similar to: How do we get two-tailed p-values for rlm?

Hi, I want to write the summary from a regression. I am doing this because I do not see a way of get the std error, tvalues from the coefficients diagnostic. n$coef does not give this only get the intercept and slope. I tried to use write and write.table and got error in both cases. I jumped thru the hoops below to no avail. Also note, this is in windows. I used to use unix, and do not

Dear friends, I have written the following lines in R console wich already exist in pdf file systemfit: data( "GrunfeldGreene" ) library( "plm" ) GGPanel <- plm.data( GrunfeldGreene, c( "firm", "year" ) ) greeneSur <- systemfit( invest ~ value + capital, method = "SUR", + data = GGPanel ) greenSur I have obtained the following incomplete

Hi , I know this question has been asked twice in the past but to my knowldege, it still hasn't been solved. I am doing a zero inflated binomial model using the VGAM package, I need to obtain p values for my Tvalues in the vglm output. code is as follows > mod2=vglm(dmat~Season+Diel+Tidal.phase+Tidal.cycle,zibinomial, data=mp1) > summary(mod2) Call: vglm(formula = dmat ~ Season +

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

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

Hi, How can I define a new psi function in the rlm comand? In particular I would like to implement the case for \rho(u)=0.5*(u^2) and \psi(u)=u in order to assume normally distributed errors. Any help? Thanks -- ======================================================== Angelo Secchi PGP Key ID:EA280337

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

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

Hello, I need to perform a robust regression on data which contains errors in BOTH observations and regressors. Right now I am using rlm from the MASS package with 'method="MM"' and get visually very nice results. MASS is quite clear, however, that the described methodologies are only applicable to observation-error only data (p. 157, 4th Ed.). So here's the questions now:

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 =

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 +

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

I have many "sample" datasets (e.g. sample 5, sample 6, etc), each identified by a number as a suffix. These datasets are saved as individual R objects on my hard drive. (e.g."Wind.5.r" . "Wind.6.r","Solar.5.r","Solar.6.r") For example purposes, I have written code that creates similar data files using the "airquality" dataset. (see

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 ~

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

Dear Group, I am having trouble with using rlm on multivariate data sets. When I call rlm I get Error in lm.wfit(x, y, w, method = "qr") : incompatible dimensions lm on the same data sets seem to work well (see code example). Am I doing something wrong? I have already browsed through the forums and google but could not find any related discussions. I use Windows XP and R

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'

Hi, I would appreciate of someone could explain how the residual standard error is computed for rlm models (MASS package). Usually, one would expect to get the residual standard error by > sqrt(sum((y-fitted(fm))^2)/(n-2)) where y is the response, fm a linear model with an intercept and slope for x and n the number of observations. This does not seem to work for rlm models and I am wondering

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 ~