similar to: rlm and lmrob error messages

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:

This message also reports wrong estimates produced by lmRob.fit.compute() for nested factors when using the correct contrast matrix. And in these respects, I have found that S-Plus behaves the same way as R. Using the three available contrast types (sum, treatment, helmert) with lm() or lm.fit(), but just contr.sum with rlm() and lmRob(), and small examples, I generated contrast matrices for

I am looking at MM-estimates for some interlab comparison work. The usual situation in this particular context is a modest number of results from very expensive methods with abnormally well-characterised performance, so for once we have good "variance" estimates (which can differ substantially for good reason) from most labs. But there remains room for human error or unexpected chemistry

Dear R users, I'd like to known your opinion about a problem with anova.lmRob() of "Robust" package that occurs when I run a lmRob() regression on my dataset. I check my univariate model by single object anova as anova(lmRob(y~x)). If I compare my model with the null model (y~1), I must obtain the same results, but not for my data. Is it possible? My example:

I'm sorry, maybe the question was bad posed. Ista has well described my problem. Thanks Massimo >----Messaggio originale---- >Da: izahn at psych.rochester.edu >Data: 28/07/2011 17.52 >A: "David Winsemius"<dwinsemius at comcast.net> >Cc: "m.fenati at libero.it"<m.fenati at libero.it>, <r-help at r-project.org> >Ogg: Re: [R]

Hi all and thanks for your time in advance, I can't figure out why summary.lmrob complains when lmrob is used on a zooreg object. If the zooreg object is converted to vector before calling lmrob, no problems appear. Let me clarify this with an example: >library(robustbase) >library(zoo) >dad<-c(801.4625,527.2062,545.2250,608.2313,633.8875,575.9500,797.0500,706.4188,

Hi, I am fairly new to R and have encountered an issue with the lmRob function that I have been unable to resolve. I am trying to run a robust regression using the lmRob function which runs successfully, but the results are rather strange. I'm not sure it's important, but my model has 3 dichotomous categorical variables and 2 continuous variables in it. When I look at a summary of my

Hi, I am trying to fit a ANCOVA model using lmRob. The P-values of the variables in the model differ hugely between the summary() function and the anova() function (from >0.8 in the summary to <0.001in the anova for the same variable). I understand that with an ANCOVA the order in which the variables are added to the model matters and that this influences the P-value, but can this make such

lm's "predict" function offers an "interval" parameter to choose between 'confidence' and 'prediction' bands. In the package "robust" and for "lmRob", there is also a "predict" but it lacks such a parameter, and the documented "type" parameter has only "response" offerred. Is there some way of obtaining

> On Mar 3, 2018, at 3:04 PM, Christien Kerbert <christienkerbert at gmail.com> wrote: > > Dear list members, > > I want to perform an MM-regression. This seems an easy task using the > function lmrob(), however, this function provides me with NA coefficients. > My data generating process is as follows: > > rho <- 0.15 # low interdependency > Sigma <-

Hard to help you if you don't provide a reproducible example. On Sun, Mar 4, 2018 at 1:05 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > d is the number of observed variables (d = 3 in this example). n is the > number of observations. > > 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > >> What is 'd'? What is

Dear list members, I want to perform an MM-regression. This seems an easy task using the function lmrob(), however, this function provides me with NA coefficients. My data generating process is as follows: rho <- 0.15 # low interdependency Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 x.clean <- mvrnorm(n, rep(0,d), Sigma) beta <- c(1.0, 2.0, 3.0, 4.0) error <- rnorm(n = n,

d is the number of observed variables (d = 3 in this example). n is the number of observations. 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > What is 'd'? What is 'n'? > > > On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < > christienkerbert at gmail.com> wrote: > >> Thanks for your reply. >> >> I use

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

Thanks for your reply. I use mvrnorm from the *MASS* package and lmrob from the *robustbase* package. To further explain my data generating process, the idea is as follows. The explanatory variables are generated my a multivariate normal distribution where the covariance matrix of the variables is defined by Sigma in my code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y

What is 'd'? What is 'n'? On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > Thanks for your reply. > > I use mvrnorm from the *MASS* package and lmrob from the *robustbase* > package. > > To further explain my data generating process, the idea is as follows. The > explanatory variables are generated my a

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

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