http://blog.gmane.org/gmane.comp.lang.r.robust hourly 1 1901-01-01T00:00+00:00 http://gmane.org/img/gmane-25t.png http://gmane.org http://permalink.gmane.org/gmane.comp.lang.r.robust/197 <pre>yes, the solutions will not be the same but they should be fairly similar. </pre> Kaveh Vakili 2012-04-25T19:35:52 http://permalink.gmane.org/gmane.comp.lang.r.robust/196 <pre>library(rrcov) data(bus) p &lt;- ncol(bus) rpca &lt;- PcaHubert(bus, k=p, kmax=p) summary(rpca) Hope this helps. Best regards, Valentin On Tue, Apr 24, 2012 at 6:03 PM, Michael &lt;comtech.usa-Re5JQEeQqe8AvxtiuMwx3w&lt; at &gt;public.gmane.org&gt; wrote: [[alternative HTML version deleted]] </pre> Valentin Todorov 2012-04-24T19:07:21 http://permalink.gmane.org/gmane.comp.lang.r.robust/195 <pre>I am reading about these robust PCA functions in R... I read words like "random directions"... It seems that these algos are random algorithms, i.e their results will be different each time we run them? Thank you! [[alternative HTML version deleted]] </pre> Michael 2012-04-24T16:05:27 http://permalink.gmane.org/gmane.comp.lang.r.robust/194 <pre>In robust PCA methods, how to get variance explained? For example, PcaHubert, how to get the variance explained which are similar to those concepts in traditional PCA? In traditional PCA, you have a bunch of eigenvalue lambdas... and you sort the lambdas from the biggest to the smallest, the lambda_i / (sum of all lambdas) is the variance explained by that principal component... how to obtain the equivalent concepts in PcaHubert? Thanks a lot! [[alternative HTML version deleted]] </pre> Michael 2012-04-24T16:03:47 http://permalink.gmane.org/gmane.comp.lang.r.robust/193 <pre> I'm a italian student and I use R for my final degree thesis ... I want study a Robustly Proportional Hazards (Bednarski's teory) for a dataset of medical result. I have exstimate with library coxrobust a coxr model (exponential weights) site , cyto5.6 and zeb1 are my esplicative variables so the model is: 0.95, f.w= "exp" , singular.ok = TRUE, model = FALSE) with plot(exp) I can see 5 graphs - the firs shows the standardizzed survival differences : one with Cox model, and one green with Kaplan -Meir stimator -other four show the same differences for four strata, defined by the quartiles of the estimated linear predictor. But my problem is the I want a graphic the robust exponential weight (log trasformed) versus case number for the dataset.. If I ask R about weight of the model exp: NULL If I write Error in plot.window(...) : 'xlim' devono essere finiti Inoltre: Warning messages: 1: In min(x) : no non-missing arguments to min; returning Inf 2: In max(x) : no non-missing arguments to max; returning -Inf 3: In min(x) : no non-missing arguments to min; returning Inf 4: In max(x) : no non-missing arguments to max; returning -Inf Can you help me? Thanks and best regards Elisabetta &lt;P&gt;&lt;p&gt;&lt;font face="Arial, Helvetica, sans-serif" size="2" style="font-size:13.5px"&gt;_______________________________________________________________&lt;BR&gt;1 richiesta di amicizia&lt;br&gt; &lt;a href="http://www.facebook.com/pages/Excite-Italia/101704826535618"&gt;Diventa fan di Excite su Facebook!&lt;/a&gt;&lt;/font&gt; [[alternative HTML version deleted]] </pre> Elisabetta Mattiolo 2012-02-07T21:12:34 http://permalink.gmane.org/gmane.comp.lang.r.robust/192 <pre> &gt; Dear all, I am not sure if this mail is for R-help or &gt; should be sent to R-devel instead, and therefore post to &gt; both. &gt; While using nlrob from package 'robustbase', I ran into &gt; the following problem: &gt; For psi-functions that can become zero &gt; (e.g. psi.bisquare), weights in the internal call to nls &gt; can become zero. Example: You are right. The next version of robustbase (0.8-1) will have this fixed. Note however, that for me, in your example and in other more interesting ones, as soon as I use a redescending psi() function, the IRLS iterations do *not* converge... but rather strangely ``cycle'' around the true solution. As a redescending psi() has a non-convex rho(), and non-linear problems depend quite a bit on "feasible"/"good" starting values, I currently think I would discourage from using such psi(). As others, some possibly more expert in robust non-linear fitting, may be interested, and have interesting feedback, I'm crossposting this reply to the R-SIG-robust mailing list. (Further replies should typically only go there!) You have