similar to: cforest() and missing values (party package)

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,

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 =

> ---------- 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]],

All -- I have been trying to work with the 'Party' package using R v2.15.1 and have cobbled together a (somewhat) functioning code from examples on the web. I need to run a series of unbiased, conditional, cForest tests on several subsets of data which I have made into a loop. The results ideally will be saved to an output file in matrix form. The two questions regarding the script in

Hi, This is my code (my data is attached): library(languageR) library(rms) library(party) OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls

> ---------- Forwarded message ---------- > Date: Mon, 18 Jul 2011 10:17:00 -0700 (PDT) > From: KHOFF <kuphoff at gmail.com> > To: r-help at r-project.org > Subject: [R] cforest - keep.forest = false option? > > Hi, > > I'm very new to R. I am most interested in the variable importance > measures > that result from randomForest, but many of my predictors

Dear useRs, Version 0.8-1 of the `party' package will appear on CRAN and its mirrors in due course. This version implements two new tools: o `mob', an object-oriented implementation of a recently suggested algorithm for model-based recursive partitioning (Zeileis, Hothorn, Hornik, 2005) has been added. It works out of the box for partitioning (generalized) linear

Hi, I'm very new to R. I am most interested in the variable importance measures that result from randomForest, but many of my predictors are highly correlated. My first question is: 1. do highly correlated variables render variable importance measures in randomForest invalid? and 2. I know that cforest is robust to highly correlated variables, however, I do not have enough space on my

Greetings, I am using cforest to predict age of fishes using several variables; as it is rather difficult to age fishes I would like to show that a small subset of fish (training dataset) can be aged, then using RF analysis, age can accurately be predicted to the remaining individuals not in the subsample. In cforest_unbiased the samples are drawn without replacement and so it creates a default

Hi, I am facing a hierarchically structured dataset, and I am not sure of the right way to analyses it with cforest, if their is one. - - BACKGROUND & PROBLEM We are analyzing the behavior of some social birds facing different temperature conditions. The behaviors of the birds were recorder during many sessions of 2 hours. Conditional RF (cforest) are quite useful for this analysis

Dear all: I¡¯m trying to get unbiased feature importance of my data via package ¡°party¡±, which contains 1-5 integer value, and a few numeric values attributes. The class label is 1-5 integer value as well. In total I have 20 features with 1100 observations. I checked the type my data in R using class(my_data_cell), no factor has been observed. I received a commond error like others did

Hi all, I'm having great trouble working with the Cforest (from the party package) and Random forest functions. Large data set seem to create very large model objects which means I cannot work with the number of observations I need to, despite running on a large 8GB 64-bit box. I would like the object to only hold the trees themselves as I intend to export them out of R. Is there anyway,

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

Greetings! I'm trying to use function varimpAUC in the party package (party_1.0-3 released September 26th of this year). Unfortunately, I get the following error message: > data.cforest.varimp <- varimpAUC(data.cforest, conditional = TRUE) Error: could not find function "varimpAUC" Was this function NOT included in the Windows binary I downloaded and installed? Could someone

> > I would like to build a forest of regression trees to see how well some > covariates predict a response variable and to examine the importance of > the > covariates. I have a small number of covariates (8) and large number of > records (27368). The response and all of the covariates are continuous > variables. > > A cursory examination of the covariates does not

I would like to build a forest of regression trees to see how well some covariates predict a response variable and to examine the importance of the covariates. I have a small number of covariates (8) and large number of records (27368). The response and all of the covariates are continuous variables. A cursory examination of the covariates does not suggest they are correlated in a simple fashion

Hello, I'm facing a strange behaviour when I try to run predict function for a cforest model from party package in CentOS. It works OK on MacOSX and Ubuntu, but R process is killed when I try it on CentOS. I read a dataframe and generate a forest model using cforest from party package. To reduce computation for the tests I'm running the forest model with 2 (two) trees only. So I believe

I tried to use ctree but am not sure about the meaning of the plot. My.data.ct<-ctree(Resp~., data=My.data) plot(My.data.ct) My data.frame contains 88 explanatory variables (continous,ordered/unordered multistate,count data) and one response with two groups. In the plot are only two variables shown (2 internal nodes) and 3 final nodes. Does it mean that only these two variables show a

Dear R users, I am trying to apply the analysis processed in a paper, on the data I'm working with. The data is: 80 patients for which I have survival data (time - days, and event - binary), and microarray expression data for 200 genes (predictor continuous variables). My data matrix "data.test" has ncol: 202 and nrow: 80. What I want to do is: - run recursive partitioning on

Hello All, I am using library "party" and I have found a curious/strange behaviour when trying to save the output of a ctree in a file via jpeg/png command. If you use: ################ library(party) airq <- subset(airquality, !is.na(Ozone)) airct <- ctree(Ozone ~ ., data = airq) plot(airct, terminal_panel = node_boxplot, drop_terminal = FALSE) ############### you get a