search for: biweight

I want to estimate the center of a distribution with lots of outliers in one tail, and thought I would use a function such as S-plus's location.m() with psi.fun=bisquare (as per MASS 3 p. 131). However, R seems not have such a function, so my questions are: 1) Is there an R equivalent to location.m()? 2) Would huber() give me results that are similar (i.e., close enough)? Thanks.

...ncer = na.omit(BreastCancer) d = dim(BCancer)[1] i1 = seq(1, d, 2) i2 = seq(2, d, 2) t1 = BCancer[i1, ] t2 = BCancer[i2, ] y2 = BCancer[i2, 11] x = 10 k = array(1:x, dim = c(x,1)) ker = array(c( "rectangular", "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian"), dim = c(8,1)) f = function(x, ker){ BreastCancer.kknn <- kknn(Class~., train = t1, test = t2, k = x, kernel = ker, distance = 1) fit = fitted(BreastCancer.kknn)...

hi all, Am new to R. I am having problems installing Bioconductor package in R on fedora core 4 running on AMD64 bit machine. this is the error message I get : gcc -shared -L/usr/local/lib -o affyPLM.so avg_log.o biweight.o chipbackground.o common_types.o do_PLMrlm.o do_PLMrma.o do_PLMthreestep.o idealmismatch.o LESN.o lm.o lm_threestep.o log_avg.o matrix_functions.o median_logPM.o medianPM.o medianpolish.o nthLargestPM.o PLM_modelmatrix.o preprocess.o psi_fns.o qnorm.o qnorm_probeset.o rlm_anova.o rlm.o rlm_PLM...

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant figure it out from the help files. thanks, rafael

Hi all - I'm stumped by the following mdl <- glm(resp ~ . , data = df, family=binomial, offset = ofst) WORKS yhat <- predict(mdl) WORKS yhat <- predict(mdl,newdata = df) FAILS Error in drop(X[, piv, drop = FALSE] %*% beta[piv]) : subscript out of bounds I've tried without offset, quoting binomial. The offset variable ofst IS in df. Previous postings indicate possible

...e from the litterature. - provide the current "cosine" as kernel = "smoothcosine" {I'd like to keep the possibility of 1-initial-letter abbreviation} Enhancement (easy, I'll do that): - We further provide both Epanechnikov and "quartic" aka "biweight" additionally in any case. Martin Maechler <maechler@stat.math.ethz.ch> http://stat.ethz.ch/~maechler/ Seminar fuer Statistik, ETH-Zentrum LEO D10 Leonhardstr. 27 ETH (Federal Inst. Technology) 8092 Zurich SWITZERLAND phone: x-41-1-632-3408 fax: ...-1228 <>< -.-.-.-.-....

...21 1.02517352 1.61796106 -10.50956979 (Intercept) VAR00001 VAR00002 VAR00003 -41.5230433 0.9388404 0.5794524 -0.1129150 I am very worried by this discrepancy what have I done wrong? I read in R:Robust Fitting of Linear Models: MM estimation is M-Estimation with Tukey's biweight initialized by a specific S-estimator and so I alter my S-Plus code to ct<-lmRobMM.robust.control(weight=c("Optimal","Bisquare")) fit<-lmRobMM(formula=V4~V1+V2+V3,data=data, robust.control=ct) and obtain for my residuals 1 2 3 4 5...

I always followed http://cran.r-project.org/bin/linux/ubuntu/ to install R on Ubuntu 8.1. I had no errors before! > install.packages("XML") Warning in install.packages("XML") : argument 'lib' is missing: using '/usr/local/lib/R/site-library' --- Please select a CRAN mirror for use in this session --- Loading Tcl/Tk interface ... done trying URL

Hello, I'm learning about robust M-estimators right now and had settled on the "Huber Proposal 2" as implemented in MASS, but further reading made clear, that at least 2 further weighting functions (Hampel, Tukey bisquare) exist. In a post from B.D. Ripley going back to 1999 I found the following quote: >> 2) Would huber() give me results that are similar (i.e., close

Hi!I'm doing a little data importing from .cel files, > setwd("/home/mandova/celfiles") > mydata<-ReadAffy() Error in sub("^/?([^/]*/)*", "", filenames, extended = TRUE) : unused argument(s) (extended = TRUE) Then I tried > filenames<-paste("GSM",c(seq(138597,138617,1)),".cel",sep="") >

I am in need of someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix