[R-sig-ME] Lmer and variance-covariance matrix

[Jarrod Hadfield]

Hi,

In addition, each trait is only measured once for each id (correct?)  
which means that the likelihood could not be optimised even if the  
data-set was massive. If you could fix the residual variance to some  
value (preferably zero) then the problem has a unique solution given  
enough data, but I'm not sure this can be done in lmer? . Since the  
structure of the residuals is at the moment quite inflexible you  
probably can't use lmer to fit multi-response models, unless the  
responses are non-Gaussian and non-binary.

Cheers,

Jarrod


On 11 Mar 2011, at 13:18, Douglas Bates wrote:

> On Thu, Mar 3, 2011 at 7:03 AM, Antoine Paccard
> <antoine.paccard at unine.ch> wrote:
>> Dear modelers,
>> I have been trying in the past few months to obtain a variance-
>> covariance matrix using lmer. I failed multiple times until I decided
>> to do it under SAS. Now, I am going back on it and would like to run
>> it with R.
>> I measured 15 different traits and my data is organized this way:
>> fam     id     trait     score
>> 57     1     1     0.047207645
>> 57     2     1     1.420790311
>> 57     3     1     -0.290782077
>> 57     1     2     -0.585655473
>> 57     2     2     -0.986483343
>> 57     3     2     0.290187057
>> 57     1     3     0.741162271
>> 57     2     3     1.59448736
>> 57     3     3     .
>> .     .     .     .
>> .     .     .     .
>> .     .     .     .
>> 57     1     15     .
>> 57     2     15     .
>> 57     3     15     .
>> 58     1     1     .
>> 58     2     1     .
>> 58     3     1     .
>> .     .     .     .
>> .     .     .     .
>> .     .     .     .
>> 58     1     15     .
>> 58     2     15
>> 58     3     15
>> .
>> .
>> .
>> 100     1     15
>> 100     2     15
>> 100     3     15
>> with “fam” being the family, “id” the individual, “trait”
>> the traits measured and “score” the result of each measures.
>> So far I have been trying to fit this model:
>> d <- data
>> d$fam1 <- factor(d$fam)
>> d$id1 <- factor(d$id)
>> d$trait1 <- factor(d$trait)
>> w.family <- lmer(score ~ (trait1 | fam1/id1 ), data=d)
>
>> But I was getting some “stack overflow” error messages so I ran  
>> the
>> model this way:
>> w.family <- lmer(score ~ (trait1 | fam1/id1 ), data=d,control = list
>> (maxIter = 500))
>
> If there are 15 levels of trait you are trying to estimate 240
> variance-covariance parameters (120 for fam1 and 120 for fam1:id1).
> That is a very large optimization problem,  I'm not surprised that
> there is difficulty in finding the optimum.
>> Although it still didn’t work and I am now wondering what is wrong  
>> in
>> this model. The reason why I have put “trait1” in the random  
>> effect
>> is because it was the only way for me to obtain a variance-covariance
>> matrix on all my traits.
>> I am currently trying to put “trait1” also as fixed effect:
>> w.family <- lmer(score ~ trait1 + (trait1 | fam1/id1 ),  
>> data=d,control
>> = list (maxIter = 500))
>> I would be curious to know what you think about such a model and why
>> it actually doesn’t work.
>> Moreover, I am trying to write done the equation for the model:
>> w.family <- lmer(score ~ (trait1 | fam1/id1 ), data=d)
>> but can’t figure it out. I know I should get something as:
>> Scoreij = µ + fam(i) + Ij(i) + ε
>> But I am not sure if that’s right.
>> What do you think? Thanks a lot for your help,
>> Best,
>> Antoine Paccard
>> -----------
>> Antoine Paccard,
>> Laboratory of Evolutionary Botany,
>> Institut of biology, Faculté des Sciences,
>> University of Neuchâtel, Unimail,
>> Rue Émile-Argand 11,
>> 2000, Neuchâtel Switzerland
>> Office: (0041) (0)32 718 23 49
>> Fax: (0041) (0)32 718 30 01
>> www.unine.ch/members/antoine.paccard/
>> http://www2.unine.ch/evobot
>>
>>
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>>
>>
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