[R-sig-ME] mgcv gam/bam model selection with random effects and AR terms
v@r|n@@ch@ @end|ng |rom y@hoo@|r
Sat Jul 20 08:19:55 CEST 2019
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). »
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?
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