similar to: decision tree display

My decision tree grows only with one split and based on what I see in E-Miner it should split on more variables. How can I adjust splitting criteria in R? Also is there way to indicate that some variables are binary, like variable Info_G is binary so in the results would be nice to see "2) Info_G=0" instead of "2) Info_G<0.5". Thank you in advance! And thanks for Eric who

Hi, I am a brand new user and may be my question is too simple. I have R on our (not Unix) server. I am trying to build a decision tree and the error message says "couldn't find function rpart". Does it mean I have to ask our server guy to install an additional package? Thank you, Olga [[alternative HTML version deleted]]

Dear useRs: I try to plot an rpart object but cannot get a nice tree structure plot. I am using plot.rpart and text.rpart (please see below) but the branches that connect the nodes overlap the text in the ellipses and rectangles. Is there a way to get a clean nice tree plot (as in the Rpart Mayo report)? I work under Windows and use R2.11.1 with rpart version 3.1-46. Thank you. Tudor ...

Hi, I am Olga Viedma. I am running a Zero-inflated negative binomial (ZINB) multi-level model using MCMCglmm package. I have a doubt. Can I use the "Liab" outputs as fitted data, instead of the predicted values from "predict"? The liab outputs fit very well with the observed data, whereas the predicted values are so bad. Thanks in advance, Olga Viedma D . Olga

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

brtfs could be supported on Opensolaris, too. IMO it could even complement ZFS and spawn some concurrent development between both. ZFS is too high end and works very poorly with less than 2GB while brtfs reportedly works well with 128MB on ARM. Olga On Wed, Apr 14, 2010 at 5:31 PM, <Casper.Dik at sun.com> wrote: > > >>Just a completely different question...is there any plans

HI, Dear R community, How to extract the variables actually used in tree construction? I want to extract these variables and combine other variable as my features in next step model building. > printcp(fit.dimer) Classification tree: rpart(formula = outcome ~ ., data = p_df, method = "class") Variables actually used in tree construction: [1] CT DP DY FC NE NW QT SK TA WC WD WG WW

I'd like to train a decision tree on a set of weighted data points. I looked into the rpart package, which builds trees but doesn't seem to offer the capability of weighting inputs. (There is a weights parameter, but it seems to correspond to output classes rather than to input points). I'm making do for now by preprocessing my input data by adding multiple instances of each data

Dear all, I am using gnome-terminal to run R, and I noticed that standard PageUp/Down do not work but they do work for other programs within the same terminal window (e.g. irssi). Scroll bar does not work either. I run R using GNU-screen, but it doesn't seem to make any difference. Many thanks Olga ------------------ R version 2.11.1 (2010-05-31) 2010 x86_64 GNU/Linux Ubuntu 10.04.1 LTS

Have anybody used Decision Tree in Python or C++? (or written their own decision tree implementation in Python or C++)? My goal is to run decision tree on 8 million obs as training set and score 7 million in test set. I am testing 'rpart' package on a 64-bit-Linux + 64-bit-R environment. But it seems that rpart is either not stable or running out of memory very quickly. (Is it

Dear all, mydata<-data.frame(x1=c(1,4,6),x2=c(3,1,2),x3=c(2,1,3)) cor(mydata) x1 x2 x3 x1 1.0000000 -0.5960396 0.3973597 x2 -0.5960396 1.0000000 0.5000000 x3 0.3973597 0.5000000 1.0000000 I wonder if it is possible to fill only lower triangle of this correlation matrix? Using 'dist' doesn't seem to be useful as it doesnt allow to convert this table

Hi I'm trying to get the top decision rules from a decision tree. Eventually I will like to do this with R and Random Forrest. There has to be a way to output the decsion rules of each leaf node in an easily readable way. I am looking at the randomforrest and rpart packages and I dont see anything yet. Mike [[alternative HTML version deleted]]

Dear all, I am making a barplot as following: barplot(c(1,2,3,5,2,3,1),names.arg=c("100","200","300","400","500","600","700"),xlab="diameter",ylab="flow",main="some title",space=0.1) I am also trying to add a probability density curve, however using lines(density(c(1,2,3,5,2,3,1))) does not give

Ah yes I will have to use the predict function. But the predict function will not get me there really. If I can take the example that I have a model predicting whether or not I will play golf (this is the dependent value), and there are three independent variables Humidity(High, Medium, Low), Pending_Chores(Taxes, None, Laundry, Car Maintenance) and Wind (High, Low). I would like rules like

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

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

Hello, All! I have function which is written in C, the function uses other functions also builded bu me. Now I need to use the function to build R function. My questions are: 1. How can I include libraries (for example, iomanip, sstream, tcl.h)? 2. Should I put other function to the separate file and then include the file? Thanks All :) Olga [[alternative HTML version deleted]]

Dear all, I am trying to import data with read.csv and my file is on remote machine. I believe that I need to open a connection, not sure about syntax though. I would appreciate any suggestions, Thanks! Olga

Dear all, Using: library(vegan) data(BCI) mod <- radfit(BCI[1,]) mod RAD models, family poisson No. of species 93, total abundance 448 par1 par2 par3 Deviance AIC BIC Null 39.5261 315.4362 315.4362 Preemption 0.042797 21.8939 299.8041 302.3367 Lognormal 1.0687 1.0186 25.1528 305.0629 310.1281

Dear all, I am using GNU screen to run multiple R sessions from one working directory in order to split task, however I noticed that dataset is not synchronized e.g. if I have two sessions R1 and R2, and I remove an object from R1, R2 doesn't change as expected or change at random. I have tried to save.image(), q() and then restart both sessions, but it does not help. Any suggestions?