[R-sig-ME] Testing Significance of Random Effects

[Ben Bolker]

   I wouldn't generally recommend removing random effects on the basis 
of null-hypothesis significance testing ... but others on this list 
might, e.g. Matuschek et al. https://arxiv.org/pdf/1511.01864.pdf 
(section 2.4) suggest backward stepwise removal with an alpha-level of 0.2.

   Also see 
https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#testing-significance-of-random-effects


On 3/12/21 9:43 AM, Marco Zanin wrote:
> To whom it may concern,
> 
> My name is Marco Zanin, I am a PhD candidate in Sport & Exercise Physiology at Leeds Beckett University (Leeds, UK) and I work as Sport Scientist at Bath Rugby (Bath, UK).
> 
> I am relatively new to lme4, but I was wondering whether you might help me to find a way to test the significance of the random effects in a model, whether the random effects improve/reduce the fit of the model and are thus necessary or if they could be removed from the model.
> 
> I look forward to hearing from you.
> 
> Kind regards,
> 
> Marco Zanin
> 
> ______________________________________________________________________
> This email has been scanned by the Symantec Email Security.cloud service.
> For more information please visit http://www.symanteccloud.com
> ______________________________________________________________________
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>