Hi R core team, I experienced the following issue with the attached data/code snippet, where the studentized residual for a single observation appears to be NaN given finite predictors/responses, which appears to be driven by the glm.influence method in the stats package. I am curious to whether this is a consequence of the specific implementation used for computing the influence, which it would appear is the driving force for the NaN influence for the point, that I was ultimately able to trace back through the lm.influence method to this specific line <https://github.com/SurajGupta/r-source/blob/a28e609e72ed7c47f6ddfbb86c85279a0750f0b7/src/library/stats/R/lm.influence.R#L67> which calls C code which calls iminfl.f <https://github.com/SurajGupta/r-source/blob/master/src/library/stats/src/lminfl.f> (I don't know fortran so I can't debug further). My understanding is that the specific issue would have to do with the leave-one-out variance estimate associated with this particular point, which it seems based on my understanding should be finite given finite predictors/responses. Let me know. Thanks! Sincerely, -- Eric Bridgeford ericwb.me
Nothing was attached. The r-help server strips most attachments. Include your code inline. Also note that> 0/0[1] NaN so maybe something like that occurs in the course of your calculations. But that's just a guess, so feel free to disregard. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Apr 2, 2019 at 11:32 AM Eric Bridgeford <ericwb95 at gmail.com> wrote:> Hi R core team, > > I experienced the following issue with the attached data/code snippet, > where the studentized residual for a single observation appears to be NaN > given finite predictors/responses, which appears to be driven by the > glm.influence method in the stats package. I am curious to whether this is > a consequence of the specific implementation used for computing the > influence, which it would appear is the driving force for the NaN influence > for the point, that I was ultimately able to trace back through the > lm.influence method to this specific line > < > https://github.com/SurajGupta/r-source/blob/a28e609e72ed7c47f6ddfbb86c85279a0750f0b7/src/library/stats/R/lm.influence.R#L67 > > > which > calls C code which calls iminfl.f > < > https://github.com/SurajGupta/r-source/blob/master/src/library/stats/src/lminfl.f > > > (I > don't know fortran so I can't debug further). My understanding is that the > specific issue would have to do with the leave-one-out variance estimate > associated with this particular point, which it seems based on my > understanding should be finite given finite predictors/responses. Let me > know. Thanks! > > Sincerely, > > -- > Eric Bridgeford > ericwb.me > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. >[[alternative HTML version deleted]]
How can I add attachments? The following two files were attached in the initial message On Tue, Apr 2, 2019 at 3:34 PM Bert Gunter <bgunter.4567 at gmail.com> wrote:> Nothing was attached. The r-help server strips most attachments. Include > your code inline. > > Also note that > > > 0/0 > [1] NaN > > so maybe something like that occurs in the course of your calculations. > But that's just a guess, so feel free to disregard. > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Tue, Apr 2, 2019 at 11:32 AM Eric Bridgeford <ericwb95 at gmail.com> > wrote: > >> Hi R core team, >> >> I experienced the following issue with the attached data/code snippet, >> where the studentized residual for a single observation appears to be NaN >> given finite predictors/responses, which appears to be driven by the >> glm.influence method in the stats package. I am curious to whether this is >> a consequence of the specific implementation used for computing the >> influence, which it would appear is the driving force for the NaN >> influence >> for the point, that I was ultimately able to trace back through the >> lm.influence method to this specific line >> < >> https://github.com/SurajGupta/r-source/blob/a28e609e72ed7c47f6ddfbb86c85279a0750f0b7/src/library/stats/R/lm.influence.R#L67 >> > >> which >> calls C code which calls iminfl.f >> < >> https://github.com/SurajGupta/r-source/blob/master/src/library/stats/src/lminfl.f >> > >> (I >> don't know fortran so I can't debug further). My understanding is that the >> specific issue would have to do with the leave-one-out variance estimate >> associated with this particular point, which it seems based on my >> understanding should be finite given finite predictors/responses. Let me >> know. Thanks! >> >> Sincerely, >> >> -- >> Eric Bridgeford >> ericwb.me >> ______________________________________________ >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> 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. >> >-- Eric Bridgeford ericwb.me