[R-sig-ME] Statistical significance of random-effects (lme4 or others)

Ben Bolker bbo|ker @end|ng |rom gm@||@com
Mon Sep 7 17:58:18 CEST 2020

     Also see RLRsim, pbkrtest.

   lmerTest::ranova() is more convenient (and sounds like what you're 
looking for), but RLRsim and pbkrtest are going to be more accurate for 
individual comparisons.

On 9/7/20 2:13 AM, Daniel Lüdecke wrote:
> Hi Simon,
> I'm not sure if this is a useful question. The variance can / should never
> be negative, and it usually is always above 0 if you have some variation in
> your outcome depending on the group factors (random effects).
> Packages I know that do some "significance testing" or uncertainty
> estimation of random effects are lmerTest::ranova() (quite well documented
> what it does) or "arm::se.ranef()" resp. "parameters::standard_error(effects
> = "random")". The two latter packages compute standard errors for the
> conditional modes of the random effects (what you get with "ranef()").
> Best
> Daniel
> -----Ursprüngliche Nachricht-----
> Von: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> Im
> Auftrag von Simon Harmel
> Gesendet: Montag, 7. September 2020 06:28
> An: Juho Kristian Ruohonen <juho.kristian.ruohonen using gmail.com>
> Cc: r-sig-mixed-models <r-sig-mixed-models using r-project.org>
> Betreff: Re: [R-sig-ME] Statistical significance of random-effects (lme4 or
> others)
> Dear J,
> My goal is not to do any comparison between any models. Rather, for each
> model I want to know if the variance component is different from 0 or not.
> And what is a p-value for that.
> On Sun, Sep 6, 2020 at 11:21 PM Juho Kristian Ruohonen <
> juho.kristian.ruohonen using gmail.com> wrote:
>> A non-statistician's two cents:
>>     1. I'm not sure likelihood-ratio tests (LRTs) are valid at all for
>>     models fit using REML (rather than MLE). The anova() function seems to
>>     agree, given that its present version (4.0.2) refits the models using
>>     in order to compare their deviances.
>>     2. Even when the models have been fit using MLE, likelihood-ratio
>>     tests for variance components are only applicable in cases of a single
>>     variance component. In your case, this means a LRT can only be used for
> *m1
>>     vs ols1* and *m2 vs ols2*. There, you simply divide the p-value
>>     reported by *anova(m1, ols1) *and *anova(m2, ols2)* by two. Both are
>>     obviously extremely statistically significant. However, models *m3 *and
>>     *m4* both have two random effects. The last time I checked, the
>>     default assumption of a chi-squared deviance is no longer applicable in
>>     such cases, so the p-values reported by Stata and SPSS are only
> approximate
>>     and tend to be too conservative. Perhaps you might apply an information
>>     criterion instead, such as the AIC
> <https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#can-i-use-aic-for-m
> ixed-models-how-do-i-count-the-number-of-degrees-of-freedom-for-a-random-eff
> ect>
>>     .
>> Best,
>> J
> --
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