similar to: how to Store loop output from a function

cv.fold<-function(i, size=3, rang=0.3){ cat('Fold ', i, '\n') out.fold.c <-((i-1)*c.each.part +1):(i*c.each.part) out.fold.n <-((i-1)*n.each.part +1):(i*n.each.part) train.cv <- n.cc[-out.fold.c, c(2:2401, 2417)] train.nv <- n.nn[-out.fold.n, c(2:2401, 2417)] train.v<-rbind(train.cv, train.nv) #training data for feature

Dear R friends- I'm attempting to generate a regression tree with one gradient predictor and multiple responses, trying to test if change in size (turtle.data$Clength) acts as a single predictor 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)

Dear All, I have a question regarding predict.rpart. I use rpart to build classification and regression trees and I deal with data with relatively large number of input variables (predictors). For example, I build an rpart model like this rpartModel <- rpart(Y ~ X, method="class", minsplit =1, minbucket=nMinBucket,cp=nCp); and get predictors used in building the model like

Hi. I uses rpart to build a regression tree. Y is continuous. Now, I try to predict on a new set of data. In the new set of data, one of my x (call Incoterm, a factor) has a new level. I wonder why the error below appears as the guide says "For factor predictors, if an observation contains a level not used to grow the tree, it is left at the deepest possible node and

Dear R community, I have one function, it works for small data set, but does not work on large data set, can anyone help me with this? > #creat new variable by dividing each aa dimer by total_length. > imper<-function(x, file) { + round(x/file$length, 5) + } > dim(test) [1] 999 2402 > test[varname[2:2401]]<-

Brian The following (which is new to rw1030) occurs with both Windows 98 & Windows ME. I have not tested behaviour under Unix or Linux, but I expect it is no different. text.rpart() prints unwanted NAs (presumably in the splitting criterion position) on terminal nodes. Criterion <- factor(paste("Leaf", 1:5)) Node <- factor(1:5)

> fit.dimer <- rpart(as.factor(out) ~ ., method="class", data=p_df) > > fit.dimer$frame[, "var"] [1] NE WC <leaf> TA <leaf> <leaf> WG WD WW WC [11] <leaf> <leaf> <leaf> CT <leaf> FC <leaf> YG QT <leaf> [21] <leaf> <leaf> NW DP DY <leaf> SK

Dear r-list: I am using rpart to build a tree on a dataset. First I obtain a perhaps too large tree: > arbol.bsvg.02 <- rpart(formula, data = bsvg, subset=grp.entr, control=rpart.control(cp=0.001)) > arbol.bsvg.02 n= 100000 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 100000 6657 0 (0.93343000 0.06657000) 2) meses_antiguedad_svg>=10.5 73899 3658

Hi All, I have to say upfront that I am a complete neophyte when it comes to programming. Nevertheless I enjoy the challenge of using R because of its incredible statistical resources. My problem is this .........I am running a regression tree analysis using "rpart" and I need to run the calculation repeatedly (say n=50 times) to obtain a distribution of results from which I will pick

HI, Dear R community, Last friday, I used the codes, it works, but today, it does not run? > fit.dimer <- rpart(outcome ~., method="class", data=p.df) Error in `[.data.frame`(frame, predictors) : undefined columns selected DOEs anyone have comments or suggestions? Thanks in advance! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

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

HI, Dear R community, I have a large data set names dd.txt, the columns are: there are 2402 variables. a1, b1, ..z1, a11, b11, ...z11, a111, b111, ..z111.. IF I dont know the relative position of the columns, but I know I need the following variables: var<-c(a1, c1,a11,b11,f111) Can I use read.columns to read the data into R? I have tried the following codes, but it does not work

HI, Dear R-community, I AM using the following codes to grow tree and plot tree: # Classification Tree with rpart library(rpart) pdf(file="/home/cdu/changbin/dimer_tree.pdf") # grow tree fit.dimer <- rpart(outcome ~ ., method="class", data=p.dimer[,2:402]) plotcp(fit.dimer) # visualize cross-validation results # plot tree plot(fit.dimer, uniform=TRUE,

Dear all, I'm trying to manage with user defined split function in rpart (file rpart\tests\usersplits.R in http://cran.r-project.org/src/contrib/rpart_3.1-34.tar.gz - see bottom of the email). Suppose to have the following data.frame (note that x's values are already sorted) > D y x 1 7 0.428 2 3 0.876 3 1 1.467 4 6 1.492 5 3 1.703 6 4 2.406 7 8 2.628 8 6 2.879 9 5 3.025 10 3 3.494

mboost contains a blackboost method to build tree-based boosting models. I tried to write my own "cost-sensitive" ada family. But obviously my understanding to implement ngradient, loss, and offset functions is not right. I would greatly appreciate if anyone can help me out, or show me how to write a cost-sensitive family, thanks! Follows are some families I wrote ngradient <-

Dear all, I know in rpart(), one can control the tree size (i.e. number of terminal nodes) through rpart.control(), e.g. minsplit, minbucket, maxdepth etc. But is there any more direct way to specify the number of terminal nodes when rpart() does the recursive partitioning? Your help is highly appreciated! Regards, -Ji

I have a question relating to mvpart, which I hope you can answer. We recently conducted a study using TBR. In our first study, we used "regular" TBR in SPSS to model 1 dependent variable. Note we have a relatively small data-set of 100 cases. In SPSS, we used a minimum change of improvement smaller than 0.000001 as a stopping rule. Also, we chose the 1SE "rule", set the

Should one expect minor numerical differences between 64bit and 32bit R on Windows? Hunting around the lists I've not been able to find a definitive answer yet. Seems plausible using different precision arithmetic, but waned to confirm from those who might know for sure. BACKGROUND A colleague was trying to replicate some modelling results (from a soon to be published book) using rpart, ada,

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

Hi, I am using rpart to do leave one out cross validation, but met some problem, Data is a data frame, the first column is the subject id, the second column is the group id, and the rest columns are numerical variables, > Data[1:5,1:10] sub.id group.id X3262.345 X3277.402 X3369.036 X3439.895 X3886.935 X3939.054 X3953.777 X3970.352 1 32613 HAM_TSP 417.7082 430.4895 619.4776 720.8246