24 Apr 18:05
24 Apr 18:03
In robust PCA methods, how to get variance explained?
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]]
7 Feb 22:12
library cox robust : weights?
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) > s=Surv(survival,status) site , cyto5.6 and zeb1 are my esplicative variables so the model is: > exp=coxr(formula = s ~ site + cyto5.6 + zeb1, data = IHC, trunc = 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: > exp$weights NULL If I write > plot(exp$weights) Error in plot.window(...) : 'xlim' devono essere finiti Inoltre: Warning messages: 1: In min(x) : no non-missing arguments to min; returning Inf(Continue reading)
29 Sep 01:12
27 Jul 12:17
Call for Papers ICPRAM 2012
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.
(Continue reading)
16 Jun 18:02
Re: minimum sample size for the robust counterpart of the t-test #2
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: > There is general information about sample sizes and p-values, when > using robust analogs of t, in my 2005 book (Introduction to Robust > Estimation and Hypothesis Testing, Academic Press) . > (A third edition will be out early in 2012. ) > > Hope this helps. > > Rand > > Rand Wilcox > Professor(Continue reading)
15 Jun 21:10
minimum sample size for the robust counterpart of the t-test
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@... http://cancercenter.columbia.edu/~friedman/(Continue reading)
1 Apr 23:47
Estimating robust distances in R (MVE vs. MCD)
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<-cbind(c0[,3], c0[,7], c0[,8]) rest<-cov.rob(x, method = "mve", nsamp = "exact", cor=FALSE) xrd<-mahalanobis(x, rest$center, rest$cov, inverted=FALSE) xrd<-xrd^.5 d0<-ifelse(xrd> 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(Continue reading)
31 Mar 17:35
VIF for robust regression?
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
19 Feb 03:30
hubers m-estimator in R / SPSS
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
23 Feb 17:14
Re: hubers m-estimator in R / SPSS
thanks. of course, that was my idea too, but i couldn't find out which scale estimator spss uses. no manual, book, paper or whatever contains information about that. i tried other programs (like R or the ACM-tool), which use MAD, SMAD or an iterated standard deviation, but i always get different results with spss... manfred Am 22.02.2011 00:42, schrieb Matias Salibian-Barrera: > Hello Manfred, > > I'm not familiar with SPSS, but based on my experience, I'll go out on a > limb and say that the difference may be on the scale estimator that it's > used (either a preliminary estimator like the MAD) or a simultaneously > computer M-scale. Hopefully the SPSS manual will have some details. > > Matias > > > On 11-02-18 06:35 PM, Manfred Hammerl sat down at the computer and wrote: >> 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,(Continue reading)
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