[R-sig-ME] Statistical significance of random-effects (lme4 or others)
Juho Kristian Ruohonen
juho@kr|@t|@n@ruohonen @end|ng |rom gm@||@com
Mon Sep 7 08:29:12 CEST 2020
While I agree with Daniel that an assumption of zero random-effect variance
doesn't make much sense, I would point out that in my understanding, a
simple LRT comparing a model with and without the random effect, where
applicable, tests exactly that. The p-value divided by 2 reflects the
probability of a deviance difference equal to or greater than the one
observed between the models, under the null hypothesis that the variance
component equals zero. A very low p/2 means a random-effect variance of 0
is very implausible.
ma 7. syysk. 2020 klo 9.13 Daniel Lüdecke (d.luedecke using uke.de) kirjoitti:
> 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.
> = "random")". The two latter packages compute standard errors for the
> conditional modes of the random effects (what you get with "ranef()").
> -----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
> 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
> > 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
> > *m4* both have two random effects. The last time I checked, the
> > default assumption of a chi-squared deviance is no longer applicable
> > such cases, so the p-values reported by Stata and SPSS are only
> > and tend to be too conservative. Perhaps you might apply an
> > criterion instead, such as the AIC
> > .
> > Best,
> > J
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