Mixed Models providing a correlation structure.
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 -- View this message in context: http://r.789695.n4.nabble.com/Mixed-Models-providing-a-correlation-structure-tp4635569.html Sent from the R help mailing list archive at Nabble.com.
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Simon Blomberg, BSc (Hons), PhD, MAppStat, AStat. Lecturer and Consultant Statistician School of Biological Sciences The University of Queensland St. Lucia Queensland 4072 Australia T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au http://www.uq.edu.au/~uqsblomb/ Policies: 1. I will NOT analyse your data for you. 2. Your deadline is your problem. Statistics is the grammar of science - Karl Pearson.