Hi Kate and others, thanks for the info. Btw, you sent the different methods to analyze the data: nls, nls.lm and nlrob. Comparing the results visually nlrob performed better then nls, but nls.lm (using the 0.9 quantile of residuals) was still better than nlrob. My data may have a rather large amount of contamination, so that an M-estimator with a higher breakdown point should be used (least trimmed squares?). I haven't found this in R and wouldn't know how to implement it. But I can live with my results. Then remains the question of obtaining the parameter st. errors. Jackknife was suggested. Is there an R function I could use for that?
cheers, Fernando Katharine Mullen wrote: > > Dear Martin, > > Thanks for the ideas regarding the relation of what Fernando is doing with > robust regression. Indeed, it's an important point that he can't consider > the standard error estimates on his parameters correct. > > I know from discussion off-list that he's happy with the results he has > now; nevertheless the robust regression route may be an interesting > alternative. I'm posting a scipt to R-SIG-robust now that compares the 3 > ways (nls, nlrob and nls.lm w/residuals above a certain quantile set to > zero). > > best, > Kate > > On Sat, 10 May 2008, Martin Maechler wrote: > >> Hi Kate and Fernando, >> >> I'm late into this thread, >> but from reading it I get the impression that Fernando really >> wants to do *robust* (as opposed to least-squares) non-linear >> model fitting. His proposal to set residuals to zero when they >> are outside a given bound is a very special case of an >> M-estimator, namely (if I'm not mistaken) the so-called "Huber >> skipped-mean", an M-estimator with psi-function >> psi <- function(x, k) ifelse(abs(x) <= k, x, 0) >> It is known that this can be far from optimal, and either using >> Huber-psi or "a redescender" such as Tukey's biweight can be >> considerably better. >> Also note that the standard inference (std.errors, P-values, ...) >> that you'd get from summary(nlsfit) or anova(nls1, nl2) is >> *invalid* here, since you are effectively using *random* weighting. >> >> The nlrob() function in package 'robustbase' >> implements M-estimation of nonlinear models directly. >> Unfortunately, how to do correct inference in this situation >> is a hard problem, probably even an open research question in >> parts. I would expect that "the" bootstrap should work if you only >> have a few outliers. >> >> I don't have time at the moment to look at the example data and >> the model, and show you how to use it for nlrob(); >> if you find a way to you it for nls() , then the same should >> work for nlrob(). >> >> I'm CCing this to the specialists for "Robust Stats with R" >> mailing list, R-SIG-robust. >> >> Best regards, >> Martin Maechler >> ETH Zurich >> >> >>>>> "KateM" == Katharine Mullen <[EMAIL PROTECTED]> >> >>>>> on Fri, 9 May 2008 15:50:08 +0200 (CEST) writes: >> >> KateM> You can take minpack.lm_1.1-0 (source code and MS Windows >> build, >> KateM> respectively) from here: >> >> KateM> http://www.nat.vu.nl/~kate/minpack.lm_1.1-0.tar.gz >> KateM> http://www.nat.vu.nl/~kate/minpack.lm_1.1-0.zip >> >> KateM> The bug that occurs when nprint = 0 is fixed. Also fixed is >> another >> KateM> problem suggested your example: when the argument par is a >> list, calling >> KateM> summary on the output of nls.lm was not working. >> >> KateM> I'll submit the new version to CRAN soon. >> >> KateM> This disscusion has been fruitful - thanks for it. >> >> KateM> On Fri, 9 May 2008, Katharine Mullen wrote: >> >> >> You indeed found a bug. I can reproduce it (which I should have >> tried to >> >> do on other examples in the first place!). Thanks for finding it. >> >> >> >> It will be fixed in version 1.1-0 which I will submit to CRAN >> soon. >> >> >> >> On Fri, 9 May 2008, elnano wrote: >> >> >> >> > >> >> > Find the data (data_nls.lm_moyano.txt) here: >> >> > ftp://ftp.bgc-jena.mpg.de/pub/outgoing/fmoyano >> >> > >> >> > >> >> > >> >> > Katharine Mullen wrote: >> >> > > >> >> > > Thanks for the details - it sounds like a bug. You can either >> send me the >> >> > > data in an email off-list or make it available on-line >> somewhere, so that >> >> > > I and other people can download it. >> >> > > >> >> > > >> >> > > ______________________________________________ >> >> > > 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. >> >> > > >> >> > > >> >> > >> >> > -- >> >> > View this message in context: >> http://www.nabble.com/function-in-nls-argument-tp17108100p17146812.html >> >> > Sent from the R help mailing list archive at Nabble.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. >> >> > >> >> >> >> ______________________________________________ >> >> 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. >> >> >> >> KateM> ______________________________________________ >> KateM> R-help@r-project.org mailing list >> KateM> https://stat.ethz.ch/mailman/listinfo/r-help >> KateM> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> KateM> and provide commented, minimal, self-contained, reproducible >> code. >> > > ______________________________________________ > 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. > > -- View this message in context: http://www.nabble.com/function-in-nls-argument-tp17108100p17337520.html Sent from the R help mailing list archive at Nabble.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.