[R] adding observations to lm for fast recursive residuals?

[roger koenker]

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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