[R] Potential Issue with lm.influence
Fox, John
j|ox @end|ng |rom mcm@@ter@c@
Wed Apr 3 16:03:38 CEST 2019
Hi Peter,
Yes, that's another reflection of the degree to which Jupiter and Saturn are out of line with the data for the other planet when you fit the very unreasonable negative binomial model with Volume untransformed.
Best,
John
> On Apr 3, 2019, at 5:36 AM, peter dalgaard <pdalgd using gmail.com> wrote:
>
> Yes, also notice that
>
>> predict(fit3, new=moon_data, type="resp")
> 1 2 3 4 5 6
> 1.060694e+00 1.102008e+00 1.109695e+00 1.065515e+00 1.057896e+00 1.892312e+29
> 7 8 9 10 11 12
> 3.531271e+17 2.295015e+01 1.739889e+01 1.058165e+00 1.058041e+00 1.057957e+00
> 13
> 1.058217e+00
>
>
> so the model of fit3 predicts that Jupiter and Saturn should have several bazillions of moons each!
>
> -pd
>
>
>
>> On 3 Apr 2019, at 01:53 , Fox, John <jfox using mcmaster.ca> wrote:
>>
>> Dear Eric,
>>
>> Have you looked at your data? -- for example:
>>
>> plot(log(Moons) ~ Volume, data = moon_data)
>> text(log(Moons) ~ Volume, data = moon_data, labels=Name, adj=1, subset = Volume > 400)
>>
>> The negative-binomial model doesn't look reasonable, does it?
>>
>> After you eliminate Jupiter there's one very high leverage point left, Saturn. Computing studentized residuals entails an approximation to deleting that as well from the model, so try fitting
>>
>> fit3 <- update(fit, subset = !(Name %in% c("Jupiter ", "Saturn ")))
>> summary(fit3)
>>
>> which runs into numeric difficulties.
>>
>> Then look at:
>>
>> plot(log(Moons) ~ Volume, data = moon_data, subset = Volume < 400)
>>
>> Finally, try
>>
>> plot(log(Moons) ~ log(Volume), data = moon_data)
>> fit4 <- update(fit2, . ~ log(Volume))
>> rstudent(fit4)
>>
>> I hope this helps,
>> John
>>
>> -----------------------------------------------------------------
>> John Fox
>> Professor Emeritus
>> McMaster University
>> Hamilton, Ontario, Canada
>> Web: https://socialsciences.mcmaster.ca/jfox/
>>
>>
>>
>>
>>> -----Original Message-----
>>> From: R-help [mailto:r-help-bounces using r-project.org] On Behalf Of Eric
>>> Bridgeford
>>> Sent: Tuesday, April 2, 2019 5:01 PM
>>> To: Bert Gunter <bgunter.4567 using gmail.com>
>>> Cc: R-help <r-help using r-project.org>
>>> Subject: Re: [R] Fwd: Potential Issue with lm.influence
>>>
>>> I agree the influence documentation suggests NaNs may result; however, as
>>> these can be manually computed and are, indeed, finite/existing (ie,
>>> computing the held-out influence by manually training n models for n points
>>> to obtain n leave one out influence measures), I don't possibly see how the
>>> function SHOULD return NaN, and given that it is returning NaN, that
>>> suggests to me that there should be either a) Providing an alternative
>>> method to compute them that (may be slower) that returns the correct
>>> results in the even that lm.influence does not return a good approximation
>>> (ie, a command line argument for type="approx" that does the
>>> approximation strategy employed currently, or an alternative type="direct"
>>> or something like that that computes them manually), or b) a heuristic to
>>> suggest why NaNs might result from one's particular inputs/what can be
>>> done to fix it (if the approximation strategy is the source of the problem) or
>>> what the issue is with the data that will cause NaNs. Hence I was looking to
>>> start a discussion around the specific strategy employed to compute the
>>> elements.
