[R-sig-ME] mgcv gam/bam model selection with random effects and AR terms

[Noelle G. Beckman]

I am currently out of the office until July 5th. I will respond to your email upon my return.

On Jul 20, 2019, at 12:19 AM, varin sacha via R-sig-mixed-models <r-sig-mixed-models using r-project.org> wrote:

> 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
> 
> Envoyé de mon iPhone
> 
> Le 18 juil. 2019 à 17:07, Ben Bolker <bbolker using gmail.com> a écrit :
> 
> 
>  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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