Re: [R] function in nls argument -- robust estimation
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
Re: [R] function in nls argument -- robust estimation
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.
Re: [R] function in nls argument -- robust estimation
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. __
