similar to: nonlinear regression with M estimation

Dear R listers, I'm trying use Huber's m-estimator on a dataset, which works fine so far. In the next step I would like to assign a (frequency) weight to the observations. It seemed straight forward to me to replicate the rows according to their count variable. Unfortunately, a solution provided by jim holtman on Wed 19 Oct 2005 in this list doesn't work for me: > y

Hello, I?m with a problem in using nonlinear quantile regression, the function nlrq. I want to do a quantile regression o nonlinear function in the form a*log(x)-b, the coefficients ?a? and ?b? is my objective. I try to use the command: funx <- function(x,a,b){ res <- a*log(x)-b res } Dat.nlrq <- nlrq(y ~ funx(x, a, b), data=Dat, tau=0.25, trace=TRUE) But a can?t solve de problem,

Hello. I am trying to fit an asymptotic relationship (nonlinear) to some ecological data, and am having problems. I am interested in the upper bound on the data (i.e. if there is an upper limit to 'y' across a range of 'x'). As such, I am using the nonlinear quantile regression package (nlrq) to fit a michaelis mention type model. The errors I get (which are dependant on

I have a loop that increases the size of an object after each iteration. When the Windows Task Manager shows "Mem Usage" about 1.8GB, the Rgui.exe process no longer responds. I use: "C:\Program Files\R\rw1080\bin\Rgui.exe" --max-mem-size=4000M --min-vsize=10M --max-vsize=3000M --min-nsize=500k --max-nsize=1000M I have a dual Xeon 2.8GHz processor box with 4GB of memory and

How to get robust M-estimators of multivariate scatter using Huber's psi? Which package/function should I look into? Ideally, I hope I can self-define thresholds of Huber's psi function. Thanks a lot!!! -- View this message in context: http://www.nabble.com/How-to-get-robust-M-estimator-of-multivariate-scatter-using-Huber%27s-psi--tp20585755p20585755.html Sent from the R help mailing

Hi, I'm trying to use Robust non-linear regression to fit dose response curves. Maybe I didnt look good enough, but I dind't find robust methods for NON linear regression implemented in R. A method that looked good to me but is unfortunately not (yet) implemented in R is described in http://www.graphpad.com/articles/RobustNonlinearRegression_files/frame.htm

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 <-

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

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

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

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

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)

Hello, I would like to make a nonlinar fit (exactly the exponencial fit) to the data. But my data set is not ideal at all, so any robust method (such as LTS) would be bettre then LS. Could you advice me, please, if there is any R package or R function which provides the nonlinear robust regression? Thank you Eva Gelnarova

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 all, I sent an email on Friday asking about nlrq {quantreg}, but I haven't received any answer. I need to estimate the quantile regression estimators of a model as: y = exp(b0+x'b1+u). The model is nonlinear in parameters, although I can linearise it by using log.When I write: fitnl <- nlrq(y ~ exp(x), tau=0.5) I have the following error: Error in match.call(func, call = cll) :

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 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",

Please, I have a problem with nonlinear quantile regression. My data shows a large variability and the quantile regression seemed perfect to relate two given variables. I got to run the linear quantile regression analysis and to build the graph in the R (with quantreg package). However, the up part of my data dispersion seems a positive exponential curve, while the down part seems a negative

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),