similar to: Good Decision Trees with Product Purchased Data?

Hello, I was just assigned to perform a cost effectiveness study in healthcare. We are studying the cost effectiveness of a proposed diagnostic vs. current screening procedures. One of the team members suggest a commercial software package called "TreeAge Pro". Looking at the description, it appears to be a nice GUI to some very simple models that could be easily constructed in R.

Hi all, I have a package that is specific to a task I was repetitively using a few years ago. I now needed to run it again with new data. However I am told it was built with an older version or R and will not work. How can I tweak the package so it will run on 11.1? It was a one-off product and has not been maintained. Is there a way to "unpackage" it and repackage it to work? I

I'm experienced in statistics, but I am a first-time R user. I would like to use R for correspondence analysis. I have installed R (Mac OSX). I have used the package installer to install the CA package. I have run the following line with no errors to read in the data for a table: NonLuxury <- read.table("/Users/myUserName/Desktop/nonLuxury.data.txt") The R online help

I am new in using R. I want to know all about building decision tree model in R. Few options which I searched are rpart and rattle to build a decision tree.Both the functions are giving me splits which are statistically appropriate. But I am not able to figure out how to change those splits as per my business requirement. for example : the automatic split of Age by using rattle is > 30 and

who has purchased a V400 card from Varion ? I need some help . please help me . thanks a lot

Hello, this question is a bit out of the blue. I am a big R fan and user and in my new job I do some decision modeling (mostly health economics). For that decision trees are often used (I guess the most classic example is the investment decision A, B, and C with different probabilities, what is the expected payoff). We use a specialized software called TreeAge that some might know. The basic

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

Hi All, I need to access the node informations (viz. child nodes, split variable, split criterion etc.) for different trees packages like, CHAID, rpart, party etc. Is there an in-built function which I can use to get the requied info? Thanks a lot in advance!! Regards Ashish Kumar [[alternative HTML version deleted]]

Hi, I'm using the package randomForest to generate a classifier for the exemplary iris data set: data(iris) iris.rf<-randomForest(Species~.,iris) Is it possible to print all decision trees in the generated forest? If so, can the trees be also written to disk? What I actually need is to translate the decision trees in a random forest into equivalent C++ if-then-else constructs to

Hello, I have a question concerning decision trees coming from RWeka : library(RWeka) m =J48(Species~.,data=iris) How could such a decision tree be transferred into a matrix, pretty much in the same fashion, as it is done by getTree() in library(ofw) library(ofw) data(srbct) attach(srbct) ##ofwCART learn.cart.keep <- ofw(srbct,

Hi, while working with decision trees and unbalanced data, I came across the use of the Hellinger distance as an alternative to information gain [1,2], when dealing with skewed data. Does anybody know of R implementations of this approach to decision trees? Thanks, [1] http://www.cse.nd.edu/Reports/2008/TR-2008-06.pdf [2] http://csmr.ca.sandia.gov/~wpk/slides/wdmda-sem.pdf -- Rajarshi Guha NIH

Hi, I'm using the package randomForest to generate a classifier for the exemplary iris data set: data(iris) iris.rf<-randomForest(Species~.,iris) Is it possible to print all decision trees in the generated forest? If so, can the trees be also written to disk? What I actually need is to translate the decision trees in a random forest into equivalent C++ if-then-else constructs to

Hi all, Does any of you know how to make a decision tree when the data set contains factors and numericals? I've got a data frame with 3 columns, where y and x1 are numerical and x2 contains factors. Is it possible to use the rpart package, and in that case how? Otherwise, is there another alternative? This is what I've tried so far > rpart(LT50_NA ~ Raf + Antho,

Hi, Does R have a Decision Tree functionality akin to 'Precision Tree' by Palisade? When I search, I end up with 'rpart' but this does not appear to be what I am looking for. Kind regards, Per Bak Copenhagen

Hi, I have seen that R has a implementation of decision trees; however, after I have the tree with the classification: R Quinlan's trivial example of the "golf" decision tree. Outlook Temperature Humidity Windy PlayDontPlay 1 sunny 85 85 false DontPlay 2 sunny 80 90 true DontPlay 3 overcast 83 78 false Play 4 rain 70 96 false Play ... What's next? I mean, what is this

I am familiar with the rpart and tree packages for classification and regression trees. However, quite a bit of the research in the transportation community relating to decision trees uses the C4.5 family of algorithms by Quinlan. Are there any plans to make a C4.5 (or a derivative of it) available to R? If not, then I might use the WEKA Java package ( http://www.cs.waikato.ac.nz/ml/weka) that

Hi! I'm trying to construct and plot a decision tree to class a set of only 8 objects and tried to use the rpart and tree function, but get a error message both times: rpart: fit is not a tree, just a root tree: cannot plot singlenode tree I read in the post 'question regression trees' that rpart doesn't split a set of fewer than 20 objects...so I guess the same holds true for

Hi, I am somewhat new to R so this question may be foolish, but is it possible to add decision trees into a list, array or vector in R? I am trying to build a collection (ensemble) of decision trees. Every time a new instance arrive I need to get the prediction of each decision tree. I have tried to add a decision tree into a variable but without luck. Is a special package needed perhaps? This

Hi netters, I want to learn a decision tree from a series of instances (learning data). The packages tree or rpart can do this quite well, but the scoring functions (splitting criteria) are fixed in these packages, like gini or something. However, I'm going to use another scoring function. At first I wanna modify the R code of tree or rpart and put my own scoring function in. But it

All, does any one has a drag and drop sample code using rails and ajax that I can use as a starting point for mine? I ''ll for customers to drag and drop item being purchase into their shopping cart. Thanks Patrick