overlooked that nlrob uses a different *formula* for nls() --- nowadays unnecessarily --- and that formula has a 'w'. So this part of your report is not correct. Martin Maechler, ETH Zurich _______________________________________________ R-SIG-Robust&lt; at &gt;r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-robust </pre> Martin Maechler 2011-12-23T16:21:22 http://permalink.gmane.org/gmane.comp.lang.r.robust/191 <pre>Hello, First time posting on the robust list. New to R. How do I get the outliers list generated via uni.plot without opening a graphics device? Regards, Ben [[alternative HTML version deleted]] </pre> Ben qant 2011-09-28T23:12:19 http://permalink.gmane.org/gmane.comp.lang.r.robust/190 <pre>Dear all, My name is Pedro Latorre Carmona, program co-chair of the "2012 International Conference on Pattern Recognition Applications and Methods (ICPRAM2012)". I send you the Call for Papers for this conference and would like to draw your attention in particular to the six special sessions that are also organised. I hope you will find it interesting and may contribute to ICPRAM 2012. ********************************************************************* 2012 International Conference on Pattern Recognition Applications and Methods (ICPRAM2012) February 6-8, 2012 Vilamoura, Algarve, Portugal http://www.icpram.org ********************************************************************* ICPRAM (1st International Conference on Pattern Recognition Applications and Methods - http://www.icpram.org/) has an open call for papers, whose deadline is set for July 26, 2011. We hope you can participate in this conference by submitting a paper reflecting your current research in any of the following tracks: - Theory and Methods - Applications ICPRAM 2012 will be held in Vilamoura, Algarve, Portugal next year, on February 6-8, 2012. The conference will be sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL2), and technically co-sponsored by IEEE Signal Processing Society, Machine Learning for Signal Processing (MLSP) Technical Committee of IEEE, AERFAI (Asociación Espanola de Reconocimiento de Formas y Analisis de Imagenes) and APRP (Associacao Portuguesa de Reconhecimento de Padroes). INSTICC is member of the Workflow Management Coalition (WfMC). ICPRAM would like to become a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition, both from theoretical and application perspectives. Contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods are especially encouraged. The conference program features a number of Keynote Lectures to be delivered by distinguished world-class researchers, including those listed below. The proceedings of ICPRAM will be submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP and Elsevier Index (EI). All accepted papers (full, short and posters) will be published in the conference proceedings, under an ISBN reference, on paper and on CD-ROM support. A short list of presented papers will be selected so that revised and extended versions of these papers will be published by Springer-Verlag in a AISC Series book. Top selected papers in specific areas of interest will be published as a special issue in the Neurocomputing Journal. Best paper awards will be distributed during the conference closing session. Please check the website for further information (http://www.icpram.org/best_paper_awards.asp). All papers presented at the conference venue will be available at the SciTePress Digital Library (http://www.scitepress.org/DigitalLibrary/). SciTePress is member of CrossRef (http://www.crossref.org/). We also would like to highlight the possibility to submit to the following Special Sessions: - Algebraic Geometry in Machine Learning - Shape Analysis and Deformable Modeling - Machine Learning for Sequences - Pattern Recognition Applications in Remotely Sensed Hyperspectral Image Analysis - High-Dimensional Inference from Limited Data: Sparsity, Parsimony and Adaptivity - Interactive and Adaptive Techniques for Machine Learning, Recognition and Perception Workshops and further special sessions are also invited, with the condition that the topics do not overlap with the existing ones. If you wish to propose a workshop or a special session, for example based on the results of a specific research project, please contact the secretariat. Workshop chairs and Special Session chairs will benefit from logistics support and other types of support, including secretariat and