ct.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Jhope
> Sent: Thursday, January 26, 2012 2:26 PM
> To: r-help@r-project.org
> Subject: Re: [R] How do I compare 47 GLM models with 1 to 5
> interactions and unique combinations?
>
> I ask the question about when
28
> Para: r-help@
> Asunto: Re: [R] How do I compare 47 GLM models with 1 to 5 interactions
> and unique combinations?
>
> Ruben, I'm not sure you are understanding the ramifications of what Bert
> said. In addition you are making several assumptions implicitly:
>
> --
&g
-Mensaje original-
De: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] En
nombre de Frank Harrell
Enviado el: viernes, 27 de enero de 2012 14:28
Para: r-help@r-project.org
Asunto: Re: [R] How do I compare 47 GLM models with 1 to 5 interactions and
unique combinations
: Rubén Roa
> CC: Ben Bolker; Frank Harrell
> Asunto: Re: [R] How do I compare 47 GLM models with 1 to 5 interactions
> and unique combinations?
>
> On Wed, Jan 25, 2012 at 11:39 PM, Rubén Roa <rroa@> wrote:
>> I think we have gone through this before.
>> First, the de
-Mensaje original-
De: Bert Gunter [mailto:gunter.ber...@gene.com]
Enviado el: jueves, 26 de enero de 2012 21:20
Para: Rubén Roa
CC: Ben Bolker; Frank Harrell
Asunto: Re: [R] How do I compare 47 GLM models with 1 to 5 interactions and
unique combinations?
On Wed, Jan 25, 2012 at 11:39
Simple question. 8 million pages in the statistical literature of
answers. What, indeed, is the secret to life?
Post on a statistical help list (e.g. stats.stackexchange.com). This
has almost nothing to do with R. Be prepared for an onslaught of often
conflicting "wisdom."
-- Bert
On Thu, Jan 26
I ask the question about when to stop adding another variable even though it
lowers the AIC because each time I add a variable the AIC is lower. How do I
know when the model is a good fit? When to stop adding variables, keeping
the model simple?
Thanks, J
--
View this message in context:
http://
eans lower expected K-L distance, period.
>
> For the record, Brian Ripley has often expressed the (minority) opinion
> that AIC is *not* appropriate for comparing non-nested models (e.g.
> <http://tolstoy.newcastle.edu.au/R/help/06/02/21794.html>;).
>
>
>>
Thank you everyone for your dedication to improving 'R' - its function to
statistical analysis and comments.
I have now 48 models (unique combinations of 1 to 6 variables) and have put
them into a list and gained the results for all models. Below is a sample of
my script & results:
m$model48 <-
-boun...@r-project.org] En
nombre de Ben Bolker
Enviado el: miércoles, 25 de enero de 2012 15:41
Para: r-h...@stat.math.ethz.ch
Asunto: Re: [R]How do I compare 47 GLM models with 1 to 5 interactions and
unique combinations?
Rubén Roa azti.es> writes:
> A more 'manual' way to do it i
Ripley has often expressed the (minority)
opinion that AIC is *not* appropriate for comparing non-nested models
(e.g. <http://tolstoy.newcastle.edu.au/R/help/06/02/21794.html>).
>
> -Original Message-----
> From: r-help-bounces r-project.org on behalf of Milan Bouchet-Va
If you are trying to destroy all aspects of statistical inference this is a
good way to go. This is also a good way to ignore the subject matter in
driving model selection.
Frank
Jhope wrote
>
> Hi R-listers,
>
> I have developed 47 GLM models with different combinations of interactions
> from
AZTI Tecnalia, Txatxarramendi Ugartea z/g,
Sukarrieta, Bizkaia, SPAIN
-Original Message-
From: r-help-boun...@r-project.org on behalf of Milan Bouchet-Valat
Sent: Wed 1/25/2012 10:32 AM
To: Jhope
Cc: r-help@r-project.org
Subject: Re: [R] How do I compare 47 GLM models with 1 to 5 interactions and
u
Le mardi 24 janvier 2012 à 20:41 -0800, Jhope a écrit :
> Hi R-listers,
>
> I have developed 47 GLM models with different combinations of interactions
> from 1 variable to 5 variables. I have manually made each model separately
> and put them into individual tables (organized by the number of vari
Hi R-listers,
I have developed 47 GLM models with different combinations of interactions
from 1 variable to 5 variables. I have manually made each model separately
and put them into individual tables (organized by the number of variables)
showing the AIC score. I want to compare all of these model
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