[R] stats lm() function

[Dimitris Rizopoulos]

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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