similar to: R: Re: Problem with anova.lmRob() "robust" package

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 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 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:

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

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

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

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 =

> 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 <-

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

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

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

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

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

Dear Sir I am getting errors in using following R code for L-moment ratio diagram Help in this regard > "plotlmrdia" <- + function(lmr, + nopoints=FALSE, + nolines=FALSE, + nolimits=FALSE, + nogev=FALSE, + noglo=FALSE, + nogpa=FALSE, + nope3=FALSE, + nogno=FALSE, + noexp=FALSE, + nonor=FALSE, +

Dear All, When I plot the values and linear regression line for one data set, it is fine. But for another one I see zigzags, when I plot the confidence interval >cd Depth CHAOsep12RNA 9,94 804 25,06 1476,833333 40,04 1540,561404 50,11 1575,166667 52,46 349,222222 54,92 1941,5 57,29 1053,507042 60,11 1535,1 70,04 2244,963303 79,97 1954,507042 100,31 2679,140625 >

Dear colleagues, I?m a new R-help user. I?ve read the advertisements about the good manners and I hope to propose a good question. I?m using R to build an epidemiological SEIR model based on ODEs. The odesolve package is very useful to solve deterministic ODE systems but I?d like to perform a stochastic simulation based on Markov chain Montecarlo methods. I don?t know which packages could be

I am using R 2.1.1 under Mac OS 10.3.9. Two related problems (see notes 1. and 2. below) are illustrated by results of the following: y <- rnorm(10) x <- z <- 1:10 is.na(x[9]) <- TRUE lm0 <- lm(y ~ 1) lm1 <- lm(y ~ 1, weights = rep(1, 10)) add1(lm0, scope = ~ x) ## works ok add1(lm1, scope = ~ x) ## error lm2 <- lm(y ~ 1, offset = 1:10) add1(lm0, scope = ~ z) ##

David, Thanks. The reason add1.lm (and drop1.lm) do not support offsets is that lm did not when they were written, and the person who added offsets to lm did not change them. (I do wish they had not added an offset arg and just used the formula as in S's glm.) That is easy to add. For the other point, some care is needed if 'x' is supplied and the upper scope reduces the number