[R-sig-ME] How to test significance of random effects (intercept and slope) biologically interpretable

[Robert A LaBudde]

The obvious method would appear to be standard 
errors computed from bootstrap or crossvalidation samples.

What's the issue with this?

At 04:33 AM 7/3/2013, tommy gaillard wrote:
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>Thanks for your reply David.
>In my area, animal behavior ecology,  the comparison of random slope
>between studies is very advantageous. It has been underestimate so far but
>many authors have pointed out its importance. This is why LRT has limits
>because it only tells you whether a random effect is statistical
>significant or not. It might be enough in your area but in my field I need
>to give a biological meaning of my statistical results.
>   There are a couple of alternative to LRT:
>1) Iterative approach: based on the AIC score
>2) Pseudo-bayesian approach: mcmcsamp
>3) Bayesian approach: based on DIC score
>
>  I am more familiar with the AIC approach and was tempted to use this one.
>I know there are some drawbacks though, this is why I would like to discuss
>with you those different methods and to choose the more appropriate one to
>give a biological meaning of my results.
>
>  Thanks,
>
>  Tommy
>
>
>
>
>
>
>
>
>2013/7/3 David Duffy <David.Duffy at qimr.edu.au>
>
> > On Tue, 2 Jul 2013, tommy gaillard wrote:
> >
> >  I am aiming to assess the inter-individual variability of both random
> >> intercept and slope in response to multiple changing variables.
> >> In order to so, several studies have compared models two by two by
> >> changing
> >> their structure. For example, to know whether there is a difference in the
> >> plasticity of the responses between individuals, they compare a model with
> >> both the interest variable*Identity individual as random effect and a
> >> model
> >> with only "Identity individual" ad random effect. They then realize a
> >> loglikelihood test and base their results only on the pvalues.
> >>
> >> I am looking for an alternative as I have been strongly recommended to
> >> base my results on effect size (and 95% IC) rather than on pvalues. This
> >> has indeed several advantages as it gives the biological magnitude of an
> >> effect, its uncertainty and it is comparable between studies.
> >>
> >
> > Hopefully someone else will chime in, but I don't know if I would consider
> > an estimate of random slope effect as necessarily comparable between
> > *studies* - that will be really depend on the area.  If the dataset is not
> > too large, I'd probably find a graphical presentation of the fitted
> > regression line for each individual more biologically meaningful. Also, a
> > plot of the distribution of the individual slopes ("raw", or predicted from
> > your mixed model), as this may not be a single Gaussian.
> >
> > My simple minded way of thinking is "can we summarize these data using a
> > model without interactions?", do a LRT and try and work out its
> > distribution under the null (a hard problem!), and if interaction is
> > nonignorable, then present what's going on as complicated.
> >
> > Just 2c.
> >
> > | David Duffy (MBBS PhD)                                         ,-_|\
> > | email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
> > | Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
> > | 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v
> >
>
>
>
>--
>*Tommy Gaillard
>Etudiant stagiaire CNRS*
>Université de Dijon
>Master 2 Recherche
>Tel: 06-71-81-94-66
>Email: tommy.gaillard40 at gmail.com
>
>         [[alternative HTML version deleted]]
>
>
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Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral at lcfltd.com
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