Hello All, Looking for an easy way to feed a non-identity covariance matrix to a regression. Is there a function to do this, or do I choleski decompose the inverse of the covariance matrix and weight the observations - risking precision loss. Thanks, John. -- -------------------------------------------------------------------------- Dr. John Janmaat Department of Economics, Acadia University, Wolfville, NS, B4P 2R6 E-mail: jjanmaat at acadiau.ca Web: http://ace.acadiau.ca/~jjanmaat Tel: 902-585-1461 Fax: 902-585-1070
> 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 simon at stats.gla.ac.uk 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