similar to: [Fwd: R code and robust regression]

Hello, the R people. I look for robust regression in R. This method is available in S, its name is rreg. Colud anyone teach me ? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the

Hi. I'm looking for the robust M estimates comparable to "location.m" in S-PLUS? Alternatively, I guess I could use > lqs(x~1) But ... is "location.m" in a package? Thanks, M. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hi, trying to do a robudt regression of a two-way linear model, I keep getting the following error: > lqs(obs ~ y + s -1,method="lms", contrasts=list(s=("contr.sum"))) Error: lqs failed: all the samples were singular Robust regression with M-estimators works (also regular least square fits, of course): rlm.formula(formula = obs ~ y + s - 1, method = "M",

Hola! Anybody knows if there exists somewhere in R some implementation of robust Poisson regression, where robust is taken in the sense as usen in rlm(MASS). I found something in the package wle, but only for the Poisson distribution, not for regression. For the moment I try to use linear models with the square-root transformation, and rlm. Kjetil -- Kjetil Halvorsen. Peace is the most

Hi, i. After estimating some coefficients using robust regression with rlm() or lqs(), I wonder if there exist some measures of the goodness-of-fit as those for standard linear model(R2)... or evenly if it's a statistics non-sense to look for since I do not find any mention of that in differents chapters on robust and resistant regression or in severals R documentation (Fox, Ripley and

hi guys and gals ... How are you all ... i have to do something in robust regression by R programm , and i have some problems as following: *the first :* suppose w(r) =1/(1 r^2) and r <- c(7.01,2.07,7.061,5.607,8.502,54.909,12.222) and i want to exclude some values from r so that (abs(r)>4.9 )... after ,i want to used (w) to get on coefficients beta0 and beta1 (B1 <-

Your understanding isn't similar to mine. Mine says robust/resistant methods are for data with heavy tails, not heteroscedasticity. The common ways to approach heteroscedasticity are transformation and weighting. The first is easy and usually quite effective for dose-response data. The second is not much harder. Both can be done in R with nls(). Andy From: Quin Wills > > I am

> 3x4 Error: unexpected symbol in "3x4" R has no idea that you equate "x" as multiplication.. use an astrix > 3*4 [1] 12 dominic wrote > > This is basically my code: > > library(MASS) > lmsreg(formula = b0 ~ b1 + b3 + b1xb2, data=mydata) > > b1xb2 is an interaction but it was the centered value for a continuous > variable times a

Dear Group, I am having trouble with using rlm on multivariate data sets. When I call rlm I get Error in lm.wfit(x, y, w, method = "qr") : incompatible dimensions lm on the same data sets seem to work well (see code example). Am I doing something wrong? I have already browsed through the forums and google but could not find any related discussions. I use Windows XP and R

I am using "nls" to fit dose-response curves but am not sure how to approach more robust regression in R to get around the problem of the my error showing increased variance with increasing dose. My understanding is that "rlm" or "lqs" would not be a good idea here. 'Fairly new to regression work, so apologies if I'm missing something obvious.

This morning a handful of workstations got upgraded from 6.3 to 6.4 and things seemed to be functional, until we tried to log in to one of our instrument accounts that for various reasons uses olvwm/openwin as the desktop (and no, I don't have the option of changing that at this time). The desktop appears to load correctly, and mouse-focus and clicking appears to work, but the

Can anyone comment or point me to a discussion of the pros and cons of robust regressions, vs. a more "manual" approach to trimming outliers and/or "normalizing" data used in regression analysis?

Hi Timo, i have a few ( not really many ) errors like this with lmtp Jun 7 09:41:23 mail02 dovecot: lmtp(11034, user at user.de): DELlOoq+7U0aKwAAZA6IsQ: msgid=<4DEDD502.705020 at ameriton.com>: save failed to INBOX: BUG: Unknown internal error Jun 7 09:36:22 mail01 dovecot: lmtp(26456, user at user.de): save: box=INBOX, uid=error,

Hello. I just downloaded R onto a new computer, and after entering library(MASS), I still get the message "Error: could not find function "predict.lda"" when I try to use the predict.lda function (even just "predict.lda()") Can anyone help me out? Thank you! Diana Connett

Hi all, I have probably a basic question, but I can't seem to find the answer in the literature or in the R-archives. I would like to do a robust ANCOVA (using either rlm or lmRob of the MASS and robust packages) - my response variable deviates slightly from normal and I have some "outliers". The data consist of 2 factor variables and 3-5 covariates (fdepending on the model).

Dear all, i am trying to redo the 'eyestudy' analysis presented on the site http://www.ats.ucla.edu/stat/stata/faq/relative_risk.htm with R (1.9.0), with special interest in the section on "relative risk estimation by poisson regression with robust error variance". so i guess rlm is the function to use. but what is its equivalent to the glm's argument "family"

I'm working with data measured in a tunnel to estimate the emission factor of heavy & light vehicles. I tried to use 'gnls' and I get the following Error: >> Error in "coef<-.corARMA"(*tmp*, value = c(174.267493382104, 173.412740072763 : >> Coefficient matrix not invertible Here is my R-code: data <- d.plabutsch.neu # calculating the starting

We have a choice when calculating the Huber location estimate: > set.seed(221205) > y <- 7 + 3*rt(30,1) > library(MASS) > huber(y)$mu [1] 5.9117 > coefficients(rlm(y~1)) (Intercept) 5.9204 I was surprised to get two different results. The function huber() works directly with the definition whereas rlm() uses iteratively reweighted least squares. My surprise is

R-experts, I have fitted many different lines. The fast-tau estimator (yellow line) seems strange to me?because this yellow line is not at all in agreement with the other lines (reverse slope, I mean the yellow line has a positive slope and the other ones have negative slope). Is there something wrong in my R code ? Is it because the Y variable is 1 vector and should be a matrix ? Here is the

Hi there. I got myself a pair of old Intel Xeon blades, which I plan to repurpose with CentOS. The model is : HP bl20p-g3 server blade Manual http://h18004.www1.hp.com/products/quickspecs/12322_ca/12322_ca.pdf Now, the main problem with this hardware is that LVD UW SCSI HDDs are hard to find and hella expensive if you find em (and of reduced capacity). Any of you know: 1. If there's any