[R-sig-ME] Confidence interval around random effect variances in place of p-value
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Sat Apr 3 01:51:56 CEST 2021
Sure. If all you want is p-values, I'd recommend parametric
bootstrapping (implemented in the pbkrtest package) ... that will avoid
these difficulties. (I would also make sure that you know *why* you
want p-values on the random effects ... they have all of the issues of
regular p-values plus some extras:
http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#testing-significance-of-random-effects
)
On 4/2/21 7:37 PM, Jack Solomon wrote:
> Thanks. Just to make sure, to declare a statistically NON-significant
> random effect variance component, the lower bound of the CI must be
> EXACTLY "0", right?
>
> Tha is, for example, a CI like: [.0002, .14] is a
> statistically significant random-effect variance component but one that
> perhaps borders a p-value of relatively close to but smaller than .05,
> right?
>
> On Fri, Apr 2, 2021 at 6:19 PM Ben Bolker <bbolker using gmail.com
> <mailto:bbolker using gmail.com>> wrote:
>
> This seems like a potential can of worms (as indeed are all
> hypothesis tests of null values on a boundary ...) However, in this
> case
> bootstrapping (provided you have resampled appropriately - you may need
> to do hierarchical bootstrapping ...) seems reasonable, because a null
> model would give you singular fits (i.e. estimated sd=0) half of the
> time ...
>
> Happy to hear more informed opinions.
>
> On 4/2/21 6:55 PM, Jack Solomon wrote:
> > Dear All,
> >
> > A colleague of mine suggested that I use the bootstrapped CIs
> around my
> > model's random effect variances in place of p-values for them.
> >
> > But random effect variances (or sds) start from "0". So, to declare a
> > statistically NON-significant random effect variance component, the
> > lower bound of the CI must be EXACTLY "0", right?
> >
> > Thank you very much,
> > Jack
> >
> > [[alternative HTML version deleted]]
> >
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