>>>
>>> Below is the code:
>>> moon_data <- structure(list(Name = structure(c(8L, 13L, 2L, 7L, 1L, 5L, 11L,
>>> 12L, 9L, 10L, 4L, 6L, 3L), .Label = c("Ceres ", "Earth",
>>> "Eris ",
>>>
>>> "Haumea ", "Jupiter ", "Makemake ", "Mars ", "Mercury ", "Neptune ",
>>>
>>> "Pluto ", "Saturn ", "Uranus ", "Venus "), class = "factor"),
>>> Distance = c(0.39, 0.72, 1, 1.52, 2.75, 5.2, 9.54, 19.22,
>>> 30.06, 39.5, 43.35, 45.8, 67.7), Diameter = c(0.382, 0.949,
>>>
>>> 1, 0.532, 0.08, 11.209, 9.449, 4.007, 3.883, 0.18, 0.15,
>>>
>>> 0.12, 0.19), Mass = c(0.06, 0.82, 1, 0.11, 2e-04, 317.8,
>>>
>>> 95.2, 14.6, 17.2, 0.0022, 7e-04, 7e-04, 0.0025), Moons = c(0L,
>>>
>>>
>>> 0L, 1L, 2L, 0L, 64L, 62L, 27L, 13L, 4L, 2L, 0L, 1L), Volume =
>>> c(0.0291869497930152,
>>>
>>>
>>>
>>> 0.447504348276571, 0.523598775598299, 0.0788376225681443,
>>>
>>>
>>>
>>> 0.000268082573106329, 737.393372232996, 441.729261571372,
>>>
>>>
>>>
>>> 33.6865588825666, 30.6549628355953, 0.00305362805928928,
>>>
>>>
>>>
>>> 0.00176714586764426, 0.00090477868423386, 0.00359136400182873
>>>
>>>
>>> )), row.names = c(NA, -13L), class = "data.frame")
>>>
>>> fit <- glm.nb(Moons ~ Volume, data = moon_data)
>>> rstudent(fit)
>>>
>>> fit2 <- update(fit, subset = Name != "Jupiter ")
>>> rstudent(fit2)
>>>
>>> influence(fit2)$sigma
>>>
>>> # 1 2 3 4 5 7 8 9
>>> 10 11 12 13
>>> # 1.077945 1.077813 1.165025 1.181685 1.077954 NaN 1.044454 1.152110
>>> 1.187586 1.181696 1.077954 1.165147
>>>
>>> Sincerely,
>>> Eric
>>>
>>> On Tue, Apr 2, 2019 at 4:38 PM Bert Gunter <bgunter.4567 using gmail.com>
>>> wrote:
>>>
>>>> Also, I suggest you read ?influence which may explain the source of
>>>> your NaN's .
>>>>
>>>> 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 1:29 PM Bert Gunter <bgunter.4567 using gmail.com>
>>> wrote:
>>>>
>>>>> I told you already: **Include code inline **
>>>>>
>>>>> See ?dput for how to include a text version of objects, such as data
>>>>> frames, inline.
>>>>>
>>>>> Otherwise, I believe .txt text files are not stripped if you insist
>>>>> on
>>>>> *attaching* data or code. Others may have better advice.
>>>>>
>>>>>
>>>>> 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 1:21 PM Eric Bridgeford <ericwb95 using gmail.com>
>>>>> wrote:
>>>>>
>>>>>> 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 using 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 using 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/a28e609e72ed7c47f6ddfb
>>>>>>>> b86c85279a0750f0b7/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/sta
>>>>>>>> ts/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 using 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
>>>>>>
>>>>>
>>>
>>> --
>>> Eric Bridgeford
>>> ericwb.me
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help using 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.
>>
>> ______________________________________________
>> R-help using 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.
>
> --
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Office: A 4.23
> Email: pd.mes using cbs.dk Priv: PDalgd using gmail.com
>
>
>
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