similar to: `partykit': A Toolkit for Recursive Partytioning

Hi Manuel, find our SoC proposal below. Best wishes, Torsten & Achim _______________________________________________________________________ Party On! New Recursive Partytioning Tools. Mentor: Torsten Hothorn & Achim Zeileis Short Description: The aim of the project is the implementation of recursive partitioning methods ("trees") which aren't available in R at the

Hi All, I'm a newcomer to the R community, just started learning R. I need to use partykit for developing CART. But I'm not getting any document/help for using partykit/rpart. http://www.agrocampus-ouest.fr/math/useR-2009/slides/Hothorn+Zeileis.pdfgives some example, but it doesn't tell how my own data should be formatted so that it can be accepted by partykit. Example,

I run the following code: library(rpart) data(kyphosis) fit <- rpart(Kyphosis ~ ., data=kyphosis) plot(fit) text(fit, use.n=TRUE) The text labels represent the count of each class at the leaf node. Unfortunately, the numbers are rounded and in scientific notation rather than the exact number of examples sorted by that node in each class. The plot is supposed to look like

Hola, ¿cómo están? Soy estudiante de Ing. en Sistemas de Información y estoy cursando la materia Tecnologías para la Explotación de Datos. Me he decidido a usar R para hacer DM. Estoy recién comenzando con ambos. El asunto es que no he encontrado la implementación en R de uno de los algoritmos que dimos para árboles de decisión, ID3. Ya revisé varios de los paquetes para DM

Dear r-help mailing list, Is there a way to incorporate weights into the minsplit criteria in rpart, when the weights are uneven? I could not find a way for the minsplit threshold to take the weights into account, and when the weights are uneven it becomes an issue, as the following example shows. My current workaround is to expand the data into one in which each row is an observation, but that

Hi, I have a technical question about rpart: according to Breiman et al. 1984, different costs for misclassification in CART can be modelled either by means of modifying the loss matrix or by means of using different prior probabilities for the classes, which again should have the same effect as using different weights for the response classes. What I tried was this: library(rpart)

Greetings R world, I know some version of the this question has been asked before, but i need to save the output of a loop into a data frame to eventually be written to a postgres data base with dbWriteTable. Some background. I have developed classifications models to help identify problem accounts. The logic is this, if the model classifies the record as including variable X and it turns out

Hello all, I wish to extract the terms from an rpart object. Specifically, I would like to be able to know what is the response variable (so I could do some manipulation on it). But in general, such a method for rpart will also need to handle a "." case (see fit2) Here are two simple examples: fit1 <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) fit1$call fit2 <-

Greetings All, I am looking to write a parametrized Rscript that will accept a variable name(that also is the name of the flat file), transform the data into a data frame and preform various modeling on the structure and save the output and plot of the model. In this example i am using a rpart decision tree. The only problem i am having is integrating the parameter into the internal object name

Dear R-users I am using RPART package to get regression trees. However having trouble getting the text function to put the full splitting rule number on the plot, instead to puts it in scientific notation. When a covariate has 1e4 or greater number of digits then the splitting rule number displayed on the plot is in scientific notation. But print.rpart displays the splitting rules in full.

Hi, I am having some trouble using R 2.13.1 for generating a pmml object of of class "c('randomForest.formula', 'randomForest')" I see that these methods are available: > methods(pmml) [1] pmml.coxph* pmml.hclust* pmml.itemsets* pmml.kmeans* pmml.ksvm* pmml.lm* pmml.multinom* pmml.nnet* pmml.rpart* [10] pmml.rsf* pmml.rules* pmml.survreg*

Hello everybody I'm having some difficulties to design the decision tree algorithm J48. I am using the following code and when I run it gives me the following message plot(m1) Error in plot.Weka_tree(m1) : Plotting of trees with multi-way splits is currently not implemented. #The code library(RWeka) library(randomForest) library(party) if(require(mlbench, quietly = TRUE) &&

Version 1.2.0 of pmml has been released and is available from CRAN. The pmml package (http://rattle.togaware.com/pmml.html) is part of the Rattle data mining suite http://rattle.togaware.com. It generates representations of analytic models built in R using the open standard predictive modelling markup language (http://www.dmg.org/). PMML represents analytic models in an application independent

Version 1.2.0 of pmml has been released and is available from CRAN. The pmml package (http://rattle.togaware.com/pmml.html) is part of the Rattle data mining suite http://rattle.togaware.com. It generates representations of analytic models built in R using the open standard predictive modelling markup language (http://www.dmg.org/). PMML represents analytic models in an application independent

Simple example: # Classification Tree with rpart library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start, method="class", data=kyphosis) Now I would like to know how can I measure the "importance" of each of my three explanatory variables (Age, Number, Start) in the model? If this was a regression model, I could have looked at p values from the

Hi, Suppose i have generated an object using the following : fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) And when i print fit, i get the following : n= 81 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 81 17 absent (0.7901235 0.2098765) 2) Start>=8.5 62 6 absent (0.9032258 0.0967742) 4) Start>=14.5 29 0 absent (1.0000000

Hi, I am using rpart package to fit classification trees. library(rpart) fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) plot(fit,uniform=T) text(fit, use.n=TRUE) But I am unable to label the branches (not the nodes) of the tree. Can somebody help me out in this? Thank you, Regards Utkarsh Singhal | Amba Research Ph +91 80 3980 8017 | Mob +91 99 0295 8815

I ran this code (several times) from the Quick-R web page ( http://www.statmethods.net/advstats/cart.html) but my cross-validation errors increase instead of decrease (same thing happens with an unrelated data set). Why does this happen? Am I doing something wrong? # Classification Tree with rpart library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start,

Hello Everyone, I'm doing a Classification trees with categorical explanatory variables using library rpart and I would like to do a prediction for some data imputs. I don't know where's a function or how can I do it?. Is there someone can help ?? ¿. Here's the code that I'm using. library(rpart) fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) plot(fit)

Philipp Benner reported a Debian bug report against r-cran-rpart aka rpart. In short, the issue has to do with how rpart evaluates a formula and supporting arguments, in particular 'weights'. A simple contrived example is ----------------------------------------------------------------------------- library(rpart) ## using data from help(rpart), set up simple example myformula <-