...@comcast.net
To: Paul Hermes paul.her...@analytic-company.com
Cc: r-help@r-project.org
Sent: Thursday, March 12, 2009 3:42 PM
Subject: Re: [R] stats lm() function
I think you will find that many readers of this list would rather try to
dissuade you from this misguided strategy. You
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
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
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 -
and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: ph84 masterods...@gmx.de
Date: Thursday, March 12, 2009 3:28 pm
Subject: [R] stats lm() function
To: r-help@r-project.org
Hi,
Im using the lm
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
call's in this loop tooks the most time and i
want to reduce this.
any ideas?
- Original Message -
From: David Winsemius dwinsem...@comcast.net
To: Paul Hermes paul.her...@analytic-company.com
Cc: r-help@r-project.org
Sent: Thursday, March 12, 2009 3:42 PM
Subject: Re: [R] stats lm
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