similar to: randomForest.error: length of response must be the same as predictors

While trying to train randomForest with my dataset, I am ending up with the following error Error in randomForest.default(datatrain, classtrain) : length of response must be the same as predictors My data looks like: A,B,C,D,Class 1,2,1,2,cl1 1,2,1,2,cl1 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,1,2,cl2 3,2,12,3,cl2 3,2,1,2,cl2 Actual dataset has around 4000

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

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

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], ...) :

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) ##########################

Dear all, I need to create a for-loop in which I can compute multiple histograms My code is the following : #singlefile includes huge csv file #I want to specify the binsize #I would like to compute in the for -loop the histograms numfiles <- length(singlefile) for (i in 1 :51) { binsize <- -20 :20/2 hist(singlefile(singlefile$GVC[singlefile$new_id==i]], break=seq(),

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 community, I am new to R, a reforming SAS user :) I am running R 2.10.1 on a Windows XP machine. I would like to write linear functions of my coefficient parameter estimates from a glm, but am having a difficult time understanding the parameterization R uses. In the toy example below I am running a glm on binomial data, with clones and lines within clones as fixed effects, each with 6

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

Dear R Community, I am trying to create an heatmap for the following set of data: ##example of data matrix o4 V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 green 27 28 29 29 28 28 26 25 25 23 23 22 22 21 21 22 22 22 yellow 6 8 8 7 7 7 6 6 6 7 7 7 6 6 6 6 6 5 red 15 15 15 15 15 15 14 13 12 11 12 10 9 8 7 6 8 9 pink

Hi all, I am using R trying to get a inverse matrix of (X^T)X , but I keep getting the error message like: no b argument and no default value for sprintf(gettext(fmt, domain = domain), ...) . -------------------------------------------------------------------------------------------- # my code X<-Matrix(rep(1,500),100,5) X[lower.tri(X)]<-1-10^-7 XtX<- t(X)%*% X XtXu<-lu(XtX)

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 doing cluster analysis [hclust(Dist, method="average")] on data that potentially contains redundant objects. As expected, the inclusion of redundant objects affects the clustering result, i.e., the data a1, = a2, = a3, b, c, d, e1, = e2 is likely to cluster differently from the same data without the redundancy, i.e., a1, b, c, d, e1. This is apparent when the outcome is visualized

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

Hi, I am relatively new to R; when creating functions, I run into problems with missing values. I would like my functions to ignore rows with missing values for arguments of my function) in the analysis (as for example is the case in STATA). Note that I don't want my function to drop rows if there are missing arguments elsewhere in a row, ie for variables that are not arguments of my

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