search for: lmrob

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(801.4625,527.2062,545.2250,608.2313,633.8875,575.9500,797.0500,706.418...

...) from most labs. But there remains room for human error or unexpected chemistry which still causes some outliers. MM-estimates from robust regression with variance weights is a suggested approach with promising features. It's a sledgehammer approach to getting a mean value, but .... Now, rlm, lmrob and lmRob all seem to do the job . But one of the things of particular interest to this particular lab community is the standard error on that mean value, and there I am seeing some unexpected anomalies in lmRob. rlm, by contrast, is pretty much rock steady, and lmrob little different to rlm. The...

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 exampl...

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: x&...

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, mean =...

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: http://r.789695.n...

...----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] Problem with anova.lmRob() &quot;robust&quot; package > >I found the question really confusing as well, but see below. > >On Thu, Jul 28, 2011 at 11:42 AM, David Winsemius ><dwinsemius at comcast.net> wrote: >> >> On Jul 28, 2011, at 9:13 AM, m.fenati at libero.it wrote: >> &g...

> 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 <- matrix(rho, d, d); diag(Sigma) <- 1 > x.clean <- mvrnorm(n, rep(0,d), Sigma) Which package are you using for mvrnorm...

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...

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...

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 multivariate normal distribution > where the covariance matrix of the variables is defined by Sigma in my > code, with ones on...

...ger <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 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 &gt...

...t; >> 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 multivariate normal distribution >>> where the covariance matrix of the variab...

...r 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 = wt.method, : 'x' is singular: singular fits are not implemented in rlm As singular.ok = TRUE is default with lmrob from the robustbase package, I tried running the model using lmrob, but I get following error message Too many singular resamples Aborting fast_s() error in lmrob.S(x = x, y = y, control = control) : C function R_lmrob_S() exited prematurely I don't understand what the error messages mean...

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 prediction bands from lmRob? Is there an alternative robust (linear) regression package that offers such a...

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 t...

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version del...

...# X=c(5,3,2,4,7,6,9,3,7,11,1,6,4,5,6,9,4,5,34,2,1,3,12,8,9,7,4,12,19,21) Y=c(25,24,23,19,17,14,13,14,25,13,17,25,14,13,19,25,16,16,20,21,25,19,12,11,9,28,21,10,2,8) {reg1<-lm(Y ~ X) plot(X,Y) abline(reg1, col="black") install.packages("robustbase") library?(robustbase) reg=lmrob(Y ~ X) abline(reg, col="green") install.packages("MASS")? library(MASS) Huber=rlm(Y ~ X) abline(Huber,col="red") Tukey=rlm(Y ~ X,psi=psi.bisquare) abline(Tukey,col="purple") install.packages("quantreg") library(quantreg) L1=rq(Y ~ X,tau=0.5) abl...

...antreg) library(RobPer) X <- c(5,3,2,4,7,6,9,3,7,11,1,6,4,5,6,9,4,5,34,2,1,3,12,8,9,7,4,12,19,21) Y <- c(25,24,23,19,17,14,13,14,25,13,17,25,14,13,19,25,16,16,20,21,25,19,12,11,9,28,21,10,2,8) reg1 <- lm( Y ~ X ) plot( X, Y ) abline( reg1, col = "black" ) reg <- lmrob( Y ~ X ) abline( reg, col = "green" ) Huber <- rlm( Y ~ X ) abline( Huber, col="red" ) Tukey <- rlm( Y ~ X, psi = psi.bisquare ) abline( Tukey, col = "purple" ) L1 <- rq( Y ~ X, tau = 0.5 ) abline( L1, col = "blue" ) fast...

...,3,4,5,6,5,4,5,34,21,12,13,12,8,9,7,43,12,19,21) > Z=c(43,2,1,2,34,4,3,4,5,30,4,5,4,3,4,5,56,6,43,21,34,19,12,11,9,34,21,23,2,19) > reg1<-lm(Z ~ Y) > plot(Y,Z) > abline(reg1, col="black") > > install.packages("robustbase") > library?(robustbase) > reg=lmrob(Z ~ Y, data = Dataset) > abline(reg, col="green") > > install.packages("MASS") > library(MASS) > Huber=rlm(Z ~ Y, data = Dataset) > abline(Huber,col="red") > > Tukey=rlm(Z ~ Y, data = Dataset,psi=psi.bisquare) > abline(Tukey,col="purpl...