[R] Mixed Models providing a correlation structure.

Simon Blomberg s.blomberg1 at uq.edu.au
Fri Jul 6 04:57:54 CEST 2012

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.



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
> --
> View this message in context: http://r.789695.n4.nabble.com/Mixed-Models-providing-a-correlation-structure-tp4635569.html
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Simon Blomberg, BSc (Hons), PhD, MAppStat, AStat.
Lecturer and Consultant Statistician
School of Biological Sciences
The University of Queensland
St. Lucia Queensland 4072
T: +61 7 3365 2506
email: S.Blomberg1_at_uq.edu.au

1.  I will NOT analyse your data for you.
2.  Your deadline is your problem.

Statistics is the grammar of science - Karl Pearson.

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