Ted, LAPACK is newer and is supposed to contain better algorithms than LINPACK. It is not true that LAPACK cannot handle rank-deficient problems. It can.
However, I do not know if using LAPACK in glm.fit instead of LINPACK would be faster and/or more memory efficient. Ravi. ____________________________________________________________________ Ravi Varadhan, Ph.D. Assistant Professor, Division of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins University Ph. (410) 502-2619 email: rvarad...@jhmi.edu ----- Original Message ----- From: Ted <tchi...@sickkids.ca> Date: Thursday, October 22, 2009 10:53 am Subject: Re: [R] glm.fit to use LAPACK instead of LINPACK To: "r-help@R-project.org" <r-help@r-project.org> > Hi, > > I understand that the glm.fit calls LINPACK fortran routines instead of > LAPACK because it can handle the 'rank deficiency problem'. If my data > matrix is not rank deficient, would a glm.fit function which runs on > LAPACK be faster? Would this be worthwhile to convert glm.fit to use > LAPACK? Has anyone done this already?? What is the best way to do this? > > I'm looking at very large datasets (thousands of glm calls), and would > like to know if it's worth the effort for performance issues. > > Thanks, > > Ted > > ------------------------------------- > Ted Chiang > Bioinformatics Analyst > Centre for Computational Biology > Hospital for Sick Children, Toronto > 416.813.7028 > tchi...@sickkids.ca > > ______________________________________________ > R-help@r-project.org mailing list > > PLEASE do read the posting guide > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@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.
