[R] Question about multiple regression
Gabor Grothendieck
ggrothendieck at gmail.com
Mon Sep 8 19:22:16 CEST 2008
Try:
sum(lm.fit(x, y)$residuals^2)
On Mon, Sep 8, 2008 at 12:52 PM, Dimitri Liakhovitski <ld7631 at gmail.com> wrote:
> Thank you for reminding me, Gabor. I forgot to mention: So far, I have
> run one test set of regressions using lm. It took R 270 sec. I need to
> run 1,800,000 of those, which would imply 15.4 years of computing time
> :)
>
> I have not done the same for lm.fit because I am not sure how to get
> model R squared from lm.fit.
>
> Dimitri
>
> On Mon, Sep 8, 2008 at 12:17 PM, Gabor Grothendieck
> <ggrothendieck at gmail.com> wrote:
>> I would test the speed before making such as assumption. Note that
>> lm.fit is faster than lm and if they have the same x matrix then
>> you can do many in one call by having y be a matrix.
>>
>> On Mon, Sep 8, 2008 at 12:05 PM, Dimitri Liakhovitski <ld7631 at gmail.com> wrote:
>>> Dear R-list,
>>> maybe some of you could point me in the right direction:
>>>
>>> Are you aware of any FREE Fortran or Java libraries/actual pieces of
>>> code that are VERY efficient (time-wise) in running the regular linear
>>> least-squares multiple regression?
>>> More specifically, I have to run small regression models (between 1
>>> and 15 predictors) on samples of up to N=700 but thousands and
>>> thousands of them.
>>>
>>> I am designing a simulation in R and running those regressions and R
>>> itself is way too slow. So, I am thinking of compiling the regression
>>> run itself in Fortran and Java and then calling it from R.
>>>
>>> Thank you very much for any advice!
>>>
>>> Dimitri Liakhovitski
>>> MarketTools, Inc.
>>> Dimitri.Liakhovitski at markettools.com
>>>
>>> ______________________________________________
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>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>
>
>
> --
> Dimitri Liakhovitski
> MarketTools, Inc.
> Dimitri.Liakhovitski at markettools.com
>
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