similar to: randomForest training error

My data looks like: A,B,C,D,Class 1,2,0,2,cl1 1,5,1,9,cl1 3,2,1,2,cl2 7,2,1,2,cl2 2,2,1,2,cl2 1,2,1,5,cl2 0,2,1,2,cl2 4,2,1,2,cl2 3,5,1,2,cl2 3,2,12,3,cl2 3,2,4,2,cl2 **The steps followed are: trainfile <- read.csv("TrainFile",head=TRUE) datatrain <- subset(trainfile,select=c(-Class)) classtrain <- (subset(trainfile,select=Class)) rf <- randomForest(datatrain, classtrain)

Does something like this get you close: x <- list() keys <- LETTERS[1:6] # create for (i in keys){ x[[i]] <- data.frame(a=1:5, b=1:5, c=1:5) } # output output <- file('tempxx.txt', 'w') for (i in keys){ write.table(i, row.names=FALSE, col.names=FALSE, file=output, quote=FALSE) write.table(x[[i]], file=output, quote=FALSE) } close(output) On Wed, Jul 2,

Hi, I am trying to use svm for regression data. this is how my data looks like: >dataTrain x y z 1 4 6 2 5 4 3 7 5 >classTrain a 2 3 4 >dataTest x y z 1 7 2 2 8 3 >classTest a 3 4 5 building the model model<-svm(dataTrain,classTrain,type="nu-regression") pred <- predict(model, dataTest) > pred 1 2 3.008842 3.120078 I

Hi, I am trying to train svm with some training data of about 4000 rows and 4000 columns. While running svm function I am ending up with the following error. trainfile <- read.csv('0_train_0016435.csv',head=TRUE,na.strings = "NULL") datatrain <- subset(trainfile,select=c(-Class)) model <- svm(datatrain, kernel="radial") Error in FUN(newX[, i], ...) :

What should I do if I need to train svm() with data having same value across all rows in some columns. These must be the important features of the class and we cant exclude these columns to build up models. The error I am getting is: Error in predict.svm(ret, xhold) : Model is empty! In addition: Warning message: In svm.default(datatrain, classtrain) : Variable(s) 'F112' and

I expected, that I will get the same prediction, if I multiply the weights for all classes with a constant factor, but I got different results. Please look for the following code. > library(e1071) > data(Glass, package = "mlbench") > index <- 1:nrow(Glass) > testindex <- sample(index, trunc(length(index)/5)) > testset <- Glass[testindex, ] > trainset <-

Dear R-helpers, I tried the playwith packages for the first time, and it crashed R: > require(playwith) Loading required package: playwith Loading required package: lattice Loading required package: grid Loading required package: gWidgets Loading required package: gWidgetsRGtk2 Loading required package: RGtk2 Loading required package: cairoDevice > sessionInfo() R version 2.7.1

Hello, In the attached file training.csv (I apologize for the large file) I have 238 objects belonging to 13 classes, which are described by 183 properties. I would like to find a svm model for these objects. I tried the following R statements. library('e1071') datatraining <- read.csv("training.csv",head=TRUE) names<-names(datatraining) print("before

I am trying to use knn to do a nearest neighbor classification. I tried using my dataset and got an error message so I used a simple example to try and understand what I was doing wrong and got the same message. Here is what I typed into R: try [,1] [,2] [,3] [,4] r "A" "A" "T" "G" r "A" "A" "T" "G" f

List, In order to get rid of some old, unreadable S3 code in package sp, I'm trying to rewrite things using S4 methods. Somewhere I fail, and I cannot sort out why. In order to isolate the problem, I created two functions, doNothing<- and dosth, and both should do nothing. The issue is that in most cases they do nothing, but in some cases dosth(obj) changes the class of obj and breaks with

Hi, I have generated a skyline plot of a tree in newick format using "ape". How can I plot the error bars for this graph? I only have the the tree data. un<-"((((8.1:0, 20.1:0):0, 6.1:3):123, (((((35.1:0, (22.1:0, (43.1:1, 29.1:0):0):0):4, 25.1:6):0, ((42.1:0, 21.1:0):3, (39.1:0, 2.1:0):3):1):8, (3.1:0, 7.1:0):8):48, (((((15.1:0, 14.1:0):0, 11.1:0):0, 37.1:0):0,

The function knn.cv in the class package doesn't have error checking to ensure that the length of the classlabel argument is equal to the number of rows in the test set. If the classlabel is short, the result is often a segfault. > library(class) > dat <- matrix(rnorm(1000), nrow=10) > cl <- c(rep(1,5), rep(2,5)) > cl2 <- c(rep(1,5), rep(2,4)) > knn.cv(dat, cl) [1] 2

Greetings R-users, I have been using the fpc package in R to cluster my data. Speficically I am using kmeansruns clustering. I would like to know how I use R to partition data into clusters. What I am doing is as follows. # Use csv file as input ##################### wholeset = read.csv("Spellman800genesImputed.csv") # exclude first col (gene names) ##########################

I am using the randomForest package. I have found that multiple runs of precisely the same command can generate drastically different run times. Can anyone with knowledge of this package provide some insight as to why this would happen and whether there's anything I can do about it? Here are some details of what I'm doing: - Data: ~80,000 rows, with 10 columns (one of which is the

Hello I am writing a qdisc scheduler (called fb in the diagram) that does certain packet delaying and other stuff. (for egress traffic) root (htb/tbf) | --------------- cl1 cl2 | tbf -------- | | cl3 cl4 | | *fb* fifo Now I need to

Hi all, I have a Samba system running on a standard purchased SuSE Linux 9.3 box. I want to transfer the data to a new Samba running on a standard Suse Linux 10.0 purchased box version, on a x86_64 cluster platform. The new configuration is as follows: I have one central SuSE Linux 10.0 server cluster for all data. It exports as NFS-Server several shares to a number of NFS-Clients. (The

Dear list, here is an example of stepAIC that I do not understand. The data is n=42, Lage is the only factor and there are four other variables treated as continuous. First you see the stepAIC-forward solution (fs7). The strange thing here is that apparently not all interactions are tried for inclusion, but only WQ:Lage. In particular, I think that WFL:Lage should be tried in the last two

Hello, The problem is that the factors are not orderd by the row number. If you want to check their order, use str(sortdata) and you'll see Santa-Rosa was attributed factor level 4 (in the output, first variable, the 3rd and 4th). Try the following. sortdata <- read.table(text=" county year x1 x2 x3 x4 x5 x6 x7 rank 141 Escambia 2002 6.50

Dear Group I'm still trying to bring many data into R (see older postings). After solving some troubles with the database I do most of the work in MySQL. But still I could be nice to work on some data using R. Therefore I can use a dedicated Server with Gentoo Linux as OS hosting only R. This Server is a nice machine with two CPU and 4GB RAM which should do the job: Dual Intel XEON 3.06 GHz

HI Stefan, CC'ing Reid Kleckner, who might have some insight here, and llvm-dev as this may be of interest to other windows JIT users. I am facing the issue that C++ dynamic_cast doesn't work for types > loaded from object files with RuntimeDyLd. <snip> Do you think it is possible that RuntimeDyLd misses type info data in > the COFF file or doesn't wire it up