The Cook's distances given in plot.lm are inconsistent for weighted least squares. Randomly generated test case: x <- c(0.254, 0.638, 0.957, 0.553, 0.983, 0.511, 0.933, 0.428, 0.486, 0.382) y <- c(7.010, 8.400, 11.769, 7.491, 11.925, 7.197, 10.829, 7.673, 7.106, 7.160) weights <- c(0.224, 1.188, 1.543, 0.075, 1.070, 0.648, 0.185, 0.059, 0.136, 0.271) out <- lm(y ~ x, weights=weights) par(mfrow=c(1,2)) plot(out, c(4,5)) The Cook's Distance plot shows that point 1 has a Cook's distance between 3 and 3.5, but the Residuals vs Leverage contours show that it is between 0.5 and 1. cooks.distance(out) and influence.measures(out) both give output matching the Residuals vs Leverage plot. The difference results from the Residuals vs Leverage plot using weighted residuals and the Cook's Distance plot using the unweighted residuals. This can be seen in plot.lm, where the Cook's distances are calculated with if (any(show[4L:6L])) { cook <- if (isGlm) cooks.distance(x) else cooks.distance(x, sd = s, res = r) } where r is residuals(x), rather than weighted.residuals(x). I am not sure which definition of Cook's distance is correct for weighted least squares, but at the least the plots should be consistent with each other. > version _ platform x86_64-apple-darwin10.8.0 arch x86_64 os darwin10.8.0 system x86_64, darwin10.8.0 status major 3 minor 1.2 year 2014 month 10 day 31 svn rev 66913 language R version.string R version 3.1.2 (2014-10-31) nickname Pumpkin Helmet