similar to: Error with cForest

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

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 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

Hi, I would like to compare 2 lists resulted from a sql query! bu there are different levels, so when I want to do: release1<-sqlQuery(channel,paste("select distinct c.ID,c.Title TitleCrit from category cat, category_criteria cc, criteria c, question_criteria qc, question q, form_question fq, form f, release_form rf, release r, product_release pr, product p where cat.ID=cc.category and

> > 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, I have written a function for calculating the volume of a tree (=trad) or snag (=h?gst). The included volume regreesion model includes ten parameter values, which are tree species specific. bj?rk.formh?jd.pars is an object which includes the parameter values (parameter set) for birch (=bj?rk). There is one row per tree in the data object. > relev.kols[1:5,

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

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

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: 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

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,

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

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, 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,

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

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