Thanks to Professor Bates for his information about how to calculate least square estimates [not using solve] in ``the right way''.
This is very useful indead, I am clearly one of of the package maintainers who is not using using solve in a proper way at the moment.
However, the calculations in my code look more like GLS than LS.
## GLS could in principlpe be implemented like this : betahat <- solve(t(X) %*% solve(Omega)%*% X) %*% t(X)%*%solve(Omega)%*% y ## where Omega is a strictly p.d. symmetric matrix
Does someone have a recommendation on how to do this in ``the right way'' ?
My first attempt (trying to imitate the LS solution recommended by Prof. Bates) is :
temp <- backsolve(chol(Omega),cbind(X,y)) betahat <- qr.coef(qr(temp[,1:ncol(X)]), temp[,ncol(X)+1])
Thank you in advance for any help
Cheers Ole
-- Ole F. Christensen Center for Bioinformatik Datalogisk Institut Aarhus Universitet Ny Munkegade, Bygning 540 8000 Aarhus C Denmark
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