financial support, to facilitate the development of a valid idea. ICPRAM 2012 will be held in conjunction with ICAART 2012 (http://www.icaart.org/home.asp) and ICORES (http://www.icores.org) in Vilamoura, Algarve, Portugal next year, on February 6-8, 2012. Registration to ICPRAM will enable free access to the ICAART and ICORES conferences (as a non-speaker). IMPORTANT DATES: Regular Paper Submission: September 8, 2011 (extended) Authors Notification (regular papers): October 17, 2011 Final Regular Paper Submission and Registration: October 31, 2011 CONFERENCE CHAIR: Ana Fred, Technical University of Lisbon / IT, Portugal PROGRAM CO-CHAIRS: J. Salvador Sanchez, Jaume I University, Spain Pedro Latorre Carmona, Jaume I University, Spain KEYNOTES SPEAKERS: Ludmila Kuncheva, Bangor University, U.K. Tiberio Caetano, NICTA, Australia Francis Bach, INRIA, France Jose C. Principe, University of Florida, U.S.A. Joachim M. Buhmann, ETH Zurich, Switzerland PROGRAM COMMITTEE: Please check the program committee members at http://www.icpram.org/program_committee.asp CONFERENCE TRACKS: TRACK 1: THEORY AND METHODS - Exact and Approximate Inference - Density Estimation - Bayesian Models - Gaussian Processes - Model Selection - Graphical and Graph-based Models - Missing Data - Ensemble Methods - Neural Networks - Kernel Methods - Large Margin Methods - Classification - Regression - Sparsity - Feature Selection and Extraction - Spectral Methods - Embedding and Manifold Learning - Similarity and Distance Learning - Matrix Factorization - Clustering - ICA, PCA, CCA and other Linear Models - Fuzzy Logic - Active Learning - Cost-sensitive Learning - Incremental Learning - On-line Learning - Structured Learning - Multi-agent Learning - Multi-instance Learning - Reinforcement Learning - Instance-based Learning - Knowledge Acquisition and Representation - Meta Learning - Multi-strategy Learning - Case-based Reasoning - Inductive Learning - Computational Learning Theory - Cooperative Learning - Evolutionary Computation - Information Retrieval and Learning - Hybrid Learning Algorithms - Planning and Learning - Convex Optimization - Stochastic Methods - Combinatorial Optimization TRACK 2: APPLICATIONS - Natural Language Processing - Information Retrieval - Ranking - Web Applications - Economics, Business and Forecasting Applications - Bioinformatics and Systems Biology - Audio and Speech Processing - Signal Processing - Image Understanding - Sensors and Early Vision - Motion and Tracking - Image-based Modelling - Shape Representation - Object Recognition - Video Analysis - Medical Imaging - Learning and Adaptive Control - Perception - Learning in Process Automation - Learning of Action Patterns - Virtual Environments - Robotics </pre> Pedro Latorre Carmona 2011-07-27T10:17:01 http://permalink.gmane.org/gmane.comp.lang.r.robust/189 <pre>Dear Manuel (and list). Thank you for doing so much the work to answer my question. Much of the theory is beyond me at present although I plan on learning more about robust methods with time along with many other fields I have to learn. I would like to rephrase the question- your response deals mainly with power. What I need to know about more is Tyoe I error. Do robust methods ever increase type I -error lead to a greater number of false positives, for n=5 than does the classical t-test? In particular does the Huber with k=1.345? I have a particular reason for returning to this method even though it is apparently no longer the robust method of choice. A collaborator used it to find significance where the classical t-test did not, and I am wondering which test to believe. Thanks and best wishes, Rich On Jun 17, 2011, at 11:55 AM, Manuel Koller wrote: </pre> Richard Friedman 2011-06-21T22:56:12 http://permalink.gmane.org/gmane.comp.lang.r.robust/188 <pre>Dear Richard, Since we did not quite cover your specific case, I ran another small simulation. See the attached file. It is basically the simulation study of our paper, but for models having only an intercept. I hope I did not overlook anything when I did this. There was a lot going on today... I apologize for the overloaded plots. I guess Figures 4, 7 and 8 are the most interesting figures. As Rand already stated, the asymmetric error distributions are a problem: all the methods perform quite badly. Otherwise, the levels of the tests are pretty much ok (even for OLS, i.e., t-test). But of course, the power will be pretty bad. In