similar to: party - ctree() - terminal nodes reference for every obs

I'm trying to understand how to set graphical parameters for trees created with the party package.  For example take the following code:   library(party)     data(airquality)     airq <- subset(airquality, !is.na(Ozone))     airct <- ctree(Ozone ~ ., data = airq,                    controls = ctree_control(maxsurrogate = 3))     plot(airct)   My problem is, I've got a ctree that has

I have run the ctree function, and my dependent variable is broken into 3 categories: low cost, moderate cost and high cost. When i plot the results (eg. using plot(test.ct)), the plot shows, at the very bottom of each node, the probability of falling into each cost category. When i print the actual results (eg. using print(test.ct)), i get all of the backup

Dear R-helpers, I am looking for a segmentation package that gives some "tree identifier" as output for every observation in the data set (my response variable is binary). I have skimmed through "rpart", "ada" and "adabag": The output "trees" gives you the formula, but I have to run several thousand segmentations on different data sets and it

After ctree builds a tree, how would I determine the direction missing values follow by examining the BinaryTree-class object? For instance in the example below Bare.nuclei has 16 missing values and is used for the first split, but the missing values are not listed in either set of factors. (I have the same question for missing values among numeric [non-factor] values, but I assume the answer

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

Hello, I've been using ctree and have developed a 55 node - 28 terminal solution. As can be imagined, the plot is difficult to travel down each of the major branches. I've read the help files for ctree I saw where terminal nodes can be color coded. plot(airct, type = "simple") > plot(airct, terminal_panel = node_boxplot(airct, col = "blue", + fill = hsv(2/3,

I tried to use ctree but am not sure about the meaning of the plot. My.data.ct<-ctree(Resp~., data=My.data) plot(My.data.ct) My data.frame contains 88 explanatory variables (continous,ordered/unordered multistate,count data) and one response with two groups. In the plot are only two variables shown (2 internal nodes) and 3 final nodes. Does it mean that only these two variables show a

Hi, I am new to R and am using the ctree() function to do customer segmentation. I am using the following code to generate the tree: treedata$Response<-factor(treedata$Conversion) fit<-ctree(Response ~ .,controls=ctree_control(mincriterion=0.99,maxdepth=4),data=treedata) plot(fit) print(fit) The variable "Response" above equals 1 if the customer responded to an offering and

Hello. I have used the "party" package to generate a regression tree as follows: >origdata<-read.csv("origdata.csv") >ctrl<-ctree_control(mincriterion=0.99,maxdepth=10,minbucket=10) >test.ct<-ctree(Y~X1+X2+X3,data=origdata,control=ctrl) The above works fine. Orig data was my training data. I now have a test data file (testdata), and

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

Dears, anyone has experiences with text mining and R? I'll be very greatfull for tutorial or examples. Thanks -- Daniele Medri - http://www.medri.org

Hi, I have a problem with ctree of party package. I have data on distribution of more than one species (about 50 species) and I would like identify the relation of this multivariate object (species distribution) with a number of explanatory variables. rs is the name of my dataframe containing the species (columns from 2 to 51) and the explanatory variables (columns 52 and 53). Rows are my

When plotting Binary Trees (ctree) from the party package, is there a way to adjust the font sizes of the leaves? require(party) irisct <- ctree(Species ~ ., data = iris) plot(irisct) I want to adjust the font sizes for "Node 2", "Node 5", etc. I'd also like to be able to adjust the font sizes for the x-axis and y-axis labels of the histograms. Thanks,

Hello, I need to classify (i.e., export a vector with terminal node id's) new cases using a ctree (party package) model based on different cases (learning sample). I tried the where command with the following syntax: > where(tree, newdata=data2) expecting to get terminal nodes of data2 cases based on rules of tree model (data1 as learning sample). However it returned the following error

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

Hello, My question is related to ctree() function from the library 'party'. Is there a way to force ctree() to use a specific variable in the first split? I am asking because the first split contains two variables with very similar scores, and choosing the alternative variable would induce a tree somewhat easier to interpret. Thanks, Sal

Hallo list, I'm wondering if there is a way to extract the path to terminal nodes in a BinaryTree object, e.g. ctree, like the function path.rpart in package rpart. Thanks, Sven

Dear Group, What is the actual usage of "mtry" in ctree(), or specifically, ctree_control() since it's a single tree? Thanks in advance. Regards, Kelvin Lam, MSc. Analyst, Programming & Biostatistics Institute for Clinical Evaluative Sciences (ICES) 2075 Bayview Avenue, G179 Toronto, ON M4N 3M5 (416) 480-4055 Ext. 3057 Fax: (416) 480-6048 email:

Dear R users, I am trying to apply the analysis processed in a paper, on the data I'm working with. The data is: 80 patients for which I have survival data (time - days, and event - binary), and microarray expression data for 200 genes (predictor continuous variables). My data matrix "data.test" has ncol: 202 and nrow: 80. What I want to do is: - run recursive partitioning on

DeaRs, I have a doubt about: BIC (Bayesian Information Criterion) SBIC (Schwartz Bayesian Informarion Criterion) SIC (Schwartz Information Criterion) In many references these are know as the same (eg. stepAIC() function) but I just found a SAS8.2 output that show either the BIC and SIC values for a logistic regression.. simillary values but different. 1) question: What are the differences?