Why did you use the 'lower.tri' syntax?
Does this work for you?
lme(Y~Random, data = DATA,
random = list(Random = pdSymm(CovM,~Random)))
Kevin
On Wed, Jul 11, 2012 at 9:27 AM, Marcio wrote:
> Dear Simon,
> Thanks for the quick reply.
> Unfortunately I don't have access to Pinheiro and Bates. I
Dear Simon,
Thanks for the quick reply.
Unfortunately I don't have access to Pinheiro and Bates. I tried googling
the pdSymm and lme but I still cannot get the syntax right.
In my model, I only have 1 random factor with repetitions (groups) (e.g. 2
records per each level)
I am pasting bellow a ver
Aah. From your model description, you are more interested in the
covariance structure of the random effects, rather than the residuals.
You will then need to use the pdSymm class in the specification of the
random effects. See Pinheiro and Bates pp 157-166.
Cheers,
Simon.
On 06/07/12 11:43,
You need to look at the corSymm correlation class for nlme models.
Essentially, in your lme call, you need to do
correlation=corSymm(mat[lower.tri(mat)], fixed=TRUE)
Where mat is your (symmetric) variance-covariance matrix. Remember to
make sure that the rows and columns of mat are in the sam
Hi folks,
I was wondering how to run a mixed models approach to analyze a linear
regression with a user-defined covariance structure.
I have my model
y = xa +zb +e and
b ~ N (0, C*sigma_square). (and a is a fixed effects)
I would like to provide R the C (variance-covariance) matrix
I can easi
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