[R-sig-ME] Confidence interval around random effect variances in place of p-value
Jack Solomon
kj@j@o|omon @end|ng |rom gm@||@com
Sat Apr 3 02:22:08 CEST 2021
Well, how about concluding so:
If a (say 2-level) model gives a singular fit (even though perhaps there is
a "tol" that is small but not exactly "0" for that warning to show up),
that would mean we have a "practically" non-significant random-effect
variance component.
On Fri, Apr 2, 2021 at 7:15 PM Ben Bolker <bbolker using gmail.com> wrote:
> I'm not sure that the bootstrapped CIs *wouldn't* work; they might
> return the correct proportion of singular fits ...
>
> On 4/2/21 8:12 PM, Jack Solomon wrote:
> > Thank you all very much. So, I can conclude that a likelihood ratio test
> > and/or a parametric bootstrapping can be used for random effect variance
> > component hypothesis testing.
> >
> > But I also concluded that the idea of simply using a bootstrapped CI for
> > a random-effect variance component [e.g., in lme4;
> > confint(model,method="boot",oldNames=FALSE) ] by definition can't be
> > used for significance testing, because it requires the possibility of
> > seeing sd = 0 which can't be "strictly" captured by such a CI from a
> > multilevel model (at least not easily so).
> >
> > I hope my conclusions are correct,
> > Thank you all, Jack
> >
> > On Fri, Apr 2, 2021 at 6:51 PM Ben Bolker <bbolker using gmail.com
> > <mailto:bbolker using gmail.com>> wrote:
> >
> > 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
> > <
> 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>
> > > <mailto: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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