similar to: robust regression

I?ve a data set and i want fit a kernel density estimate to the data. but using the k-nearest neighbour method. How i do this with R. thanks -- bertola at fastmail.fm --

Hi, I've used the Mathematica to produce 3D graphics, contour plots of a bivariate normal distribution Now I want make these graphics in R, but i do not know how. I would like to: - Plot a 3D graph for some different variance matrix - Plot the contour plots - Find and try to plot (in the 3d graph ou contour plot) the (1-a)% confidence region based in a chi-square(a) with the degrees of

I make a regression using the lm.rigde function from library MASS. Now i want some measures of influence. Is there a way to use a lm.ridge object into the function influence.measures? -- bertola at fastmail.fm --

I'm looking for data to use with nonparametrics exploration technics. I'm a undergraduate student in statistics at the Unicamp (Brazil), and i've to make a project (with orientation of a professor). I choose nonparametrics statistics to make the project. But in this project i must have to use a databank and present analisys. So, if somebody give me indications where i can find

Hi, I have been looking through all packages but I cannot find a routine for LAD-regression (L1-norm-regression). Is there none? Willi

You'll find that it is a lot easier to do it in R: # lets first simulate a bivariate normal sample library(MASS) bivn <- mvrnorm(1000, mu = c(0, 0), Sigma = matrix(c(1, .5, .5, 1), 2)) # now we do a kernel density estimate bivn.kde <- kde2d(bivn[,1], bivn[,2], n = 50) # now plot your results contour(bivn.kde) image(bivn.kde) persp(bivn.kde, phi = 45, theta = 30) # fancy contour with

A few simple questions from a novice R user. (I am running R version 0.62.3 Beta (Sept 8, 1998) under Windows NT.) 1) How do I time the execution of a function/program in R for Windows? Is there the equivalent of a dos.time function? 2) Can anyone send me details on the random number generator used by R (period, etc.). Also, why is it different from the SuperDuper RNG in S? 3) Will there

Hi, Are there some texts about time series that teatches it with R? And site 'bout time series "for dummies" , can anyone indicate them for me? thanks a lot Rafael Bertola Undergraduate in Statistics at UNICAMP - Brasil -------------------------------------------------- --------------------------------------------------

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

I got this offlist from someone on the list. This is abuse and should be punished. This didn't anwser my post and was directed to me off-list without invitation. All it was was a promotion. -----Forwarded Message----- From: Dave St John <groups@mediacast1.com> To: drew@drewb.com Subject: Icecast2 and ices Date: 22 Aug 2003 19:22:17 -0600 Hello Drew, saw your post on the xiph list

On 31/03/2018 11:57 AM, varin sacha via R-help wrote: > Dear R-experts, > > Here below my reproducible R code. I want to add many straight lines to a plot using "abline" > The last fit (fast Tau-estimator, color yellow) will not appear on the plot. What is going wrong ? > Many thanks for your reply. > It's not quite reproducible: you forgot the line to create

On Mon, 2003-08-25 at 17:04, W. Kevin Pedigo wrote: > But if your problem is serving more bandwidth than you've got, you gotta > serve less (narrower or fewer streams) or get more bandwidth. It's that > simple. Tell us what you want to do about it, and we'll try to help. OK. I've gotten everything running with one problem. I'd like to downsample a live stream.

You need to pay attention to the documentation more closely. If you don't know what something means, that is usually a signal that you need to study more... in this case about the difference between an input variable and a design (model) matrix. This is a concept from the standard linear algebra formulation for regression equations. (Note that I have never used RobPer, nor do I regularly

On Mon, 2003-08-25 at 14:10, Dave St John wrote: > Sorry for trying to help. You tried to sell me a solution, not assist me in working out my own. I don't think that's what this list was intended for. I and most others would shy away from a list where this type of abuse is tolerated. As it is, the traffic is eerily quiet. Next thing you'd know, only you would be left. >

Dear R-experts, Here below my reproducible R code. I want to add many straight lines to a plot using "abline" The last fit (fast Tau-estimator, color yellow) will not appear on the plot. What is going wrong ? Many thanks for your reply. ########## Y=c(2,4,5,4,3,4,2,3,56,5,4,3,4,5,6,5,4,5,34,21,12,13,12,8,9,7,43,12,19,21)

Dear Miss R, I have a large dataset that is skewed and would like to calculate confidence intervals for my regression line. Unfortunately I have trouble finding commands for rubust regression and in particular for the confidence intervals... Can you possibly give a hint? Thank you and best regards, Gerog. [[alternative HTML version deleted]]

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",

Hello, I'd like to translate de code below, write in octave, to R. I'm learning to operate R a few time ago, and an example of translation will be useful for me. This code is my lectures in an undergraduate course of statistical computing. echo off; k=0; while (k<>1) n = input('a prime: '); D = 2; r = n - D * floor((n/D)); while((D <= sqrt(n) & (r <>

Hello, R users, I applied segmented regression method contributed by Muggeo and got different slope estimates depending on the initial break points. The results are listed below and I'd like to know what is a reasonable approach handling this kinds of problem. I think applying various initial break points is certainly not a efficient approach. Is there any other methods to deal with segmented

Hi All, I am running quantile regression in a "for loop" starting with 1 variable and adding a variable at a time reaching a maximum of 20 variables. I get the following warning messages after my "for" loop runs. Should I be concerned about these messages? I am building predictive models and am not interested in inference. Warning messages: 1: In