similar to: Fwd: Re: Party extract BinaryTree from cforest?

Displaying 20 results from an estimated 1000 matches similar to: "Fwd: Re: Party extract BinaryTree from cforest?"

2013 Feb 14
1
party::cforest - predict?
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 =
2011 Feb 22
0
cforest() and missing values (party package)
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,
2010 Mar 16
0
Ensembles in cforest
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
2012 Apr 29
1
CForest Error Logical Subscript Too Long
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
2011 Jul 20
0
cforest - keep.forest = false option? (fwd)
> ---------- 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
2010 Nov 01
0
Extract node names from BinaryTree in package party
Hi there, I need to extract the variable names from all nodes (except the terminal nodes) from a ctree object, e.g. library(party) mammoct <- ctree(ME ~ ., data = mammoexp) plot(mammoct) how can I extract the varnames from node 1 (SYMPT) and node 3 (PB) from the fitted object "mammoct"? Many Thanks, Sven
2011 Jul 18
0
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 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
2012 Sep 13
0
cforest and cforest_unbiased for testing and training datasets
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
2017 Nov 18
0
Using cforest on a hierarchically structured dataset
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
2012 Oct 11
0
Error with cForest
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
2010 Jun 10
2
Cforest and Random Forest memory use
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,
2007 Oct 25
0
adjust labels in plot:terminal_panel {party}
Hi List, I am unsuccessfully trying to beautify barplot outputs from ctree. For example I would like to rotate x-axis lables and resize/change font/type. mtree <- ctree(ME ~ ., data = mammoexp) plot(mtree,terminal_panel=node_barplot(mtree,col="black",fill=NULL, beside=TRUE, ylines=NULL, widths=1,gap=NULL, reverse=FALSE,id=FALSE))
2006 Feb 24
0
New `party' tools
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
2006 Mar 01
1
Problems to get a ctree plot (library party) in a file via jpeg/png
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
2010 Jul 27
1
Cforest mincriterion
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,
2011 Sep 08
1
Need formatting help - ctree - plot.party - node_hist
Hi, I am trying to get the terminal nodes of a plot of a ctree object to look nice. Using the iris data I have: library(party) mtree <- ctree(Species ~ ., data=iris) plot(mtree,terminal_panel=node_barplot(mtree)) The terminal nodes don't display the species names because the names are displayed horizontally. ?I would like to reduce the size of the labels and make the terminal nodes
2012 Dec 11
2
VarimpAUC in Party Package
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
2011 Oct 17
0
Party package: varimp(..., conditional=TRUE) error: term 1 would require 9e+12 columns (fwd)
> > 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
2011 Oct 14
1
Party package: varimp(..., conditional=TRUE) error: term 1 would require 9e+12 columns
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
2012 Dec 06
0
Package party Error in model.matrix.default(as.formula(f), data = blocks) :allocMatrix: too many elements specified
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