On 27-05-2013, at 21:57, ivo welch wrote:
>
> Gentlemans as 274 algorithm allows weights, so adding an obs with a weight of
> -1 would do the trick of removing obs, too.
>
> This may be a good job for hadwell wickhams c code interface.
Searching for "Gentlemans as 274 algorithm" with google
Gentlemans as 274 algorithm allows weights, so adding an obs with a weight
of -1 would do the trick of removing obs, too.
This may be a good job for hadwell wickhams c code interface.
On May 27, 2013 12:47 PM, "Berend Hasselman" wrote:
>
> On 27-05-2013, at 17:12, ivo welch wrote:
>
> > dear R
On 27-05-2013, at 17:12, ivo welch wrote:
> dear R experts---I would like to update OLS regressions with new
> observations on the front of the data, and delete some old
> observations from the rear. my goal is to have a "flexible"
> moving-window regression, with a minimum number of observatio
The essential trick here is the Sherman-Morrison-Woodbury formula.
My quantreg package has a lm.fit.recursive function that implements
a fortran version for adding observations, but like biglm I don't remove
observations at the other end either.
Roger Koenker
rkoen...@illinois.edu
On May 27,
Look at the biglm package. It does 2 of the 3 things that you asked for:
Construct an initial lm fit and add a new block of data to update that
fit. It does not remove data, but you may be able to look at the code and
figure out a way to modify it to do the final piece.
On Mon, May 27, 2013 at
?lm.fit ## may be useful to you then. Have you tried it?
-- Bert
On Mon, May 27, 2013 at 9:52 AM, ivo welch wrote:
> hi bert---thanks for the answer.
>
> my particular problem is well conditioned [stock returns] and speed is
> very important.
>
> about 4 years ago, I asked for speedier alterna
hi bert---thanks for the answer.
my particular problem is well conditioned [stock returns] and speed is
very important.
about 4 years ago, I asked for speedier alternatives to lm (and you
helped me on this one, too), and then checked into the speed/accuracy
tradeoff. http://r.789695.n4.nabble.c
Ivo:
1. You should not be fitting linear models as you describe. For why
not and how they should be fit, consult a suitable text on numerical
methods (e.g. Givens and Hoeting).
2. In R, I suggest using lm() and ?update, feeding update() data
modified as you like. This is, after all, the reason f
dear R experts---I would like to update OLS regressions with new
observations on the front of the data, and delete some old
observations from the rear. my goal is to have a "flexible"
moving-window regression, with a minimum number of observations and a
maximum number of observations. I can keep
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