Error with custom SVM model for tuning in caret - r

Question

I'm trying to follow this link to create a custom SVM and run it through some cross-validations. My primary reason for this is to run Sigma, Cost and Epsilon parameters in my grid-search and the closest caret model (svmRadial) can only do two of those.
When I attempt to run the code below, I get the following error all over the place at every iteration of my grid:
Warning in eval(expr, envir, enclos) :
model fit failed for Fold1.: sigma=0.2, C=2, epsilon=0.1 Error in if (!isS4(modelFit) & !(method$label %in% c("Ensemble Partial Least Squares Regression", :
argument is of length zero
Even when I replicate the code from the link verbatim, I get a similar error and I'm not sure how to solve it. I found this link which goes through how the custom models are built and I see where this error is referenced, but still not sure what the issue is. I have my code below:
#Generate Tuning Criteria across Parameters
C <- c(1,2)
sigma <- c(0.1,.2)
epsilon <- c(0.1,.2)
grid <- data.frame(C,sigma)
#Parameters
prm <- data.frame(parameter = c("C", "sigma","epsilon"),
class = rep("numeric", 3),
label = c("Cost", "Sigma", "Epsilon"))
#Tuning Grid
svmGrid <- function(x, y, len = NULL) {
expand.grid(sigma = sigma,
C = C,
epsilon = epsilon)
}
#Fit Element Function
svmFit <- function(x, y, wts, param, lev, last, weights, classProbs, ...) {
ksvm(x = as.matrix(x), y = y,
type = "eps-svr",
kernel = rbfdot,
kpar = list(sigma = param$sigma),
C = param$C,
epsilon = param$epsilon,
prob.model = classProbs,
...)
}
#Predict Element Function
svmPred <- function(modelFit, newdata, preProc = NULL, submodels = NULL)
predict(modelFit, newdata)
#Sort Element Function
svmSort <- function(x) x[order(x$C),]
#Model
newSVM <- list(type="Regression",
library="kernlab",
loop = NULL,
parameters = prm,
grid = svmGrid,
fit = svmFit,
predict = svmPred,
prob = NULL,
sort = svmSort,
levels = NULL)
#Train
tc<-trainControl("repeatedcv",number=2, repeats = 0,
verboseIter = T,savePredictions=T)
svmCV <- train(
Y~ 1
+ X1
+ X2
,data = data_nn,
method=newSVM,
trControl=tc
,preProc = c("center","scale"))
svmCV

Answer 1

After viewing the second link provided, I decided to try and include a label into the Model's parameters and that solved the issue! It's funny that it worked because the caret documentation says that value is optional, but if it works I can't complain.
#Model
newSVM <- list(label="My Model",
type="Regression",
library="kernlab",
loop = NULL,
parameters = prm,
grid = svmGrid,
fit = svmFit,
predict = svmPred,
prob = NULL,
sort = svmSort,
levels = NULL)