviance(x)/df.residual(x)) w <- weights(x) hii <- lm.influence(x)$hat r.w <- if (is.null(w)) r else (sqrt(w) * r) rs <- r.w/(s * sqrt(1 - hii)) This implies that, for example, for binomial B(ni,pi) data the devaince residials (which are just r) are weighted not only with sqrt(1-hii), but also with 1/sqrt(ni) and s, leading to absurd values. As a result all leverage/outlier diagnostics is absolutly wrong. Am I right and this should be reported as a bug? Many thanks, Tatyana -- Tatyana Krivobokova Bielefeld University Department of Economics and Business Administration PF 100131, D-33501 Bielefeld +49 (0)521 106 4876 http://www.wiwi.uni-bielefeld.de/~krivobokova ______________________________________________ R-help@stat.math.ethz.ch 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.
