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://comments.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://comments.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://comments.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; returni</pre> Elisabetta Mattiolo 2012-02-07T21:12:34 http://comments.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://comments.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 </pre> Pedro Latorre Carmona 2011-07-27T10:17:01 http://comments.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 Ba</pre> Richard Friedman 2011-06-15T19:10:03 http://comments.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 b</pre> James Shaw 2011-04-01T21:47:16 http://comments.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://comments.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 http://comments.gmane.org/gmane.comp.lang.r.robust/166 <pre> dear all, I want to choose between different polynomial robust regressions, however R2 may be misleading since the highest de degree is, the "better fit" to data. Is there any function implemented in robust or robustbase that computes an index for "model selection" ??? Thanx for helping! pep </pre> Pep Serra 2010-12-01T14:48:14 http://comments.gmane.org/gmane.comp.lang.r.robust/164 <pre>Dear all, Trying to choose between different curve fittings within different robust models (what we would get with cftools in MATLAB, but I am definetly R-addicted). Maybe it is a trivial questions: lm1&lt;-lmrob(y ~ x, data=a) pl2&lt;-lmrob(y ~ poly (x,2), data=a) pl3&lt;-lmrob(y ~ poly (x,3), data=a) pl4&lt;-lmrob(y ~ poly (x,4), data=a) pl5&lt;-lmrob(y ~ poly (x,5), data=a) pl6&lt;-lmrob(y ~ poly (x,6), data=a) I want to choose between different models according to the best fit, but the problem is that there is no "best fit" in robust (or R2). How could I do that? In addition, I would love to plot these models (scatterplot and than later add the lines to see how the models look like) I tried, for instance: lines (x,predict (pl2),col="red") but I get many curves (I think) or a filled polygon. Is there any way to get a simple line out of the model? </pre> Pep Serra 2010-11-29T14:52:08 http://comments.gmane.org/gmane.comp.lang.r.robust/162 <pre>Standard glm returns a deviance field, which is apparently minus twice the maximized log-likelihood. glmrob claims to do the same in its help page, but seems to always return NULL for deviance. It seems to be an unimplemented feature. Does anyone have code or an algorithm that can calculate it? Any pointers appreciated Alex Holcombe www.psych.usyd.edu.au/staff/alexh/ </pre> Alex Holcombe 2010-10-26T06:57:47 http://comments.gmane.org/gmane.comp.lang.r.robust/160 <pre> Dear RsR list members, I somehow got stuck wth lmRob and I am sure I am missing something and I do not know what. It is my first time using robust statistics and I have read all your posts and pdfs recommended I think it is likely to be a newbie question. I ploted two variables and the pictures shows a number of outliers that I want to identify given that the main trend between those two measurements is positive. (although they look like a lot, it is more or less 800 points out of 6000) I used then robust&lt;-lmRob(WPROB~NPP,data=a) lmRob(formula = WPROB ~ NPP, data = a) Coefficients: (Intercept) NPP 499.86274538 -0.00990502 Degrees of freedom: 6002 total; 6000 residual Residual standard error: 250.3199 1) I do not really understand the slope of the regression, why is it negative??? then used plot.lmRob(robust) and choose for option 11 (scatter plot with fits) What is meant to be the x axis (WPROB) turns into the y axis. The regression line and the confidence intervals sh</pre> Pep Serra 2010-10-19T14:32:42 http://comments.gmane.org/gmane.comp.lang.r.robust/160 <pre></pre> Pep Serra 2010-10-19T14:32:42 http://comments.gmane.org/gmane.comp.lang.r.robust/157 <pre>Hi, I have recently conducted a Monte Carlo simulation study for robust univariate *nonlinear* regression estimators using small sample data taken from case studies in the chemical engineering field. The