similar to: cforest and cforest_unbiased for testing and training datasets

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

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

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, There is an excellent article at http://www.biomedcentral.com/1471-2105/9/307 by Stroble, et al. describing variable importance in random forests. Does anyone have any suggestions (besides imputation or removal of cases) for how to deal with data that *have* missing data for predictor variables? Below is an excerpt of some code referenced in the article. I have commented out one line and

R programming question, not machine learning, although that's the content. Apologies to all for whom the following code is eye-burning. I am using foreach() to run a simulation on a randomForest model (actually conditional randomForest ... "party" package). The simulation is in two dimensions. examining how "mtry" and "ntrees" are related in terms of predictive

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

> > Hello everyone, > > I use the following command lines to get important variable from training > dataset. > > > data.controls <- cforest_unbiased(ntree=500, mtry=3) > data.cforest <- cforest(V1~.,data=rawinput,controls=data.controls) > data.cforest.varimp <- varimp(data.cforest, conditional = TRUE) > > I got error: "Error in

Dear All, For a data mining project, I am relying heavily on the RandomForest and Party packages. Due to the large size of the data set, I have often memory problems (in particular with the Party package; RandomForest seems to use less memory). I really have two questions at this point 1) Please see how I am using the Party and RandomForest packages. Any comment is welcome and useful.

Dear R users: I have been trying to figure out how to include string variables in a for loop to run multiple random forests with little success. The current code returns the following error: Error in trafo(data = data, numeric_trafo = numeric_trafo, factor_trafo = factor_trafo, : data class character is not supported In addition: Warning message: In storage.mode(RET@predict_trafo) <-

> > 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 trying to use the party package function varimp() to get conditional variable importance measures, as I'm aware that some of my variables are correlated. However I keep getting error messages (such as the example below). I get similar errors with three separate datasets that I'm using. At a guess it might be something to do with the very large number of variables (e.g.

require(randomForest) rf.pred<-predict(fit, valid, type="prob") > rf.pred[1:20, ] 0 1 16 0.0000 1.0000 23 0.3158 0.6842 43 0.3030 0.6970 52 0.0886 0.9114 55 0.1216 0.8784 75 0.0920 0.9080 82 0.4332 0.5668 120 0.2302 0.7698 128 0.1336 0.8664 147 0.4272 0.5728 148 0.0490 0.9510 153 0.0556 0.9444 161 0.0760 0.9240 162 0.4564 0.5436 172 0.5148 0.4852 176 0.1730

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 =

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, 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

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,

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