similar to: Ensembles in cforest

I am still struggling to map a character string to an object name and vice versa in R. I thought the as.name() function might work, but observe the following behaviour ... > attach(warpbreaks) > levels(tension) [1] "L" "M" "H" > levels(as.name("tension")) NULL > objectname<-as.name("tension") > objectname tension >

Dear list, I am running R2.11.1 on 32 bit windows. I am receiving messages as follows ... > require(car) Loading required package: car Error: package 'car' was built before R 2.10.0: please re-install it The package kohonen was another example. This failure appears to be fatal and not only affects the package concerned, but also all its dependents. Is there anything I can do at my

Dear List, Hopefully someone will point me to a piece of documentation that I have overlooked. I am running Rscript successfully to read and execute an R program, but have failed to find the correct syntax to route the output to a file using the Rscript command that invokes the job. I tried Rscript -e 'sink("outputfile")' script.R , but it then ignored the scriptfile. I know

Dear List, No doubt I am going around this the wrong way, and hopefully one of you will be able to tell me how to go about it the right way. I want to change the names of an object inside a function and have it stay changed in the global environment. I can only effect the change inside the function as follows ... >

This is hopefully a trivial problem for list subscribers, but I am very new to writing R functions. I wish to call an R function written by myself from another program to fit a model. I need to tell it which of the independent variables are factors. I need to do this in a generic way, so that when the list is passed, R will work through the variables in the data frame and coerce them into being

Dear List, I am writing a function in R with the facility to store models for later use in scoring. It would be very useful if I could include in the name of the file stored the name of the model object being stored, this name being chosen by the user in the function call. A simple function to store the name of an object as a character string would fit the bill, but I have not found one. name()

I have been learning how to use these functions and would like to know the following as I have so far been unable to find the answers in the documentation. 1) What stopping rules are used ? 2) Can the stopping rules be changed? 3) Can the results of each step be stored as objects in R and if so how ? 4) Is there a worked example somewhere of using the keep= argument ? Many thanks in

I thought it might be of interest to the group to pass on information about a replacement SMS command we have developed for sending text messages from Asterisk. The FASTSMS command will route text messages to mobile phones in 154 countries. Applications include voicemail notifications, missed call alerts, automated text receipts for callers (e.g. call reference number), system alerts, etc, etc.

I have a problem getting Windows 98 clients printing to CUPS printers where I have the printer access allowed/denied by username. It appears that samba is passing the username in uppercase to CUPS as the owner of the printjob and then CUPS does not recognise that username and rejects the job. It all works under Windows NT/XP. I am running samba 3.0.20 and cups 1.1.23. The smb.conf file is below

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 =

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

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]],

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, 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