Thank you for your recommendation. The only thing is I don't see what
interval I am supposed to calculate. Could you give me more details?

This is the alternative method that I am planning to do, if you could tell
me what you think.

1) Testing behavioral consistency: comparison individual intercepts
 mod1=lm(response var~x)
 mod2=lm(response var~x,random=~1|Individu)

2) Testing behavioral plasticity: comparison individual slope
mod1=lmer(response var ~ x+(x|Individu))
mod2=lmer(response var ~ x + (1|Individu))

Then compare the AIC and the weights between the  2 models. Usually the
same approach is used but the results is only based on the pvalue. Here, I
believe that the AIC could tell us more about the uncertainty of the
model...

 Could you tell me what you think about this approach?

 Thanks again



2013/7/3 Douglas Bates <bates@stat.wisc.edu>

> On Wed, Jul 3, 2013 at 9:23 AM, Robert A LaBudde <ral@lcfltd.com> wrote:
>
>> The obvious method would appear to be standard errors computed from
>> bootstrap or crossvalidation samples.
>>
>
> If you are going to use bootstrap or crossvalidation samples, why not
> calculate the interval directly from the sample?  If you use the sample to
> calculate a standard error then use the standard error to create an
> interval you are throwing away information that you might have on possible
> asymmetry of the distribution.
>
>


-- 
*Tommy Gaillard
Etudiant stagiaire CNRS*
Université de Dijon
Master 2 Recherche
Tel: 06-71-81-94-66
Email: tommy.gaillard40@gmail.com

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