similar to: methods(class = class(<obj>)) - improve for |cl.| > 1 ?

I think this would be a good change. I think most users use the 'methods(class = <...>)' function to answer the question, "what methods can I call on objects with these classes?", and in that context I think it would be sensible for the function to accept more than one class. Kevin On Wed, Oct 17, 2018 at 7:15 AM Martin Maechler <maechler at stat.math.ethz.ch>

Full_Name: Finn Knudsen Version: 1.8.0 OS: windows 2000 Submission from: (NULL) (194.192.22.33) The problem seems to happen when running the GLM. When both multiplicative effects and an offset is present. I experienced this problem on my own dataset when using af Poisson familiy with log link function but the behaviour can be reproduced with the following code. I do not know if it is a bug, but

A querent on StackOverflow asked about the with() function http://stackoverflow.com/questions/42283479/why-when-to-use-with-function#42283479 and asked about the example in ?with library(MASS) with(anorexia, { anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian) summary(anorex.1) }) which saves little or no typing

Hello, I'm a starting user of R. I have installed R 1.9.1 and winedt 5.4 If I run the example from written with winedt. The summary command does not produce any output. It does when I repeat the command manualy in R. Can someone explain me what can be the problem? library(MASS) data(anorexia) anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family =

Dear Listers, I got this message when compiling a package: * creating pgirmess-manual.tex ... OK * checking pgirmess-manual.text ... ERROR LaTex errors when creating DVI version. This typically indicates Rd problems. The message is quite explicit but I struggled a lot before understanding that the trouble comes from a single file "selMod.rd" among 44 topics. Even though I have

Dear all, I am fitting a GLM similar to library(MASS) anorex.1 <- glm(Treat~Postwt+Prewt,family = binomial, data = anorexia) I have found two ways of computing the p-value of the fitted model: pval1 <- 1-pchisq(anorex.1$deviance,anorex.1$df.residual) pval2 <- 1-pchisq(anorex.1$null.deviance - anorex.1$deviance, anorex.1$df.null - anorex.1$df.residual) pval2 is

Dear all, After running a glm, I use the summary ( ) function to extract its coefficients and related statistics for further use. Unfortunately, the screen only displays a small (last) part of the results. I tried to overcome the problem by creating/saving an object "coef" for coefficients of the model and export/save it e.g. as a cvs document. While I succed with this operatiion, I do

Dear listers, I was used to package pgirmess under Windows with everything OK, but, for the first time, I had a trial this afternoon on Ubuntu 7.10 gutsy (I have a double boot computer and work more and more under unix) and R 2.7.0. Everything went OK except this: sudo R CMD check pgirmess ..... * checking examples ... ERROR Running examples in 'pgirmess-Ex.R' failed. The error most

Hi, I looking for a method that use F or Chisq test instead of AIC in a stepwise modelo selection. I try the grasp package using the grasp.step.anova, but It dont work. > library(grasp) Carregando pacotes exigidos: gam Carregando pacotes exigidos: splines Carregando pacotes exigidos: mda Carregando pacotes exigidos: class > data(anorexia,package="MASS") > > m1 <-

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating

?.S3methods f <- function()(2) > length(.S3methods(f)) [1] 0 > length(.S3methods(print)) [1] 206 There may be better ways, but this is what came to my mind. -- Bert On Wed, Mar 8, 2023 at 11:09?AM Leonard Mada via R-help < r-help at r-project.org> wrote: > Dear R-Users, > > I want to change the args() function to return by default the arguments > of the default

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

Buenos días: Otra opción es escribir directamente el nombre de la función en la consola de R: > randomForest function (x, ...) UseMethod("randomForest") En este caso, la función randomForest() llama a UseMethod() para seleccionar el método adecuado. Podemos ver los métodos para randomForest con la función methods(): > methods(randomForest) [1] randomForest.default*

Hello I'd like to try out some functions in the package randomForest. Therefore, I did install this package. However, it is not possible to load the library, although I have R-Version 2.1.1 (i.e. later than 2.0.0). The commands I used and the Answers/Error from R is as follows: > install.packages("C://Programme//R//rw2011//library//randomForest_4.5-16.zip",

Hello, I've tried to install randomForest on a Ubuntu 8.04 Hardy Heron system. I've repeatedly rec'd the error: > install.packages("randomForest", dependencies = TRUE) ERROR: compiliation failed for package 'randomForest' ** Removing '/home/admuser/R/i486-pc-linux-gnu-library/2.6/randomForest' The downloaded packages are in

Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have

What is the function call interface for predict in the package party for cforest? I am looking at the documentation (the vignette) and ?cforest and from the examples I see that one can call the function predict on a cforest classifier. The method predict seems to be a method of the class RandomForest objects of which are returned by cforest. --------------------------- > cf.model =

Hi, I have been using some code for multivariate random forests. The output from this code is a list object with all the same values as from randomForest, but the model object is, of course, not of the class randomForest. So, I was hoping to modify the code for predict.randomForest to work for predicting the multivariate model to new data. This is my first attempt at modifying code from a

Dear all, Does anybody can explain this: different results got when all the same parameters are used in the errorest() in library ipred, as the following? errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv", est.para=control.errorest(k=3), mtry=2)$err [1] 0.03333333 > errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",

I am new to R and I'd like to use the randomForest package for my thesis (identifying important variables for more detailed analysis with other software). I have found extremely well written and helpful information on the usage of R. Unfortunately it seems to be very difficult to find similarly detailed tutorials for randomForest, and I just can't get it work with the information on