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))

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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