R Object Is Not a Matrix

I am new to R and trying to save my svm model in R and have read the documentation but still do not understand what is wrong.

I am getting the error "object is not a matrix" which would seem to mean that my data is not a matrix, but it is... so something is missing.

My data is defined as:

data = read.table("data.csv")
trainSet = as.data.frame(data[,1:(ncol(data)-1)])

Where the last line is my label

I am trying to define my model as:

svm.model <- svm(type ~ ., data=trainSet, type='C-classification', kernel='polynomial',scale=FALSE)

This seems like it should be correct but I am having trouble finding other examples.

Here is my code so far:

# load libraries
require(e1071)
require(pracma)
require(kernlab)
options(warn=-1)

# load dataset
SVMtimes = 1
KERNEL="polynomial"
DEGREE = 2
data = read.table("head.csv")
results10foldAll=c()

# Cross Fold for training and validation datasets
for(timesRun in 1:SVMtimes) {
    cat("Running SVM = ",timesRun," result = ")

    trainSet = as.data.frame(data[,1:(ncol(data)-1)])
    trainClasses = as.factor(data[,ncol(data)])
    model = svm(trainSet, trainClasses, type="C-classification", 
                kernel = KERNEL, degree = DEGREE, coef0=1, cost=1, 
                cachesize = 10000, cross = 10)
    accAll = model$accuracies
    cat(mean(accAll), "/", sd(accAll),"\n")

    results10foldAll = rbind(results10foldAll, c(mean(accAll),sd(accAll)))
 }


# create model

svm.model <- svm(type ~ ., data = trainSet, type='C-classification', kernel='polynomial',scale=FALSE)

An example of one of my samples would be:

10.135338 7.214543 5.758917 6.361316 0.000000 18.455875 14.082668  31