Different methods of performing least squares calculations in R are discussed in
@Article{Rnews:Bates:2004,
author = {Douglas Bates},
title= {Least Squares Calculations in {R}},
journal = {R News},
year = 2004,
volume = 4,
number = 1,
pages
Note that using solve can be numerically unstable for certain problems.
On Fri, Feb 20, 2009 at 6:50 AM, Kenn Konstabel wrote:
> Decyphering formulas seems to be the most time consuming part of lm:
>
> mylm1 <- function(formula, data) {
> # not perfect but works
> F <- model.frame(formula
Decyphering formulas seems to be the most time consuming part of lm:
mylm1 <- function(formula, data) {
# not perfect but works
F <- model.frame(formula,data)
y <- model.response(F)
mt <- attr(F, "terms")
x <- model.matrix(mt,F)
coefs <- solve(crossprod(x), crossprod(x,y))
On Thu, Feb 19, 2009 at 8:30 AM, Esmail Bonakdarian wrote:
> Hi Kenn,
>
> Thanks for the suggestions, I'll have to see if I can figure out how to
> convert the relatively simple call to lm with an equation and the data file
> to the functions you mention (or if that's even feasible).
X <- model.m
Hi Kenn,
Thanks for the suggestions, I'll have to see if I can figure out how to
convert the relatively simple call to lm with an equation and the data file
to the functions you mention (or if that's even feasible).
Not an expert in statistics myself, I am mostly concentrating on the
programming
Doran, Harold wrote:
lm(y ~ x-1)
solve(crossprod(x), t(x))%*%y# probably this can be done more
efficiently
You could do
crossprod(x,y) instead of t(x))%*%y
that certainly looks more readable (and less error prone) to an R newbie
like myself :-)
Gabor Grothendieck wrote:
On Wed, Feb 18, 2009 at 7:27 AM, Esmail Bonakdarian wrote:
Gabor Grothendieck wrote:
See ?Rprof for profiling your R code.
If lm is the culprit, rewriting your lm calls using lm.fit might help.
Yes, based on my informal benchmarking, lm is the main "bottleneck", th
> lm(y ~ x-1)
> solve(crossprod(x), t(x))%*%y# probably this can be done more
> efficiently
You could do
crossprod(x,y) instead of t(x))%*%y
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R-help@r-project.org mailing list
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PLEASE do read the posti
lm does lots of computations, some of which you may never need. If speed
really matters, you might want to compute only those things you will really
use. If you only need coefficients, then using %*%, solve and crossprod will
be remarkably faster than lm
# repeating someone else's example
# lm(DAX
On Wed, Feb 18, 2009 at 7:27 AM, Esmail Bonakdarian wrote:
> Gabor Grothendieck wrote:
>>
>>
>> See ?Rprof for profiling your R code.
>>
>> If lm is the culprit, rewriting your lm calls using lm.fit might help.
>
> Yes, based on my informal benchmarking, lm is the main "bottleneck", the
> rest
> o
Barry Rowlingson wrote:
- and the bulk of the time in the regression calls will be taken up
by C code in the underlying linear algebra libraries (lapack, blas,
atlas and friends).
ah, good point.
Your best bet for optimisation in this case would be making sure you
have the best libraries
Gabor Grothendieck wrote:
See ?Rprof for profiling your R code.
If lm is the culprit, rewriting your lm calls using lm.fit might help.
Yes, based on my informal benchmarking, lm is the main "bottleneck", the rest
of the code consists mostly of vector manipulations and control structures.
I
2009/2/17 Esmail Bonakdarian :
> Well, I have a program written in R which already takes quite a while
> to run. I was
> just wondering if I were to rewrite most of the logic in Python - the
> main thing I use
> in R are its regression facilities - if it would speed things up. I
> suspect not sinc
On Tue, Feb 17, 2009 at 6:59 PM, Esmail Bonakdarian wrote:
> Well, I have a program written in R which already takes quite a while
> to run. I was
> just wondering if I were to rewrite most of the logic in Python - the
> main thing I use
> in R are its regression facilities - if it would speed thi
On Tue, Feb 17, 2009 at 6:05 PM, Barry Rowlingson
wrote:
> 2009/2/17 Esmail Bonakdarian :
> When I need to use the two together, it's easiest with 'rpy'. This
> lets you call R functions from python, so you can do:
>
> from rpy import r
> r.hist(z)
wow .. that is pretty straight forward, I'll
Hello!
On Tue, Feb 17, 2009 at 5:58 PM, Warren Young wrote:
>
> Esmail Bonakdarian wrote:
>>
>> I am just wondering if any of you are doing most of your scripting
>> with Python instead of R's programming language and then calling
>> the relevant R functions as needed?
>
> No, but if I wanted to
2009/2/17 Esmail Bonakdarian :
> Hello all,
>
> I am just wondering if any of you are doing most of your scripting
> with Python instead of R's programming language and then calling
> the relevant R functions as needed?
I tend to use R in its native form for data analysis and modelling,
and pytho
Esmail Bonakdarian wrote:
I am just wondering if any of you are doing most of your scripting
with Python instead of R's programming language and then calling
the relevant R functions as needed?
No, but if I wanted to do such a thing, I'd look at Sage:
http://sagemath.org/
It'll give you acc
Hello all,
I am just wondering if any of you are doing most of your scripting
with Python instead of R's programming language and then calling
the relevant R functions as needed?
And if so, what is your experience with this and what sort of
software/library do you use in combination with Python
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