numbers, for n = 5 you will have approximately the correct level (+/- 2%), but a power of about 40% only for an effect size of 1 (10% for an effect size of 0.4). And this does not really depend on which method you are using. To conclude, I would recommend to use lmrob from robustbase with the argument setting="KS2011". I hope this helps, Manuel On Thu, Jun 16, 2011 at 8:19 PM, Richard Friedman &lt;friedman-VS9ntHdz6ztaNOFNA11YEsysmGwsrwg7h13vi7wywA4&lt; at &gt;public.gmane.org&gt; wrote: </pre> Manuel Koller 2011-06-17T15:55:57 http://permalink.gmane.org/gmane.comp.lang.r.robust/187 <pre>Rand, Thanks, I know very little about robust methods. I am interested in whether rlm can be used in its default state or if I have to tearn much more to do use the methods correctly. Best wishes, Rich On Jun 16, 2011, at 2:14 PM, Rand Wilcox wrote: </pre> Richard Friedman 2011-06-16T18:19:56 http://permalink.gmane.org/gmane.comp.lang.r.robust/186 <pre>When dealing with M-estimators and the goal is to compute confidence intervals, one thing you have to be careful about is skewed distributions. Have not encountered any non-bootstrap method that performs well in simulations where the confidence interval is based on an estimate of the standard error. Just how symmetric the distribution must be seems unclear. What works better is a percentile bootstrap method, even with fairly small sample sizes. This is why the methods in my book focus on bootstrap techniques when dealing with M-estimators. However, have not yet seen the Koller and Stahel paper. Maybe this problem has been addressed. Rand Rand Wilcox Professor Dept of Psychology USC Los Angeles, CA 90089-1061 FAX: 213-746-9082 For information about statistics books and software, see http://www-rcf.usc.edu/~rwilcox/ as well as http://college.usc.edu/labs/rwilcox/home ----- Original Message ----- From: Richard Friedman &lt;friedman-VS9ntHdz6ztaNOFNA11YEsysmGwsrwg7h13vi7wywA4&lt; at &gt;public.gmane.org&gt; Date: Thursday, June 16, 2011 9:02 am Subject: Re: [RsR] minimum sample size for the robust counterpart of the t-test #2 To: Rand Wilcox &lt;rwilcox-DG7SOAwx7rg&lt; at &gt;public.gmane.org&gt;, r-sig-robust-0bNBQ1PAWB4BXFe83j6qeQ&lt; at &gt;public.gmane.org </pre> Rand Wilcox 2011-06-16T18:14:39 http://permalink.gmane.org/gmane.comp.lang.r.robust/185 <pre> &gt; Dear List, I am a beginner in the use of robust methods. Is &gt; there a minimum sample size for which the robust analog of a two &gt; sample t-test using rlm with default parameters and categorical &gt; explanatory variables may be trusted to yield reliable p-values? &gt; Is so, can you please point me at a reference which treats this &gt; problem. It's a bit more complicated, because "the" robust analog does not exist: There are an infinite number of possible robust analogues to the t-test, and my two colleagues have been actively researching on this, not just for the two-sample case, but the general lm() case, *with* an emphasis on small-sample performance: Originally, (I think) they started answering the question (+/-): How do you have to estimate sj^2 := \Var(\hat{\beta_j}) such that \hat{\beta_j} +/- 1.96 * sj has the correct coverage probability of 95%, also for small samples (and of course generalizing to other probs. alpha). Here's their main publication : Koller, M. and Stahel, W.A. (2011), Sharpening Wald-type inference in robust regression for small samples, _Computational Statistics &amp; Data Analysis_ *55*(8), 2504-2515. and the good news is that Manuel Koller has implemented everything in package robustbase, lmrob() {which you'd use instead of rlm()}, and to use the new methods, you use (something like) summary( fmod &lt;- lmrob(Y ~ ., data=..., setting = 'KS2011') ) I've CC'ed them here, just in case they are accidentally not yet subscribed to the R-SIG-Robust mailing list. I hope this helps, Martin Maechler, ETH Zurich &gt; Thanks and best wishes, Rich &gt; ------------------------------------------------------------ &gt; Richard A. Friedman, PhD Associate Research Scientist, ................ ................ &gt; R-SIG-Robust-0bNBQ1PAWB4BXFe83j6qeQ&lt; at &gt;public.gmane.org mailing list &gt; https://stat.ethz.ch/mailman/listinfo/r-sig-robust </pre> Martin Maechler 2011-06-16T16:43:46 http://permalink.gmane.org/gmane.comp.lang.r.robust/184 <pre>Dear Rand (and List), I read the relevant sections of your book and while informative it did not answer my question directly as best I can see. I will restate the question more explicitly: A robust analog of the two sample t-test is performed with the rlm function with the default parameters of the Huber method with K=1.345. Is there a minimum sample size for which it should be trusted? are 5 samples enough? 