> do I choleski decompose > the inverse of the covariance matrix and weight the observations - > risking precision loss. - I think you'd be better off choleski decomposing the cov matrix itself wouldn't you? e.g. if V is the covariance matrix use chol() to get V=L^T L and then form L^{-T}y and L^{-T}X using solve() (assuming model is y=Xb+e). Simon _____________________________________________________________________ > Simon Wood [EMAIL PROTECTED] www.stats.gla.ac.uk/~simon/ >> Department of Statistics, University of Glasgow, Glasgow, G12 8QQ >>> Direct telephone: (0)141 330 4530 Fax: (0)141 330 4814
______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help