[R-sig-ME] Question about random effects

[Reinhold Kliegl]

There is another kind of power issue involved as well:  Keeping spurious
variance components in the model leads to significant loss in statistical
power.

Stroup (2012, Generalized linear mixed models: Modern concepts, methods and
applications, p. 185):
"Neither the [maximal] nor the [minimal] linear mixed models are
appropriate for most repeated measures analysis. Using the [maximal] model
is generally wasteful and costly in terms of statistical power for testing
hypotheses. On the other hand, the [minimal] model fails to account for
nontrivial correlation among repeated measurements. This results in
inflated [T]ype I error rates when non-negligible correlation does in fact
exist. We can usually find middle ground, a covariance model that
adequately accounts for correlation but is more parsimonious than the
[maximal] model. Doing so allows us full control over [T]ype I error rates
without needlessly sacrificing power."

See also:  http://arxiv.org/abs/1511.01864


On Mon, May 23, 2016 at 5:57 PM, Ben Bolker <bbolker at gmail.com> wrote:

>
>   I agree, although I'll also say that if you are faced with a power
> imbalance (reviewer/supervisor/etc. insists that it should be removed),
> in the case where the random effect variance is estimated as zero there
> is really very little (no?) *practical* difference in this case between
> keeping or removing the random effect. In particular, the estimates of
> any other variance components in the model, as well as all of the
> contents of summary() [point estimates and Wald standard errors of
> fixed-effect of coefficients] should be identical (try it and see).
>
>   cheers
>     Ben Bolker
>
>
> On 16-05-23 11:42 AM, Thierry Onkelinx wrote:
> > If the random effect reflects the design of the study then it should
> remain
> > in the model.
> >
> > ir. Thierry Onkelinx
> > Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and
> > Forest
> > team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> > Kliniekstraat 25
> > 1070 Anderlecht
> > Belgium
> >
> > To call in the statistician after the experiment is done may be no more
> > than asking him to perform a post-mortem examination: he may be able to
> say
> > what the experiment died of. ~ Sir Ronald Aylmer Fisher
> > The plural of anecdote is not data. ~ Roger Brinner
> > The combination of some data and an aching desire for an answer does not
> > ensure that a reasonable answer can be extracted from a given body of
> data.
> > ~ John Tukey
> >
> > 2016-05-23 17:13 GMT+02:00 Adriana Maldonado Chaparro <
> > maldonado.aa at gmail.com>:
> >
> >> Greetings,
> >>
> >> I want to ask for advise on the following issue:
> >> I fitted a mixed model where I'm trying to explain variation in Litter
> Sex
> >> Ratio as a function of social network position. In this model the random
> >> effect, individual identity, explained none of the variance, and one of
> the
> >> reviewers argued that I should exclude it from my model because of these
> >> reason. I think I should keep it because I have repeated measures. What
> are
> >> your thoughts on this matter?
> >>
> >> Thanks in advance,
> >>
> >> Adriana Maldonado
> >> Postdoctoral Researcher
> >>
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> >>
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