similar to: How to get around heteroscedasticity with non-linear least squares in R?

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

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

Does anyone can provide a code to implement least median squares regression in R (not using the lqs function or calling C functions)? Reason: teaching/learning purposes Thanks PM

The question is general for all functions, but here is a specific example - # For the logistic regression of the following correlated variables: C <- c(457, 1371, 4113, 12339, 37017, 111051, 333153, 999459) E <- c(0.003858377, 0.014334578, 0.014092836, 0.737950754, 0.996371828, 0.997482379, 1.005569257, 0.994382856) # The nls function: A = nls(E~(Em*C^p)/(C50^p + C^p),

Hello all I've just spent a few joyless hours trying to get EBImage to install in R. I'm running Ubuntu (Hardy Heron), fully updated (including R and Imagemagick). EBImage installation seems to work, but when I 'library(EBImage)' I get the following: - - - - Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared library '/home/qilin/R/i486-pc-linux-

Hi I'm running the latest R on a presumably up to date Linux server. 'Doing something silly I'm sure, but can't see why my saved PLMset objects come out all wrong. To use an example: Setting up an example PLMset (I have the same problem no matter what example I use) > library(affyPLM) > data(Dilution) # affybatch object > Dilution = updateObject(Dilution)

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 could only find some discussion on this wrt lattice graphics (which I'm not using). Apologies if I'm missing something obvious. I'd like to produce 3 rug plots under a kernel density plot for a population. The population is subdivided into 3 subpopulations, which I'd like the rug plots to highlight. Naturally, when I do 3 rug plots, they all plot over each other.

Hi: I have data from an assay in the form of two vectors, one is response and the other is a predictor. When I attempt to fit a 5 parameter logistic model with "nls", I get converged parameter estimates. I also get the same answers with "gnls" without specifying the "weights" argument. However, when I attempt to use the "gnls" function and try to

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",

Hi. Day three and I’m still struggling with this. Any advice to overcome the final hurdle will be enormously appreciated. I now have all the right Java applications etc. in their right places and have managed to get rid of most errors but still get this: — Making package SJava — Building JNI header files... adding build stamp to DESCRIPTION running src/Makefile.win (cd .. ;

Hello all Simple question for the gurus. I'm trying to interactively label curves on a single plot. The labcurve() function from Hmisc seems like the way to do this (?). I just can't seem to get it to work. Toy example: > x <- 1:10 > y1 <- x^2 > y2 <- 2*x > plot(x,y1) > lines(x,y2) > labcurve(labels=c("curve1", "curve2"),

Dear list, (this is a follow up from a previous query) Why does R CMD INSTALL write most of its messages to stderr? If it wrote to stdout, then we could capture its output within an R session when calling sink("stdout.txt", type="output") install.packages("MASS", type="source") sink() As it stands, the stderr messages can't be captured via

Hi there, I'm trying to fit a growth curve to some data and need to use a weighted least squares estimator to account for heteroscedasticity in the data. A weights argument is available in nls that would appear to be appropriate for this purpose, but it is listed as 'not yet implemented'. Is there another package which could implement this procedure? Regards, Robert Brown

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> 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 lmer users, The experiment includes 15 groups of (3 males and 1 female). The female is characterized by its quality Q1 and Q2. Each male of a group is characterized by the number of MatingAttempts (with Poisson distribution). I want to examine if male mating attempts depend on female quality. I can see from graphic exploration that the within-group heterogeneity of male attempts increases

Dear list, I have encountered a special case for searching a linear regression where I'm not satisfied with the results obtained using the traditional least squares method (sometimes called OLS) for estimating/optimizing the residues to the regression line (see code below). Basically, a group of my x-y data are a bit off the diagonal line (in my case the diagonal represents the ideal or

Dear R users, I 'm working on a dataset consisting of 4 different dataframes with tree, leaf, fruit and seed measurements made on 300 trees, coming from 10 provenances (30 trees per provenance, 10 leaves/fruits/seeds per tree). Provenances are fixed effects (they were not randomly chosen), but trees within provenances and leaves/fruits/seeds within trees were randomly assigned. I wanted to

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",