adding observations to lm for fast recursive residuals?
In my quantreg package there is a function called lm.fit.recursive()
that, as the .Rd file
says:
Description:
This function fits a linear model by recursive least squares. It
is a utility routine for the 'khmaladzize' function of the
quantile regression package.
Usage:
lm.fit.recursive(X, y, int=TRUE)
Arguments:
X: Design Matrix
y: Response Variable
int: if TRUE then append intercept to X
Value:
return p by n matrix of fitted parameters, where p. The ith column
gives the solution up to "time" i.
It is written in fortran so it should be reasonably quick.
HTH
url: www.econ.uiuc.edu/~roger Roger Koenker
email rkoenker at uiuc.edu Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Champaign, IL 61820
On Sep 15, 2004, at 9:53 AM, <ivo_welch-rstat8783 at mailblocks.com> wrote:
dear R community: i have been looking but failed to find the following: is there a function in R that updates a plain OLS lm() model with one additional observation, so that I can write a function that computes recursive residuals *quickly*? PS: (I looked at package strucchange, but if I am not mistaken, the recresid function there takes longer than iterating over the models fresh from start to end.) I know the two functions do not do the same thing, but the main part (OLS) is the same:
> handrecurse.test <- function( y, x ) { z<- rep(NA, T); for (i in
2:T) { z[i] <- coef(lm(y[1:i] ~ x[1:i]))[2]; }; return(z); }
> system.time(handrecurse.test(y,x))
[1] 0.69 0.00 0.70 0.00 0.00
> system.time(length(recresid( y~x )))
[1] 1.44 0.07 1.59 0.00 0.00 pointers appreciated. regards, /iaw --- ivo welch
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