10 samples? If this question does not have a simple answer please let me know. Thanks and best wishes, Rich On Jun 15, 2011, at 3:19 PM, Rand Wilcox wrote: </pre> Richard Friedman 2011-06-16T16:02:04 http://permalink.gmane.org/gmane.comp.lang.r.robust/183 <pre>Dear List, I am a beginner in the use of robust methods. Is there a minimum sample size for which the robust analog of a two sample t-test using rlm with default parameters and categorical explanatory variables may be trusted to yield reliable p-values? Is so, can you please point me at a reference which treats this problem. Thanks and best wishes, Rich ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist, Biomedical Informatics Shared Resource Herbert Irving Comprehensive Cancer Center (HICCC) Lecturer, Department of Biomedical Informatics (DBMI) Educational Coordinator, Center for Computational Biology and Bioinformatics (C2B2)/ National Center for Multiscale Analysis of Genomic Networks (MAGNet) Room 824 Irving Cancer Research Center Columbia University 1130 St. Nicholas Ave New York, NY 10032 (212)851-4765 (voice) friedman-VS9ntHdz6ztaNOFNA11YEsysmGwsrwg7h13vi7wywA4&lt; at &gt;public.gmane.org http://cancercenter.columbia.edu/~friedman/ I am a Bayesian. When I see a multiple-choice question on a test and I don't know the answer I say "eeney-meaney-miney-moe". Rose Friedman, Age 14 </pre> Richard Friedman 2011-06-15T19:10:03 http://permalink.gmane.org/gmane.comp.lang.r.robust/182 <pre> I don't know if there is work on a robust VIF. Here are my two cents: - What type of variable do you have ? If some of them are discrete with very few different values, this may cause problem to robust covariance matrices estimation. - Your approach to apply the "partialling out" of the robust matrix, as in the OLS case, might or might not be correct, I don't know. - If you believe that 1/(1-R^2_i) is a good measure, then you might want to compute its direct robust equivalent. The output of lmrob does not provide a R^2, but the output of lmRob does. We have recently published a paper however that shows that the robust R-squared provided by lmRob is biased, sometimes to a large extent. We provide a consistent and robust estimator of R-squared and a version adjusted for the sample size. See my previous post for an example and the code at https://stat.ethz.ch/pipermail/r-sig-robust/2010/000290.html Olivier ref: Renaud, O. &amp; Victoria-Feser, M.-P. (2010). A robust coefficient of determination for regression. Journal of Statistical Planning and Inference, 140, 1852-1862. http://dx.doi.org/10.1016/j.jspi.2010.01.008 On 31/03/2011 17:35, Nicholas Lewin-Koh wrote: </pre> Olivier Renaud 2011-04-06T09:33:36 http://permalink.gmane.org/gmane.comp.lang.r.robust/181 <pre>I have been trying to estimate robust Mahalanobis distances in R for a set of three regressors that includes one dummy variable. Initially, I tried generating robust MCD estimates and their associated VCE using cob.rob. However, when I did so I received the following error message: "Error in solve.default(cov, ...) : Lapack routine dgesv: system is exactly singular". I believe that the MCD estimator involves subsampling and that the parameter for the discrete variable could not be identified in one of the subsamples due to insufficient variance. When using the minimum volume ellipsoid (MVE) estimator, I did not experience any problems. My code is given below. x&lt;-cbind(c0[,3], c0[,7], c0[,8]) rest&lt;-cov.rob(x, method = "mve", nsamp = "exact", cor=FALSE) xrd&lt;-mahalanobis(x, rest$center, rest$cov, inverted=FALSE) xrd&lt;-xrd^.5 d0&lt;-ifelse(xrd&gt; 3.0575159,1,0) Can anyone explain to me why the MVE estimator is able to accommodate discrete variables, whereas the MCD estimator cannot do so? I would like to be certain that the method I used to estimate robust distances is valid in light of the inclusion of a discrete variable in the regressor set. -- Jim James W. Shaw, Ph.D., Pharm.D., M.P.H. Assistant Professor Department of Pharmacy Administration College of Pharmacy University of Illinois at Chicago 833 South Wood Street, M/C 871, Room 266 Chicago, IL 60612 Tel.: 312-355-5666 Fax: 312-996-0868 Mobile Tel.: 