similar to: party::cforest - predict?

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

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

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

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.

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

Hi, I'm using package "caret" to rank predictors using random forest model and draw predictors importance plot. I used below commands: rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector ## Then I got the ranked predictors by ranking

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

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,

> > 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

> ---------- 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

Dear All, I would like to use a randomForest algorithm on a dataset. The set is not particularly large/difficult to handle, but it has some missing values (both factors and numerical values). According to what I found https://stat.ethz.ch/pipermail/r-help/2005-September/078880.html https://stat.ethz.ch/pipermail/r-help/2007-January/123117.html the randomForest package has a problem with missing

Hi, I'm trying to make a partial plot with package randomForest in R. After I perform my random forest object I type partialPlot(data.rforest, pred.data=act2, x.var=centroid, "C") where data.rforest is my randomforest object, act2 is the original dataset, centroid is one of the predictor and C is one of the classes in my response variable. Whatever predictor or response class I

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,

Hello, I am learning caret package, and I want to use the RFE to reduce the feature. I want to use RFE coupled Random Forest (RFE+FR) to complete this task. As we know, there are a number of pre-defined sets of functions, like random Forest(rfFuncs), however,I want to tune the parameters (mtr) when RFE, and then I write code below, but there is something wrong message, How can I deal with it?

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.

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

> ---------- 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 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

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,