similar to: Huber location estimate

Hi, I've been using the Matlab robustfit function for linear regressions where I suspect some data points are outliers. Is there an equivalent function in R? Take care, Darren PS, This is the Matlab help on robustfit: >> help robustfit ROBUSTFIT Robust linear regression B = ROBUSTFIT(X,Y) returns the vector B of regression coefficients, obtained by performing robust

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on

Dear R users, I use the glm() function to fit a generalized linear model with gamma distribution function and log link. I have read in the help page that the default method used by R is "glm.fit" (iteratively reweighted least squares, IWLS). Is it possible to use maximum likelihood method? Thanks Silvia Narduzzi Dipartimento di Epidemiologia ASL RM E Via di S. Costanza, 53 00198

Hello, first of all: I'm new to R and have only used SPSS befor this (which can't do this at all...). I'm trying to output some regression results to latex. The regressions are normal OLS and I'm trying to output the results with standardized regression coefficients and t-statistics based on "Huber-White sandwich estimator for variance". The final result should be

I've used optim to analyze some data I have with good results, but need to correct the var-cov matrix for possible effects of clustering of observations (respondents) in small groups (non-independence). Is there any function to adjust the matrix? I heard some time ago that the vcovHC function would have a cluster capability added to it, but I don't see that in my fairly recent version.

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

Dear Madam/Sir This may be quite a long shot... By way of intro, I am a masters student in actuarial science at the University of Cape Town, and I am doing a project in R on some healthcare cost data. During my coding in R I encountered an error message, which I then googled, but I am still unable to resolve the issue. I would like to please ask if and how it is possible to resolve the problem

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

Full_Name: Stephen Weigand Version: 1.9.0 OS: Mac OS X 10.3.4 Submission from: (NULL) (68.115.89.235) When running an iteratively reweighted least squares program R crashes and the following is written to the console.app (when using R GUI) or to stdout (when using R from the command line): Parameter 5 to routine DSTEIN was incorrect Mac OS BLAS parameter error in DSTEIN, parameter #0,

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

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

Hello R-listers! My first post to the list is a very simple one for those who use the software continuosly. I am trying to understand the fixed-x resampling and random-x-resampling method proposed by Fox about Bootstrapping. The doubt that I have is on the side of the model run in one of the functions expressed for fixed-x resampling. What I don't understand is: X=model.matrix, and the -1

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)

Hi all, I need to test the significnce of difference between slopes of two regression lines and regression line with theoretical line. I try to use Slope.test function from emu package, but an error occured... library(emu) d1<-data.frame(P1=c(1,2,3,5,7,8,9,13,14,15), P2=c(1,2,5,8,11,13,15,15,18,24), R=c(2,7,8,9,16,21,27,31,33,36)) # First data set m1<-lm(R~P1+P2+P1*P2,data=d1) # Regr.

I have written a parser and "engine" for a simple scripting language - before I knew about LLVM. I am thinking about whether it would be possible (and a good idea) to rewrite it to use the LLVM. A main issue that I am not sure if LLVM can provide is the following: The script (which is called from C++ code) gets passed several C structs that have been populated by the

I should hope that there is trouble, since "type III" is an undefined concept for a Cox model. Since SAS Inc fostered the cult of type III they have recently added it as an option for phreg, but I am not able to find any hints in the phreg documentation of what exactly they are doing when you invoke it. If you can unearth this information, then I will be happy to tell you whether

Hi All, Could any one tells me if R or S has the capacity to fit nonlinear regression with Huber's M estimation? Any suggestion is appreciated. I was aware of 'rlm' in MASS library for robust linear regression and 'nls' for nonlinear least squares regression, but did not seem to be able to find robust non-linear regression function. Thanks and regards, Ray Liu

Hei Group, I want to compare the relative importance of predictors in a multiple linear regression y~a+bx1+cx2... However, bptest indicates heteroskedasticity of my model. I therefore perform a robust regression (rlm), in combination with bootstrapping (as outlined in J. Fox, Bootstrapping Regression Models). Now I want to compare the relative importance of my predictors. Can I rely on the

The Bioconductor core group would like to announce the 1.3 release of the Bioconductor software. There are many new packages as well as several major upgrades and fixes in older packages, and users are encouraged to check them out. Release 1.3 is intended to be operated with R version 1.8.X, which can be obtained at CRAN (http://cran.r-project.org/) -- WHAT FEATURES DOES THIS RELEASE PROVIDE?

The Bioconductor core group would like to announce the 1.3 release of the Bioconductor software. There are many new packages as well as several major upgrades and fixes in older packages, and users are encouraged to check them out. Release 1.3 is intended to be operated with R version 1.8.X, which can be obtained at CRAN (http://cran.r-project.org/) -- WHAT FEATURES DOES THIS RELEASE PROVIDE?