[R-sig-ME] Specifying and fitting LME model with unstructured error correlation within subject
Ben Bolker
bbolker @ending from gm@il@com
Mon Dec 3 01:17:44 CET 2018
I'd suggest using control=lmerControl(...) to override the error
(something like check.nobs.vs.nRE="ignore", but you can look it up in
the help page ...). Your residual variance and random-effects
variances will indeed be confounded, and I can't say for sure how it
will affect the Kenward-Roger [sic] degrees of freedom calculation,
but the estimates of the fixed effects and their standard errors
should still be correct.
Actually, if you want Kenward-Roger, that may be the only option I
can think of (other than switching to SAS or something ...) For
various technical reasons previously described on this list (and in
the lme4 paper), it's not possible to force the residual variance to
zero and remove the confounding (or, in fact, to any specified value).
You _can_ fix the residual variance to a very small value (but not
exactly zero) by setting a prior in blme::blmer(), or you can fit a
model without a residual variance in glmmTMB (using dispformula ~ 0),
but ... these models won't work with lmerTest to give you
degrees-of-freedom calculations, as far as I know.
On Sun, Dec 2, 2018 at 6:22 PM Clark Kogan <kogan.clark using gmail.com> wrote:
>
> Thierry,
>
> I believe this will induce a compound symmetric covariance structure rather
> than an unstructured covariance structure. I would like to allow for unique
> correlations between different subtests.
>
> Thanks,
> Clark
>
>
> On Sun, Dec 2, 2018 at 11:58 AM Thierry Onkelinx via R-sig-mixed-models <
> r-sig-mixed-models using r-project.org> wrote:
>
> > Dear Kogan,
> >
> > Add (1|id) as random effect. This will induce a correlation among the
> > observations from the same individual.
> >
> > Best regards,
> >
> > ir. Thierry Onkelinx
> > Statisticus / Statistician
> >
> > Vlaamse Overheid / Government of Flanders
> > INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> > FOREST
> > Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> > thierry.onkelinx using inbo.be
> > Havenlaan 88 bus 73, 1000 Brussel
> > www.inbo.be
> >
> >
> > ///////////////////////////////////////////////////////////////////////////////////////////
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> > what the experiment died of. ~ Sir Ronald Aylmer Fisher
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> > ensure that a reasonable answer can be extracted from a given body of data.
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> > <https://www.inbo.be>
> >
> >
> > Op vr 30 nov. 2018 om 18:20 schreef Kogan, Clark <clark.kogan using wsu.edu>:
> >
> > > I have some data where a number of individuals have taken a few different
> > > subtests and there is 1 response per individual for each subtest. I am
> > > fitting the following model using lmer:
> > >
> > > mod <- lmer(score ~ faculty + gender + subtest + gender:subtest +
> > > faculty:gender + faculty:subtest+ (subtest|id), data = score)
> > >
> > > When fitting this model, I get the error:
> > > Error: number of observations (=219) <= number of random effects (=219)
> > > for term (subtest | id); the random-effects parameters and the residual
> > > variance (or scale parameter) are probably unidentifiable
> > >
> > > The error makes sense to me - as there is only one data point for every
> > > subtest*id, and so we cannot differentiate the random effects from the
> > > residuals. What I would like to be able to do is specify that the
> > residuals
> > > have an unstructured correlation matrix within individuals to account for
> > > the fact that an individual will likely have some correlation between
> > their
> > > subtest scores.
> > >
> > > Is there a way to do this in lmer or a similar package so that I can
> > still
> > > get Kenwood Rodgers or Satterthwaite corrected tests of effects (e.g.,
> > with
> > > pbkrtest or lmerTest).
> > >
> > > Thanks,
> > > Clark
> > >
> > >
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