.
>>
>> 32.03 61.18 97.20 112.20 165.00 226.00
>>
>>
>>
>> Value range of observed data:
>>
>>
>>> summary(nb_unique)
>>>
>>Min. 1st Qu. Median Mean 3rd Qu.Max.
>>
>> 43.00 67.00 81
ment and offset().
> Thanks a lot for your help.
> Have a nice day
> Marine
>
>
>
>
> De : peter dalgaard
> Envoy� : mardi 22 novembre 2016 23:52
> � : Bert Gunter
> Cc : Marine Regis; r-help@r-project.org
> Objet : Re: [R] GAM with the
e different between
models with offset=argument and offset().
Thanks a lot for your help.
Have a nice day
Marine
De : peter dalgaard
Envoy� : mardi 22 novembre 2016 23:52
� : Bert Gunter
Cc : Marine Regis; r-help@r-project.org
Objet : Re: [R] GAM with the negative
> On 22 Nov 2016, at 23:07 , Bert Gunter wrote:
>
> Define "very different." Sounds like a subjective opinion to me, for
> which I have no response. Apparently others are similarly flummoxed.
> Of course they would not in general be identical.
Er? I don't see much reason to disagree that a ran
Well part of the issue is that the negative binomial estimates are for
means and they can differ a fair bit from the raw counts, but I'm also
guessing that part of the issue is that the offset may not be accounted for
with the predict.gam() function.
Brian
Brian S. Cade, PhD
U. S. Geological Sur
> On Nov 22, 2016, at 1:29 PM, Marine Regis wrote:
>
> Hello,
>
>> From capture data, I would like to assess the effect of longitudinal changes
>> in proportion of forests on abundance of skunks. To test this, I built this
>> GAM where the dependent variable is the number of unique skunks and
Define "very different." Sounds like a subjective opinion to me, for
which I have no response. Apparently others are similarly flummoxed.
Of course they would not in general be identical.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticki
Hello,
>From capture data, I would like to assess the effect of longitudinal changes
>in proportion of forests on abundance of skunks. To test this, I built this
>GAM where the dependent variable is the number of unique skunks and the
>independent variables are the X coordinates of the centroid
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