?lm.fit ## may be useful to you then. Have you tried it? -- Bert
On Mon, May 27, 2013 at 9:52 AM, ivo welch <ivo.we...@gmail.com> 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 alternatives to lm (and you > helped me on this one, too), and then checked into the speed/accuracy > tradeoff. > http://r.789695.n4.nabble.com/very-fast-OLS-regression-td884832.html > . for the particular problem I had, solve(crossprod(x),crossprod(x,y)) > worked reasonably well. moreover, it is easy to debug, being so > simple. it was faster than lm() by a factor 5.. (for a more generic > library use, it would be nice to have a warning flag when this > "algorithm" fails, in which case it would fall back on a more robust > algorithm or at least emit a warning. I wonder how much it would cost > to check the condition of the matrix before deciding on the > algorithm.) > > I looked at update(), but its documentation seems to refer to updating > models, not observations. even if it did, given the speed of lm(), I > don't think it will be that useful. > > regards, > > /iaw > > ---- > Ivo Welch (ivo.we...@gmail.com) > > On Mon, May 27, 2013 at 9:26 AM, Bert Gunter <gunter.ber...@gene.com> wrote: >> 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 for update(). >> >> -- Bert >> >> On Mon, May 27, 2013 at 8:12 AM, ivo welch <ivo.we...@anderson.ucla.edu> >> 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 observations and a >>> maximum number of observations. I can keep (X' X) and (X' y), and add >>> or subtract observations from these two quantities myself, and then >>> use crossprod. >>> >>> strucchange does recursive residuals, which is closely related, but it >>> is not designed for such flexible movable windows, nor primarily >>> designed to produce standard errors of coefficients. >>> >>> before I get started on this, I just wanted to inquire whether someone >>> has already written such a function. >>> >>> regards, >>> >>> /iaw >>> ---- >>> Ivo Welch (ivo.we...@gmail.com) >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >> >> >> >> -- >> >> Bert Gunter >> Genentech Nonclinical Biostatistics >> >> Internal Contact Info: >> Phone: 467-7374 >> Website: >> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
