search for: maxsurrog

...edictor of ten multiple diet taxa abundances (prey.data) Neither rpart or mvpart seem to allow me to do multiple responses. (Or if they can, I'm not using the functions properly.) > library(rpart) > turtle.rtree<-rpart(prey.data~., data=turtle.data$Clength, method="anova", maxsurrogate=0, minsplit=8, minbucket=4, xval=10); plot(turtle.rtree); text(turtle.rtree) Error in terms.formula(formula, data = data) : '.' in formula and no 'data' argument When I switch response for predictor, it works. But this is the opposite of what I wanted to test and gives...

Dear all, I have a 100 iteration loop. Within each loop, there are some calls to rpart() like: ctl <- rpart.control(maxcompete=0, maxsurrogate=0, maxdepth=10) temp <- rpart(y~., x, w=wt, method="class", parms=list(split="gini"), control=ctl) res <- log(predict.rpart(temp, type="prob")) newres <- log(predict.rpart(temp, newdata=newx, type="prob")) The code runs fine for the first 30-60 i...

FWIW, I wrote a little function to extract variable importance as defined in the CART book a while ago. It's rather limited: Only works for regression problem, and you need to set maxsurrogate=0 and maxcompete=0. It may (or may not) help you: varimp.rpart <- function(x) { dev <- x$frame[, c("var", "dev")] dev <- dev[dev$var != "<leaf>", ] improve <- x$split[, "improve"] imp <- tapply(dev[, 2] * improve, dev...

...nderstand 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 14 terminal nodes, and as a result of the default graphical paramters of plot(ctree), all of the text is completely undreadable (decison node text is too large and as a result, these nodes overlap one another, terminal node bar...

...Message() to retrieve the error. When we execute the following command, cnr_model<-rpart(as.factor(Species)~Sepal Length+Sepal Width+Petal Length, method="class", parms=list(split="gini",prior=c()), control=rpart.control(minsplit=2, na.action=na.pass,cp=0.001,usesurrogate=1,maxsurrogate=2,surrogatestyle=0,maxdepth=20,xval=10)) we get an error message* "Error: unexpected symbol in "a<-cnr_model<-rpart(as.factor(Species)~Sepal Length""* The REXP returned in not null and the geterrMessage() call doesnt return an error message. But for other errors in R...

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

Dear R.Users, using ctree() (from "party" library) on a data.frame, I want to append a column with the references for the groups/segments detected. While these nodes are easy readable in output, I need a vector for my obs. Hints? Cheers -- Daniele Medri

Hi, I have a question regarding how to get some partial information from the output of rpart, which could be used as the first argument to predict. For example, in my code, I try to learn a stump tree (decision tree of depth 2):    "fit        <- rpart(y~bx, weights = w/mean(w), control = cntrl)     print(fit)     btest[1,]  <- predict(fit, newdata = data.frame(bx)) " I found

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

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

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