similar to: Ada package question

Dear R Users, I'm having trouble reproducing the results in Section 5.1 of Culp, M., Johnson, K., Michailidis, G. (2006). ada: an R Package for Stochastic Boosting Journal of Statistical Software, 16 They build and display a boosting model with the code: library("ada") n <- 12000 p <- 10 set.seed(100) x <- matrix(rnorm(n*p), ncol=p) y <-

Hi R-helpers, I am using rpart package for decision tree using R.We are invoking R environment through JRI from our java application.Hence, the result of R command is returned in REXP and we use geterrMessage() to retrieve the error. When we execute the following command, cnr_model<-rpart(as.factor(Species)~Sepal Length+Sepal Width+Petal Length, method="class",

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)

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 Listers I need to calculate and then plot a frequency histogram of the best tree calculated using the 1-se rule. I have included some code that has worked well for me in the past but it was only for selecting the minimum cross-validation error. I include the code for my model, some relevant output and the code for selecting and plotting the frequency histogram of minimum xerror. Here is the

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 <-

HI, Dear R community, I am writing the following function to create one data set(*tree.pred*) and one vector(*valid.out*) from loops. Later, I want to use the data set from this loop to plot curves. I have tried return, list, but I can not use the *tree.pred* data and *valid.out* vector. auc.tree<- function(msplit,mbucket) { * tree.pred<-data.frame()

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

Hi, I am trying to use the rpart library with my own set of functions on a survival object. I get an immeadiate segmentation fault when i try calling rpart with my list of functions. I get the same problem with the logrank example from Therneau,s S-rpart library though their anova example works. Should I report this as a bug, as even if my functions are structured improperly, that should lead to

I am using the package rpart to explore various classification structures. The call looks like: seekhi1<-rpart(pvol~spec+a1+psize+eppres+numpt+icds+bivalcrt+stents+ppshare+ nhosp+nyrs,data=dat,method="class", control=rpart.control(minsplit=30,xval=10)) The output is 1) root 198 87 1 (0.5606061 0.4393939) 2) psize=1,2 122 43 1 (0.6475410 0.3524590)

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

Dear r-help mailing list, Is there a way to incorporate weights into the minsplit criteria in rpart, when the weights are uneven? I could not find a way for the minsplit threshold to take the weights into account, and when the weights are uneven it becomes an issue, as the following example shows. My current workaround is to expand the data into one in which each row is an observation, but that

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

Duncan Sands wrote: > Hi Eric, > > >> I'm having problems building llvm-gcc for Ada, which may or may not be >> similar to the trouble Andre was having recently. I'm trying to build >> it on Fedora 10 (32-bit), using the instructions on the web site. The >> make terminates with a gnatbind error: >> >> gcc -c -g -O2 -gnatpg -gnata

Hi Eric, > I'm having problems building llvm-gcc for Ada, which may or may not be > similar to the trouble Andre was having recently. I'm trying to build > it on Fedora 10 (32-bit), using the instructions on the web site. The > make terminates with a gnatbind error: > > gcc -c -g -O2 -gnatpg -gnata -I- -I. -Iada > -I../../llvm-gcc-4.2/gcc/ada

Duncan, It would be useful to know what motivated this work -- in particular, is some organization (company, open source project team, research group, any other kind) interested in having an Ada front-end? Thanks, --Vikram ---------------------------------------------------------------------- VIKRAM S. ADVE Associate Professor, Computer Science E-MAIL: vadve at cs.uiuc.edu Siebel

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

I am trying to build llvm-gcc-4.2 but cannot figure out how to get past the problem described below. Any help will be much appreciated. Regards Roger gnatbind -C -I- -I. -Iada -I../../llvm-gcc-4.2/gcc/ada -o ada/b_gnat1.c -n ada/gnat1drv.ali error: "s-imenne.adb" must be compiled error: ("/opt/gnat-gpl-2009/lib/gcc/i386-apple-darwin10.2.0/4.3.4/adalib/s-imenne.ali" is obsolete

I'm having problems building llvm-gcc for Ada, which may or may not be similar to the trouble Andre was having recently. I'm trying to build it on Fedora 10 (32-bit), using the instructions on the web site. The make terminates with a gnatbind error: gcc -c -g -O2 -gnatpg -gnata -I- -I. -Iada -I../../llvm-gcc-4.2/gcc/ada ../../llvm-gcc-4.2/gcc/ada/gnat1drv.adb -o ada/gnat1drv.o

Hi Roger, > I am trying to build llvm-gcc-4.2 but cannot figure out how to get past the problem described below. you need to build with GNAT GPL 2007 or earlier, as described here: http://llvm.org/docs/GCCFEBuildInstrs.html This is because llvm-gcc-4.2 is based on gcc-4.2, and you have the same problem with gcc-4.2. I don't know if anyone ever built an Ada-enabled llvm-gcc on MacOS