yes, indeed, you can certainly speed things up, by just changing the
design matrix X and feeding it back to lm.fit().
In addition, if you just need the least squares estimates, then you gain
a bit more by using constructs of the form:
XtX <- crossprod(X)
Xty <- crossprod(X, y)
betas <- solve(XtX, Xty)
I hope it helps.
Best,
Dimitris
Paul Hermes wrote:
Hi,
Im using the lm() function where the formula is quite big (300 arguments) and the data is a frame of 3000 values.
This is running in a loop where in each step the formula is reduced by one argument, and the lm command is called again (to check which arguments are useful) .
This takes 1-2 minutes.
Is there a way to speed this up?
i checked the code of the lm function and its seems that its preparing the data and then calls lm.Fit(). i thought about just doing this praparing stuff first and only call lm.fit() 300 times.
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Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
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