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 same order as in your data frame.

Cheers,

Simon.

On 06/07/12 11:43, Marcio wrote:
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 easily provide an example, but at this point I am first trying to know
what is the best package the allows an unstructured covariance matrix.

I was trying the function lme in the package nlme but I didn't have success
in the defining the option "correlation"

Thanks


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Simon Blomberg, BSc (Hons), PhD, MAppStat, AStat.
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