call's in this loop tooks the most time and i
> want to reduce this.
> any ideas?
>
> - Original Message -----
> From: "David Winsemius"
> To: "Paul Hermes"
> Cc:
> Sent: Thursday, March 12, 2009 3:42 PM
> Subject: Re: [R] stats lm() func
fugured that the lm call's in this loop tooks the most time and i
want to reduce this.
any ideas?
- Original Message -
From: "David Winsemius"
To: "Paul Hermes"
Cc:
Sent: Thursday, March 12, 2009 3:42 PM
Subject: Re: [R] stats lm() function
I think you will fin
I think you will find that many readers of this list would rather try
to dissuade you from this misguided strategy. You are unlikely to get
to a sensible solution in using step-down procedures with this sort of
situation (large number of predictors with modest size of data).
--
David Winsem
Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: ph84
Date: Thursday, March 12, 2009 3:28 pm
Subject: [R] stats lm() function
To: r-help@r-project.org
> Hi,
>
> Im using the l
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(
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 mi
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
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