215-852-3045 </pre> James Shaw 2011-04-01T21:47:16 http://permalink.gmane.org/gmane.comp.lang.r.robust/180 <pre>Hi, I was looking at the vif function in the car package and it it is trivial to modify to make a version for robust regression. However, after trying it out I noticed that what were reasonable values under ols, jumped way up. So my thought is that either, I made a coding error, and the weights attribute needs to be used to modify the variance covariance matrix of the coefficients Or, the reduced variance from the robust regression, causes peripheral points (outside the mve) to have much more influence in the r^2's for each predictor. So that the standard vif measure, 1/(1-R^2_i) is not relevant in this context. Am I off base here? Thanks Nicholas </pre> Nicholas Lewin-Koh 2011-03-31T15:35:10 http://permalink.gmane.org/gmane.comp.lang.r.robust/179 <pre>Martin, I agree fully with you. It reminds me that some 30 years ago I puzzled around why SPSS produces negative variances. It was not too difficult to find out the reason by trial and error. Conclusion: We didn't recommend the usage SPSS at a scientific level any more, and some years ago, we cancelled all licenses from our institute. I am not surprised that Hubers algorithm remains mysterious in that package. Sorry to say that. Rudi Martin Maechler wrote: </pre> Rudolf Dutter 2011-02-23T20:53:41 http://permalink.gmane.org/gmane.comp.lang.r.robust/178 <pre> MH&gt; thanks. of course, that was my idea too, but i couldn't find out which MH&gt; scale estimator spss uses. no manual, book, paper or whatever contains MH&gt; information about that. i tried other programs (like R or the ACM-tool), MH&gt; which use MAD, SMAD or an iterated standard deviation, but i always get MH&gt; different results with spss... so why on earth are you using SPSS at all? ... actually it's hard to believe that they don't document what their M-estimate does ... but that would really belong to an SPSS help list rather than an R one, right? Martin MH&gt; manfred MH&gt; Am 22.02.2011 00:42, schrieb Matias Salibian-Barrera: &gt;&gt; Hello Manfred, &gt;&gt; &gt;&gt; I'm not familiar with SPSS, but based on my experience, I'll go out on a &gt;&gt; limb and say that the difference may be on the scale estimator that it's &gt;&gt; used (either a preliminary estimator like the MAD) or a simultaneously &gt;&gt; computer M-scale. Hopefully the SPSS manual will have some details. &gt;&gt; &gt;&gt; Matias &gt;&gt; &gt;&gt; &gt;&gt; On 11-02-18 06:35 PM, Manfred Hammerl sat down at the computer and wrote: &gt;&gt;&gt; hello, i'm new to robust statistics but found out very quick that R &gt;&gt;&gt; (used huber, hubers and huberM) and SPSS (huber m-estimator) calculate &gt;&gt;&gt; different location estimates, given the same tuning constant k. since &gt;&gt;&gt; the differences a really not very small, i wanted to get some detailed &gt;&gt;&gt; information about this but i couldn't find out which algorithm SPSS uses &gt;&gt;&gt; to calculate hubers estimator so far... &gt;&gt;&gt; &gt;&gt;&gt; does anyone know something about SPSS's huber function? &gt;&gt;&gt; &gt;&gt;&gt; greetings, &gt;&gt;&gt; &gt;&gt;&gt; manfred &gt;&gt;&gt; &gt;&gt;&gt; _______________________________________________ &gt;&gt;&gt; R-SIG-Robust-0bNBQ1PAWB4BXFe83j6qeQ&lt; at &gt;public.gmane.org mailing list &gt;&gt;&gt; https://stat.ethz.ch/mailman/listinfo/r-sig-robust &gt;&gt; MH&gt; -- MH&gt; MMag. Manfred Hammerl, BBakk. MH&gt; manfred.hammerl-oKmA2oUcaWH+YS6zDuZUZQ&lt; at &gt;public.gmane.org MH&gt; _______________________________________________ MH&gt; R-SIG-Robust-0bNBQ1PAWB4BXFe83j6qeQ&lt; at &gt;public.gmane.org mailing list MH&gt; https://stat.ethz.ch/mailman/listinfo/r-sig-robust </pre> Martin Maechler 2011-02-23T20:25:29 http://permalink.gmane.org/gmane.comp.lang.r.robust/177 <pre>hello, i'm new to robust statistics but found out very quick that R (used huber, hubers and huberM) and SPSS (huber m-estimator) calculate different location estimates, given the same tuning constant k. since the differences a really not very small, i wanted to get some detailed information about this but i couldn't find out which algorithm SPSS uses to calculate hubers estimator so far... does anyone know something about SPSS's huber function? greetings, manfred </pre> Manfred Hammerl 2011-02-19T02:30:13 Search the mailing list at Gmane query http://search.gmane.org/?group=$group=gmane.comp.lang.r.robust