This may be useful:
Sven Hammarling and Craig Lucas
"Updating the QR factorization and the least squares problem"
http://eprints.ma.man.ac.uk/1192/01/covered/MIMS_ep2008_111.pdf
http://www.maths.manchester.ac.uk/~clucas/updating/
2016-07-21 20:02 GMT+02:00 jeremiah rounds <roundsjerem...@gmail.com>:
> Hi,
>
> A not unusual task is performing a multiple regression in a rolling window
> on a time-series. A standard piece of advice for doing in R is something
> like the code that follows at the end of the email. I am currently using
> an "embed" variant of that code and that piece of advice is out there too.
>
> But, it occurs to me that for such an easily specified matrix operation
> standard R code is really slow. rollapply constantly returns to R
> interpreter at each window step for a new lm. All lm is at its heart is
> (X^t X)^(-1) * Xy, and if you think about doing that with Rcpp in rolling
> window you are just incrementing a counter and peeling off rows (or columns
> of X and y) of a particular window size, and following that up with some
> matrix multiplication in a loop. The psuedo-code for that Rcpp
> practically writes itself and you might want a wrapper of something like:
> rolling_lm (y=y, x=x, width=4).
>
> My question is this: has any of the thousands of R packages out there
> published anything like that. Rolling window multiple regressions that
> stay in C/C++ until the rolling window completes? No sense and writing it
> if it exist.
>
>
> Thanks,
> Jeremiah
>
> Standard (slow) advice for "rolling window regression" follows:
>
>
> set.seed(1)
> z <- zoo(matrix(rnorm(10), ncol = 2))
> colnames(z) <- c("y", "x")
>
> ## rolling regression of width 4
> rollapply(z, width = 4,
> function(x) coef(lm(y ~ x, data = as.data.frame(x))),
> by.column = FALSE, align = "right")
>
> ## result is identical to
> coef(lm(y ~ x, data = z[1:4,]))
> coef(lm(y ~ x, data = z[2:5,]))
>
> [[alternative HTML version deleted]]
>
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