Problems with apply R - r

Question

I Have a problem with using the apply function in R. I made the following function:
TrainSupportVectorMachines <- function(trainingData,kernel,G,C){
####train het model
fit<-svm(Device~.,data=trainingData,kernel=kernel,probability=TRUE,
gamma =G, costs=C)
return(fit);
}
I want to train the model with different values of Cost(c). Therefore, I tried the following commend:
cst = matrix(2^(-4:-2),ncol=3)
kernl = "sigmoid"
fitSVMBP <- apply(cst,2,function(x)TrainSupportVectorMachines(dtr1,kernl,0.625,x))
My opinion is that, fitSVMBP becomes a list with different SVM models with different values for cost. But I get a list with different SVM model but they have all a cost of 1.
Does anybody know what I do wrong?
EDIT:
I use the e1071 package.
And the dataset looks like:
> head(dtr1)
Device Geslacht Leeftijd Invultijd Type Maanden.geleden
1 pc M 45 16.0 A 15
2 pc V 43 27.5 A 3
3 pc V 28 16.0 A 15
4 pc V 17 10.0 A 13
5 pc M 56 16.0 A 15
6 pc M 50 27.5 A 3

Answer 1

You have called the argument costs and not cost. Here's an example using the sample data in ?svm so you can try this:
model <- svm(Species ~ ., data = iris, cost=.6)
model$cost
# [1] 0.6
model <- svm(Species ~ ., data = iris, costs=.6)
model$cost
# [1] 1
R will do partial matching (so in this case cos=.6 would work) but if you overspecify an argument it doesn't match.
Nor will it always complain if you give it an argument it doesn't expect:
> model <- svm(Species ~ ., data = iris, costs=.6, asjkdakjshd=1)
>
Because unmatched args get caught in the ... argument.
If you take this too far, you get:
> model <- svm(Species ~ ., data = iris, c=.122)
Error in svm.default(x, y, scale = scale, ..., na.action = na.action) :
argument 4 matches multiple formal arguments
because c matches cost, coef0, class.weights and cachesize.