Thank you, Martin. I agree with the subjective remark. But that's a different conversation!
The fix is quite easy. The difference mainly stems from the fact that R uses "deviance" residuals instead of "working" residuals. I will proceed as per your advice. Thanks & Best regards, Ravi ________________________________ From: R-devel <[email protected]> on behalf of Martin Maechler <[email protected]> Sent: Thursday, October 9, 2025 04:55 To: Ravi Varadhan <[email protected]> Cc: R Development List <[email protected]> Subject: Re: [Rd] Inaccuracy in DFBETA calculation for GLMs External Email - Use Caution >>>>> Ravi Varadhan via R-devel >>>>> on Sat, 4 Oct 2025 13:34:48 +0000 writes: > Hi, > I have been calculating sensitivity diagnostics in GLMs. I am noticing that the dfbeta() and influence() functions in base R are inaccurate for non-Gaussian GLMs. Even though the help says that the DFBETAs can be inaccurate for GLMs, the accuracy can be substantially improved. > I was thinking of writing this up along with a proper fix to R Journal but then started wondering whether this is a well-known issue and it has been addressed in other packages. > Has the inaccuracy of DFBETA been addressed already? > Thank you, > Ravi As nobody has replied till now: No, I haven't heard yet about such properties and even less that and how they can be substantially improved (I assume you have "searched the net" for that). I agree that this would probably be a nice R journal paper when accompanied with both math and code. A subjective remark: Being statistically educated from ETH Zurich and similar places (UW Seattle, Bellcore): I've been convinced that such "leave-one-out" diagnostics are not providing "true robustness" (against violiation of error distribution assumptions etc), but one should rather use M- (and MM-)estimation approaches providing a guaranteed breakdown point above 2/n (or so, which I think is what you get with such L.o.o. diagnostics: just look at the effect of one huge outlier masking a large one). For that reason, I would not want to substantially blow up our base R code underlying DFBETA (which then has to be kept maintained into "all" future), but then I'm only speaking for myself and not all of R core (and even less all of R using statisticians). Martin ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-devel<https://stat.ethz.ch/mailman/listinfo/r-devel> [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
