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
mgcv gam/bam model selection with random effects and AR terms
4 messages · Gi-Mick Wu, Ben Bolker, varin sacha +1 more
1 day later
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 [[alternative HTML version deleted]]
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1 day later
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 [[alternative HTML version deleted]]
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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 at 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 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 ??[[alternative HTML version deleted]]
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