Thanks Jörg for your quick reply.
 I have tried the pvals function and the only problem is that when I add a
random effect in the model I get the following error message:
fit5<-lmer(logitprop_vig~logdensity+logbiomasstrue+loggpsize+position+(1+*
logdensity*|Idtag),na.action=na.omit, data=tabimpala)
pvals.fnc(fit5,nsim=100,ndigits=2)
Error in pvals.fnc(fit5, nsim = 100, ndigits = 2): MCMC sampling is not
implemented in recent versions of lme4 for models with random correlation
parameters

1) The thing is that I would also need to know the between-individual
variability in the slope of the explanatory variables (logdensity in the
above model). Is there something I can/should do to resolve this?

2) I have another question related to this. I have read that we need to
have flat priors for the parameters. Indeed, I am using this method to
assess the magnitude of the between-individual variability in both slope
and intercept of the explanatory variables. Is there someone who could tell
me more about what we are supposed to do to interpret the magnitude of
those parameters?

 Thank you

 Tommy




2013/7/7 Jörg Albrecht <wilsn@gmx.de>

> Hi,
>
> you can use the function "pvals.fnc" from the package languageR to obtain
> the posterior distributions of the parameters of interest.
>
> Best wishes,
>
> Jörg Albrecht
>
> Am 07/07/2013 19:16, schrieb tommy gaillard:
>
>  Dear all,
>>
>>   I am aiming to assess the between-individual variability in random slope
>> and random intercept in different context. Here is my main model:
>>   fit<-lmer(logitprop_vig~**logdensity+logbiomasstrue+**
>> loggpsize+position+(1|Idtag),**na.action=na.omit,
>> data=tabimpala)
>>
>> I would like to obtain for each of the explanatory variables the Vi
>> (variability in random intercept) and Vs (variability in random slope)
>> with
>> their CI 95%.
>>
>>   Firstly I have read in many post that the mcmcsamp function caused some
>> problems..Is it still the case or can I use it without getting any
>> biaises?
>>
>>   Secondly, I did not manage to obtain the IC95% of the parameter
>> estimates
>> in R.
>> Here is what I did:
>> fit<-mcmcsamp(fit,n=100)
>> profile(fit)
>> HPDinterval(fit)
>>   With both functions above I got: Error in UseMethod :
>> method for 'profile/HPDinterval' not applicable for an object of class
>> "merMCMC"
>>
>> This is really important to me. I hope that you help me out!
>>
>>   Many thanks!
>>
>>
>>
>
> --
> M.Sc. Jörg Albrecht
>
> Department of Ecology - Conservation Ecology
> Faculty of Biology
> Philipps-Universität Marburg
> Karl-von-Frisch-Str. 8
> 35043 Marburg
> Germany
>
> mail: joerg.albrecht@staff.uni-**marburg.de<joerg.albrecht@staff.uni-marburg.de>
> phone: +49 6421 28 25385
> fax: +49 6421 28 23387
>
>


-- 
Tommy Gaillard
Intern student
Master 2 Research
Tel: 06-71-81-94-66
Email: tommy.gaillard40@gmail.com

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