similar to: RPART drawing the tree

I have narrowed down the problem. The error Error : 'AminoAcids' is not an exported object from 'namespace:bibliospec' Error : unable to load R code in package 'bibliospec' occurs only if I try to access the data using bibliospec::AminoAcids within the initialize method of an R reference class. It does work, as far as I tested everywhere else. In other methods of a

Hello, I am using kernlab to do some binary classification on aminoacid strings. I am using a custom kernel, so i use the kernel="matrix" option of the ksvm method. My (normalized) kernel matrix is of size 1309*1309, my results vector has the same length. I am using C-svc. My kernlab call is something similiar to this: ksvm(kernel="matrix", kernelMatrix, trainingDataYs,

I'm not sure if I should bother you team with this, apologies in case it's a bother. I'm trying gcc 6.2.1 (from devtoolset-6) with R, everything seems to work just fine, except for mzR. Here is failed build: g++ -m64 -shared -L/usr/lib64/R/lib -Wl,-z,relro -o mzR.so cramp.o ramp_base64.o ramp.o RcppRamp.o RcppRampModule.o rnetCDF.o RcppPwiz.o RcppPwizModule.o RcppIdent.o

I do this on a vanilla-clean R installation, simply: > biocLite("mzR") it pulls some deps in which compile fine, only mzR fails. ... meanwhile... I grabbed devtools and comiled github master - still fails. Should I attach build log? One should not send attachments to the list.. I don't suppose? On 21/12/16 17:06, Martin Morgan wrote: > mzR is a Bioconductor package, so

hello every one, How to function more than one loop in R? I have the following problem to be solved with the a method of three loops, can you help me please? The data is attached with this message. The data is composed of two parts, cleaved (denoted by ?cleaved?) and non cleaved (denoted by ?noncleaved?). ? to access to the ith peptide, you can use X$Peptide[i] ? to access to the ith label,

mzR is a Bioconductor package, so better to ask on the Bioconductor support forum https://support.bioconductor.org Oh, I see you did, and then the advice is to avoid cross-posting! The missing .o files would have been produced in an earlier compilation step; they likely failed in some way, so you need to provide the complete compilation output. Did you do this on a version of the package

I have a tree created with tr.hh.logcas <- rpart(log(YCASSX + 1)~AGE+DRUGUSEY+SEX+OBSXNUM +WINDLE, xval = 10) I would like to label the nodes with YCASSX rather than log(YCASSX + 1). But the help file for text in library rpart says that you can only use labels that are part of x$frame, which YCASSX is not. Is there a way to do what I want? Thanks in advance Peter Peter L. Flom, PhD

What bothers me is that samba is filling up my log files with a lot of extraneous/fake entries about authentication failures. "Extraneous/fake" - because all it is is a reflection of the way the protocol actually tries to login - going through the upper/lower case mutations as configured. sample log entries: ----------------------- Jun 30 19:40:35 dolphin PAM_pwdb[26988]: 1

Dear mailing list, I'm stuck with a tricky problem here - at least it seems tricky to me, being not really talented in pattern matching and regex matters. I'm analysing amino acid mutations by position and type of mutation. E.g. (fictitious example) in position 92, I can find L92V, L92MV, L92I... L is in this example the wild-type amino-acid, and everything behind the position number is

Hi, I am using the packages tree and rpart to build a classification tree to predict a 0/1 outcome. The package rpart has the advantage that the function plotcp gives a visual representation of the cross-validation results with a horizontal line indicating the 1 standard error rule, i.e. the recommendation to select the most parsimonious model (the smallest tree) whose error is not more than one

Hello useR, Could you please tell me how to draw the decision boundaries in a scatterplot of the original data for a LDA or Rpart object. For example: > library(rpart) >fit.rpart <- rpart(as.factor(group.id)~., data=data.frame(Data) ) How can I draw the cutting lines on the orignial Data? Or is there any built in functions that can read the rpart object 'fit.rpart' to do

After fitting and pruning an rpart model, it is often the case that one or more of the original predictors is not used by any of the splits of the final tree. It seems logical, therefore, that values for these "unused" predictors would not be needed for prediction. But when predict() is called on such models, all predictors seem to be required. Why is that, and can it be easily

All the documentation that I have on survival splitting is found in the technical report you mention. However, there is both a short form and a long form of this on our web site, did you get the larger one (52 pages)? I admit it is not a lot. There are no other split algorithms implimented for survival data. It would be possible to add your own. Attached is a slightly updated version of the

Hi, I have a list of mutations, called "mutList", of the form: > head(mutList) Alu 1 AluJ 2 AluJ/F(R)AM 3 AluJ/FLAM 4 AluJ/FRAM 5 AluJ/monomer 6 AluJb It contains about 500 elements and not all of them contain the sequence "Alu". I tried using this code: Alu<-mutList[which(grep("Alu",mutList)==1)] But that simply returned

Hi, I've lost my mind on it... I have to scatterplot two vectors, grouped by a third variable, with two different dimensions according to whether each cell line in the plot is sensitive or resistant to a given drug, and with a different color for each of 9 tissues of origin. Here's what I've done:

hi, I have a question regarding cp values in rpart(). When I use plotcp() I get a figure with cp values on the x-axsis, but then I use printcp() the cp values in that list are different from the values in the figure by plotcp(). Does someone know why? Silje [[alternative HTML version deleted]]

Hi R folks, I am using R version 2.2.1 for Unix. I am exploring the rpart function, in particular the rpart.control parameter. I have tried using different values for xval (0, 1, 10, 20) leaving other parameters constant but I receive the same tree after each run. Is the10 fold cross-validation default still running every time? I would expect the trees to change at least a little when I

In the help for rpart it says, "This differs from the tree function mainly in its handling of surrogate variables." And it says that an rpart object is a superset of a tree object. Both cite Brieman et al. 1984. Both call external code which looks like martian poetry to me. I've seen posts in the archives where BDR, and other knowledgeable folks, have said that rpart() is to be

Dear r-help-list: If I use the rpart method like cfit<-rpart(y~.,data=data,...), what kind of tree is stored in cfit? Is it right that this tree is not pruned at all, that it is the full tree? If so, it's up to me to choose a subtree by using the printcp method. In the technical report from Atkinson and Therneau "An Introduction to recursive partitioning using the rpart

Hello list, I'm trying to generate classifiers for a certain task using several methods, one of them being decision trees. The doubts come when I want to estimate the cross-validation error of the generated tree: tree <- rpart(y~., data=data.frame(xsel, y), cp=0.00001) ptree <- prune(tree, cp=tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"]) ptree$cptable