paper is available from doi:10.1016/j.compchemeng.2010.04.009 A very unusual finding was that for *pure* Gaussian error some of the robust estimators could *outperform* the least squares estimator. Even though I do not known of any theoretical result which prevents this behavior to happen, I have never seen it reported either. I would like to known whether you find this acceptable or not and what you think might be causing it. Thanks in advance for your help. Eduardo L.T. Conceição Dept. of Chemical Engineering University of Coimbra Portugal e-mail: econceicao&lt; at &gt;kanguru.pt; etc&lt; at &gt;eq.uc.pt </pre> Eduardo Conceição 2010-07-13T03:45:01 http://comments.gmane.org/gmane.comp.lang.r.robust/155 <pre> I'd like to S-estimates of regression, could anyone give me a hint whether a solution of this problem is already implemented or point me to some reference? Thanks!!!Nanda _________________________________________________________________ Los cochazos de los famosos Patrick Dempsey, Tom Cruise o Michael Douglas presumen de automóvil [[alternative HTML version deleted]] </pre> Nanda Mendez 2010-06-29T19:35:03 http://comments.gmane.org/gmane.comp.lang.r.robust/152 <pre> I'd like to compute spherical principal components, could anyone give me a hint whether a solution of this problem is already implemented or point me to some reference? Fernanda _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]] </pre> Nanda Mendez 2010-06-16T22:06:27 http://comments.gmane.org/gmane.comp.lang.r.robust/150 <pre>Dear R-SIG Robust I'd like to estimate the parameters of a negative binomial in a robust way. Could anyone give me a hint whether a solution of this problem is already implemented or point me to some reference? Thank you very much! Kind regards Markus Kalisch </pre> Markus Kalisch 2010-05-28T12:40:15 http://comments.gmane.org/gmane.comp.lang.r.robust/149 <pre>I am wanting to use Box-Cox transformed values to develop a reference intervals for clinical chemistry tests given 25 observations per test. After I have used the transformed values to find the range as a function of the interquartile range, I want to remap the transformed variables back to the original scale using a reverse Box-Cox transformation. This will allow me to present the reference range in the original scale. I have looked at the alr3 package, but do not see a way to get the reverse transformation and I have looked at boxcox (MASS), but it is dealing with linear models. Any guidance will be appreciated. Mark R. Mark Sharp, Ph.D. msharp-KTAgM73DmtE&lt; at &gt;public.gmane.org&lt;mailto:msharp-KTAgM73DmtE&lt; at &gt;public.gmane.org&gt; [[alternative HTML version deleted]] </pre> Mark Sharp 2010-05-13T20:36:54 http://comments.gmane.org/gmane.comp.lang.r.robust/148 <pre>Hello all, I'm just a beginner on R issue, but improving. Does anyone knows how can I get vote concentration to each ID by city (CIDADE) considering each UF (State), because my data is long and has all states together and IDs repeating across states. I also tried to get totals and then apply for division, but R din't allow me to attach new column with less rows, of course. So, someone have any light for me do that? Can I print values all without my pain programing? total.UF &lt;- tapply(senador$Votos, senador$UF, sum) total.MUN &lt;- tapply(senador$Votos, senador$CODMUNIC09, sum) z &lt;- cbind(senador, total.UF) Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 54861, 27 ========= my data look likes these =========== ID UF CIDADE VOTOS 1515 SP São Paulo 15 1515 SP Campinas 100 1515 SP Santos 0 1212 SP São Paulo 1500 1212 SP Piracicaba 20 1212 SP Campinas 300</pre> Daniel 2010-04-23T04:24:05 http://comments.gmane.org/gmane.comp.lang.r.robust/147 <pre>Hi dears I am writing a program in R for robust ridge estimator based on LTS. is there any code or package relating to it? could you please help me out? Regards Alamgir Ph.D scholar [[alternative HTML version deleted]] </pre> Alamgir Gir 2010-04-10T06:43:55 Search the mailing list at Gmane query http://search.gmane.org/?group=$group=gmane.comp.lang.r.robust