similar to: New `party' tools

Dear mailing list, I am using the cforest() method from the party package to train a randomForest with ten input parameters which sometimes contain "NA"s. The predicted variable is a binary decision. Building the tree works fine without warnings or error messages, but when using the predict() statement for validation, I run in an error: forest <- cforest(V31 ~ V1+V2+V3,

Hi All, i've got a problem using the ctree function of the party package. I've searched around a lot but couldn't manage it. I've got an ordered factor as response. As far as i know i have to use scores to be able to use this ordered factor. But if i do so i get a tree which predicts all observations as the first level of my ordered factor. In order to test if i did anything

> ---------- Forwarded message ---------- > Date: Wed, 5 Oct 2011 21:09:41 +0000 > From: Chris <christopher.a.hane at gmail.com> > To: r-help at stat.math.ethz.ch > Subject: Re: [R] Party extract BinaryTree from cforest? > > I found an internal workaround to this to support printing and plot type > simple, > > tt<-party:::prettytree(cf at ensemble[[1]],

Dear R users, there is a new version of the package `lmtest' for testing linear regression models on CRAN. Except for a couple of minor bug fixes, there are essentially these new features: o added Breusch-Godfrey test for serial correlation (thanks to David M. Mitchell who provided the initial version of the code for bgtest) o new data sets: mandible measurements in fetuses,

2nd Workshop "Ensemble Methods" Max Planck Institute Tuebingen, Germany March 4-5, 2005 The second workshop on ensemble methods will take place at the Max Planck Institute Tuebingen (Germany) on March 4-5, 2005. This workshop is jointly organised by the German working groups "Computational Statistics" (IBS-DR) and "Statistical Computing" (GMDS) as well as the

Dear useRs, a new package for tree-structured regression is available on CRAN. This package implements a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented. The methodology is applicable to all kinds of regression problems,

Dear useRs, a new package for tree-structured regression is available on CRAN. This package implements a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented. The methodology is applicable to all kinds of regression problems,

Hi, the following example aiming at a class containing a logical slot segfaults under R-1.9.0 when `gctorture(on = TRUE)' is used: Code code (dummy.c): #include <Rdefines.h> SEXP foo() { SEXP ans; PROTECT(ans = NEW_OBJECT(MAKE_CLASS("test"))); SET_SLOT(ans, install("lgl"), allocVector(LGLSXP, 1)); LOGICAL(GET_SLOT(ans,

I don't use SPSS but following through on your detective work can provide the likely answer. First note that both date numbers are evenly divisible by the number of seconds in a day, i.e. 24*60*60. This suggests that these numbers are seconds since some origin. Since we know "2003/02/11" corresponds to 13264300800 we deduce that the origin must be spss.orig <-

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

Dear R users, I've just released a new version of randomForest (available on CRAN now). This version contained quite a number of new features and bug fixes, compared to version prior to 4.0-x (and few more since 4.0-1). For those not familiar with randomForest, it's an ensemble classifier/regression tool. Please see http://www.math.usu.edu/~adele/forests/ for more detailed information,

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

Hi, Could anyone help me understand how the mincriterion threshold works in ctree and cforest of the party package? I've seen examples which state that to satisfy the p < 0.05 condition before splitting I should use mincriterion = 0.95 while the documentation suggests I should use mincriterion = qnorm(0.95) which would obviously feed the function a different value. Thanks in advance,

Dear List, I'm trying to find a way to extract the individual conditional inference trees from cforest ( a modelling function in the party package) in a manner analogous to getTree in randomForest and I'm struggling. I can see that the information is held within the ensemble list, but haven't been able to work out how this sequence of nested lists is structured or if any of the items

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 =

I just realized that in the call to `csimint' the argument `asympt=TRUE' is missing since we need to compute the confidence intervals for a glm based on the normal approximation. Torsten --------------------------------------------------------------------- library(multcomp) set.seed(290875) # a factor at three levels group <- factor(c(rep(1,10), rep(2, 10), rep(3,10))) # Williams

Hi Manuel, find our SoC proposal below. Best wishes, Torsten & Achim _______________________________________________________________________ Party On! New Recursive Partytioning Tools. Mentor: Torsten Hothorn & Achim Zeileis Short Description: The aim of the project is the implementation of recursive partitioning methods ("trees") which aren't available in R at the

Hello, I need to classify (i.e., export a vector with terminal node id's) new cases using a ctree (party package) model based on different cases (learning sample). I tried the where command with the following syntax: > where(tree, newdata=data2) expecting to get terminal nodes of data2 cases based on rules of tree model (data1 as learning sample). However it returned the following error

Hi, Thank you for the tip. I tried to re-intall R from Debian "stable", in which R's version is 1.4.0. And the installation of "mvtnorm" works. I then re-installed R yet again from Debian "unstable" (woody), in which R's version is 1.5.1. The installation of "mvtnorm" fails again with the same error message. Another package that failed with the