Dear All,
Is there is a fast way of refitting lm() when the design matrix stays constant but the response is different? For example,
y1 ~ X y2 ~ X y3 ~ X ...etc.
where y1 is the 1st instance of the response vector. Calling lm() every time seems rather wasteful since the QR-decomposition of X needs to be calculated only once. It would be nice if qr() was called only once and then the same QR-factorization used in all subsequent fits. However, I can't see a way to do this easily. Can anybody else?
Why do I want to do this? I'm fitting ~1000 different X's to a response vector (for biologists: 1000 genetic markers to a measured phenotype with 2000 cases) and wish to establish global significance thresholds for multiple testing. The fits have a complex dependency structure that makes the Bonferroni correction inappropriate. So I intend to refit all ~1000 X's with a shuffled response many times. However, this runs too slow for my needs.
Of course, not having to redo QR will only help if QR is a rate limiting step in lm(), so if anybody can tell me it's not, then that would be very helpful too. I would also like to do this for glm() and lmer() fits. Ideally.
Many thanks,
William
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Dr William Valdar ++44 (0)1865 287 717 Wellcome Trust Centre [EMAIL PROTECTED] for Human Genetics, Oxford www.well.ox.ac.uk/~valdar
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