Hi,
First thanks to everyone for your help. In summary:
- Using either the !REPEAT command or a separate pedigree file avoided
the error when reading the pedigree.
- the block.ide(age) didn't work, but using another factor coding for
the ages worked fine.
- Arthur suggested several steps for modelling random regressions. As I
understood Arthur's code, the within individual errors were assumed to
be independent, i.e. with a diagonal error matrix (in this case a US
with zero off diagonals), and unstructured covariance matrix for both
the tree.pol(age,2) effect and the block.age effect.
Block was not significant, so I took it out from the model. After
several attempts the program converged, but the variance components for
the error matrix were not significant, while all the terms of the US
matrix for the polynomial were significant. Because sometimes Eucalypts
data sets give funny results I tried with a different data set. This was
a study of the behaviour of stem diameter and wood density with age,
comprising 16 years (equally spaced) of measurements. This is just one
family with 188 trees.
I tried just:
1- diam~mu age !r tree
2- diam~mu age !r tree !f mv
1 2 !STEP .1
188
16 0 DIAG 195 200 ...
3- diam~ pol(age,2) !r tree.pol(age,2) !f mv
1 2 !STEP .1
188
16 0 DIAG 195 200 ...
tree.pol(age,2) 2
tree 0
pol(age,2) 0 US ...
And again, the error components were non significant while the
polynomial was highly significant. So I think that maybe the assumption
about the error is wrong. Maybe it's worth to try with an AR(1)
structure or alternative structures that reflect the autocorrelation of
the measures within individuals.
If you think of a different option just send me a line. Thanks again,
Luis
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Luis Apiolaza
Institute of Veterinary, Animal and Biomedical Sciences
Massey University
Palmerston North
New Zealand
L.A.Apiolaza@massey.ac.nz
"Quote me as saying I was misquoted " - Groucho Marx
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