hi,
 I am using R's  "kernlab" package, exactly i am doing classification using
ksvm(.) and predict.ksvm(.).I want use of custom kernel. I am getting some
error.

# Following R code works (with promotergene dataset):

library("kernlab")
s <- function(x, y) {
 sum((x*y)^1.25)
 }
class(s) <- "kernel"
data("promotergene")
gene <- ksvm(Class ~ ., data = promotergene,
kernel = s, C = 10, cross = 5)
gene
pred<-predict(gene, promotergene[c(6), -1])

# but the same code fails to work with iris dataset

library("kernlab")
s <- function(x, y) {
 sum((x*y)^1.25)
 }
class(s) <- "kernel"
gene <- ksvm(Species ~ ., data = iris,kernel = s, C = 10, cross = 5)


# above code gives the following error:

Error in votematrix[i, ret < 0] <- votematrix[i, ret < 0] + 1 :
  NAs are not allowed in subscripted assignments

Thank you very much for your time and attention.

Sincerely,
Vivek
Banaras Hindu University,
India.

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