Hi there,

If youre not fixed on either lmer or mcmcglmm particularly, ot might be
possible using lme...
Using the weights argument you can allow an individuals variance (in your
case) to vary. Perhaps theres a way to get at the estimates it produces...


HTH

Alan

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On 18 June 2012 11:06, Luca Borger <lborger@cebc.cnrs.fr> wrote:

> Hello,
>
> I think there have been some recent papers on estimating individual
> variability in behaviour, but in any case is this useful?:
>
> library(lme4)
> fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
> str(ranef(fm1, postVar = TRUE))
> attr((ranef(fm1, postVar = TRUE))[[1]],"postVar")
>
>
> HTH
> Luca
>
>
>
>
>
>
>
>
>
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> ------------------------------**------------------------------**---------
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> Le 18/06/2012 10:29, Samantha Patrick a écrit :
>
>  Hi
>>
>> I am currently trying to estimate how consistent individuals are in a
>> trait.  I want to produce an estimate of the variability for each level of
>> a random effect (ID).  I can do this simply by calculation the variance for
>> each ID separately but I am trying to extract this information from a mixed
>> model (either in lmer or mcmcglmm).  I have trawled the mailing list but
>> can not find any answers.
>>
>> As an simplified dummy example I have 2 individuals, each with 5
>> observations of a trait.  I can calculate 2 variances, using the 5
>> observations for each individual.
>>
>> head(Data)
>> ID    trait1
>> 1        10
>> 1        15
>> 1        12
>> 1        19
>> 1        11
>> 2        9
>> 2        10
>> 2        9
>> 2        10
>> 2        10
>>
>> Variance for 1 = 4.67
>> Variance for 2 = 0.3
>>
>> Alternatively I can fit a model of:
>>
>> model1<-lmer(trait1 ~(1|ID))
>>
>> From the variance covariance matrix I can easily extract the between and
>> within group variances, but is there a way to extract individual variance
>> estimates?
>>
>> Many Thanks
>>
>> Sam
>>
>>
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