similar to: Robust multivariate regression with rlm

Hello Group, I could use an advice on how SEXP handles work. My aim is to implement a system where I initially set a few global variables that are used for communication between C and R code. Then I do some work with R code and periodically call a function of my own that will update the system state. Such a design is useful for many purposes (for GUIs to name one). I am not entirely sure that R

Dear Group, I'm trying to build a set of new graphic device drivers. I use the devNull example a a beginning point: $ R CMD SHLIB devNull.c gcc -shared -L/usr/local/lib -o devNull.so devNull.o (everything works OK) $ R CMD SHLIB devNull.cpp g++ -shared -L/usr/local/lib -o devNull.so devNull.o (everything works OK) The difficulties start when trying to compile manually. I compile the

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.

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

Hi: Could anyone help me to clarify this: are the weights normalized inside lm function (package:stats) before applied to the error term? For example: >lm (cost ~ material, weights=quatity, data=receipt) will lm normalize quatity such that sum(quatity) = 1? I traced to lm.wfit and then the weights get transferred into a precompiled FORTRAN module so I can't figure out. Thanks!

Hello all, I'm using R2.7.0 (on Windows 2000) and I'm trying do run a robust regression on following model structure: model = "Y ~ x1*x2 / (x3 + x4 + x5 +x6)" where x1 and x2 are both factors (either 1 or 0) and x3.....x6 are numeric. The error code I get when running rlm(as.formula(model), data=daymean) is: error in rlm.default(x, y, weights, method = method, wt.method =

Hi all, I am reading the MASS book but it doesn't give examples about the diagnosis and model checking for rlm... My data is highly non-Gaussian so I am using rlm instead of lm. My questions are: 0. Are goodness-of-fit and model-checking using rlm completely the same as usual regression? 1. Please give me some pointers about how to do goodness-of-fit and residual diagnosis for

Hello, I have a simple xyplot with rlm lines. I would like to add the a and b coefficients (y=ax+b) of the rlm calculation in each panel. I know I can do it 'outside' the xyplot command but I would like to do all at the same time. I found some posts with the same question, but no answer. Is it impossible ? Thanks in advance for your help. Ptit Bleu. x11(15,12) xyplot(df1$col2 ~

How do I add to a trellis plot the best fit line from a robust fit? I can use panel.lm to add a least squares fit, but there is no panel.rlm function. -- Alan S Barnett <asb at mail.nih.gov> NIMH/CBDB

Hi all, I am also confused about the manual: a. The input arguments: wt.method are the weights case weights (giving the relative importance of case, so a weight of 2 means there are two of these) or the inverse of the variances, so a weight of two means this error is half as variable? w (optional) initial down-weighting for each case. init (optional) initial values for the

Full_Name: Alexander Blocker Version: 2.7.1 OS: Ubuntu 8.04 / Windows XP Submission from: (NULL) (76.119.235.225) When running lm(modeleq, weights=wt, data=dataset) with small weights (<1e-10), I have encountered an odd phenomenon with fitted values. Due to numerical precision issues, the fitted values and residuals returned by lm.wfit (from its .Fortran call to dqrls) can differ greatly from

R 2.0.1 Linux I am using rlm() to fit a model, e.g. fit1<-rlm(y~x). My model is more complex than the one shown. When I enter summary(fit1) I get estimates for the model's coefficients along with their SEs, and t values, but no p values. The p value column is blank. Similarly, when I enter anova(fit1) I get DF, Sum Sq, Mean Sq, but the column for F value and Pr(>F) are blank. Any

I am absolutely new to R and I am aware of only a few basic command lines. I was running a robust regression in R, using the following command line library (MASS) rfmodel2 <- rlm (TotalEmployment_2005 ~ ALABAMA + MISSISSIPPI + LOUISIANA + TotalEmployment_2000 + PCWhitePop_2005 + UnemploymentRate_2005 + PCUrbanPop2000 + PCPeopleWithACollegeDegree_2000 +

How do we get 2-tailed p-values for the rlm summary? I'm using the following: > fit <- rlm(oatRT ~ oatoacData$erp, psi=psi.bisquare, maxit=100, na.action='na.omit') > fitsum <- summary(fit, cor=F) > print(fitsum) Call: rlm(formula = oatRT ~ oatoacData$erp, psi = psi.bisquare, maxit = 100, na.action = "na.omit") Residuals: Min 1Q Median

I keep coming back to this problem of singular fits in rlm (MASS library), but cannot figure out a good solution. I am fitting a linear model with a factor variable, like lm( Y ~ factorVar) and this works fine. lm knows to construct the contrast matrix the way I would expect, which puts the first factor as the baseline level. But when I try rlm( Y ~ factorVar) I get the message "'x'

Dear R-devel, There seems to be a discrepancy in the order in which lm and rlm evaluate their arguments. This causes rlm to sometimes produce an error where lm is just fine. Here is a little script that illustrate the issue: > library(MASS) > ## create data > n <- 100 > dat <- data.frame(x=rep(c(-1,0,1), n), y=rnorm(3*n)) > > ## call lm, works fine > summary(lm(y ~

Hi, I am trying to build a series of rlm models. I have my data frame and the models will be built using various coulmns of the data frame. Thus a series of models would be m1 <- rlm(V1 ~ V2 + V3 + V4, data) m2 <- rlm(V1 ~ V2 + V5 + V7, data) m3 <- rlm(V1 ~ V2 + V8 + V9, data) I would like to automate this. Is it possible to use a string in place of the formula? I tried doing: fmla

Dear community, I have fitted a model using comands above, (rlm, lmrob or lmRob). I don't have new data to validate de models obtained. I was wondering if exists something similar to CVlm in robust regression. In case there isn't, any suggestion for validation would be appreciated. Thanks, user at host.com -- View this message in context:

Hello, I'm trying to do a regression analysis (multiple linear regression) and have to deal with a slight heteroscedascitiy in my data. I've read somewhere that it's possible to use the rlm (robust regression) out of the MASS package in such cases. Is it possible to apply the lm.beta method (from package QuantPsyc) to the returned rlm object and/or is there a way to calculate

I am needing some expertise with regard to the S-Plus command lmRobMM and its R counterpart rlm(formula,data,method="MM") I have used lmRobMM(formula,data) in S-Plus on the Stackloss data and obtained for my residuals 6.217777 1.150717 6.427946 8.174019 -0.6713005 -1.248641 -0.4236203 0.5763797 -1.057899 0.3593823 11 12 13 14 15 16