Performing SVM regression with Test and training sets in R - r

Question

I have a response variable contains 100 observation and I wish to estimate them by using 8 independent variables via employing supper Vector Regression.
I have searched a lot to find a template in order to implement my SVR with training and testing sets in R, but I could not find the way which I wanted.
I have used the following code to fit the model and calculate RMSE, but I want to check my model for unseen data and I do not know how to perform this in R.
My code is as follows:
data<-read.csv("Enzyme.csv",header = T)
Testset <- data[c(11:30),]
Trainset <- data[-c(11:30), ]
#attached dependent variable
Y<-Trainset$Urease
Trainset<-Trainset[,-c(1)]
SVMUr <- svm (Urease~., data=Trainset, kernel="radial",gamma=
1,epsilon=seq(0,1,0.1), cost=10)
summary(SVMUr)
################### RMSE SVMUr ##########################
RMSE <- function(observed, predicted){
sqrt(mean((predicted - observed)^2, na.rm=TRUE))
}
RMSE(observed =Y,predicted = predSVMUr)
######## Check the model for unseen data via using testset ######
predicted_test <- predict(SVMUr, Testset[,-1])
RMSE(Testset$Urease, predicted_test)

Answer 1

The way you want to go about testing your model is to:
First apply your model on unseen data using predict(SVMUr, Testset[,-1]) assuming the first variable is your target response Y. If it is the 15th variable for example, replace -1 with -15.
Now use the RMSE() function to get the RMSE of the model on your test dataset
Additional Recommendation:
I would not split the data the way you do because as you've pointed out you end up with too little training data in relation to test data. If you want to split it by 80%-20%, you can adjust from my code below:
data<-read.csv("Enzyme.csv",header = T)
split_data <- sample(nrow(data), nrow(data)*0.8)
Trainset <- data[split_data, ]
Testset <- data[-split_data, ]
That would put 80% of your data in the train set and 20% in the test set.
The rest of the code:
SVMUr <- svm (Urease~., data=Trainset, kernel="radial",gamma=
1,epsilon=seq(0,1,0.1), cost=10)
summary(SVMUr)
################### RMSE SVMUr ##########################
RMSE <- function(observed, predicted){
sqrt(mean((predicted - observed)^2, na.rm=TRUE))
}
RMSE(observed =Trainset$Urease, predicted = predSVMUr)
######## Check the model for unseen data via using testset ######
predicted_test <- predict(SVMUr, Testset[,-1])
RMSE(Testset$Urease, predicted_test)