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.
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.
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, 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.