Message-ID: <61A81C2B-7347-4953-8500-982AE370B43F@yahoo.fr>
Date: 2019-07-20T06:19:55Z
From: varin sacha
Subject: mgcv gam/bam model selection with random effects and AR terms
In-Reply-To: <f5f20123-6be1-b1e2-efb3-46d9388ecfcd@gmail.com>
Hi,
According to Kneib & Greven (biometrika 2010)
? the corrected version of the conditional AIC was developed exactly with the goal of allowing for sensible model selection in mixed models. For the marginal AIC we did not find a proper correction, so we would in general not recommend this in its current form. ?
? We have recently developed an R package called cAIC4 (https://cran.r-project.org/web/packages/cAIC4/index.html) that should be a good starting point (also beyond Gaussian mixed effects models). ?
Best
Sacha Varin
Envoy? de mon iPhone
> Le 18 juil. 2019 ? 17:07, Ben Bolker <bbolker at gmail.com> a ?crit :
>
>
> I'm not sure of the answer, but in general I'd say if you're
> interested in out-of-sample predictive accuracy, you should try to find
> something analogous to AIC. R^2/deviance only tell you how well your
> model fits to a specific set of data ...
>
>> On 2019-07-17 9:46 a.m., Gi-Mick Wu wrote:
>> Dear Mathew,
>>
>> I was looking for information on model selection for a bam model with an autocorrelation structure and essentially only found your unanswered post (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2017q2/025566.html).
>>
>> May I ask if you found any solution for this?
>>
>> Best,
>> Mick
>>
>>
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>>
>> _______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
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