I don't see what more could usefully be
> done.
>
> Best,
> John
>
> > On Apr 2, 2019, at 9:08 PM, Eric Bridgeford wrote:
> >
> > Hey John,
> >
> > I am aware they are high leverage points, and that the model is not the
> > best for th
t;>> 0.00176714586764426, 0.00090477868423386, 0.00359136400182873
> >>>
> >>>
> >>> )), row.names = c(NA, -13L), class = "data.frame")
> >>>
> >>> fit <- glm.nb(Moons ~ Volume, data = moon_data)
> >>> rst
hich 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
s occurring there and not in the "influence" function.
>
> Jim
>
> On Wed, Apr 3, 2019 at 9:12 AM Eric Bridgeford wrote:
> >
> > I agree the influence documentation suggests NaNs may result; however, as
> > these can be manually computed and are, indeed, fin
ou 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
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
>
> On Tue, Apr 2, 2019 at 11:32 AM Eric Bridgeford
> wrote:
>
>> Hi R core team,
>>
>> I experienced the following issue with the attached data/code snippet,
>> where the stu
g 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,
--
Eri
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