R ksvm kernlab unused arguments

Question

I'm learning how to use ksvm from kernlab to do classification. I've played with some examples (i.e. iris etc). However, when I try with my data, I keep getting an error:

Error in rbfdot(length = 4, lambda = 0.5) : unused argument(s) (length = 4, lambda = 0.5)

I really appreciate if someone can point out what went wrong, or point me to the appropriate documents.

Attached is my data file.

DataFile: http://www.mediafire.com/view/?todfg2su1qmw18n

My R code:

id = "100397.txt"
dat <- read.table(id, header=FALSE,sep = ",")
n = nrow(dat) # number of data points
numCol = ncol(dat)
dat <- dat[,-c(numCol)] ### get rid of the last column because it is not useful.
numCol = ncol(dat) ### update the number of columns

ntrain <- round(n*0.8) # get 80% of data points for cross-validation training

tindex <- sample(n,ntrain) # get all indices for cross-valication trainining

xtrain <- dat[tindex,-c(numCol)] # training data, not include the class label

xtest  <- dat[-tindex,-c(numCol)] # test data, not include the class label

ytrain <- dat[tindex,c(numCol)] # class label for training data

ytest  <- dat[-tindex,c(numCol)] # class label for testing data

nrow(xtrain)

length(ytrain)

nrow(xtest)

length(ytest)

### SVM function ###
svp <- ksvm(xtrain, ytrain, type="C-bsvc", kernel='rbf', C = 10, prob.model=TRUE)

Answer 1

Looking at the documentation of rbfdot, the function does not have the input arguments length nor lambda, which is exactly what the error message says. The kernel function stringdot does have these arguments, but does not have the sigma argument. For generating kernels, take a more close look at the documentation.