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I am interested in fitting a SVM model to my data with Elastic SCAD penalty. I was trying to use the penalizedSVM library for this. The issue is that for some reason, the library outputs a null model and I am not sure where I am going wrong with the coding.

I will include a minimal working example to show what I mean.

library("penalizedSVM")

X=matrix(c(3, 2, 4, 1, 2, 4, 4, 4, 2, 4, 4, 1, 3, 1),nrow=7 )
Y=c(-1, -1, -1, -1, 1, 1, 1)

#Note this is linearly separable
plot(X[,1], X[,2],  pch=16, col=Y+2)

#penalty weights
lambda.grid1=list(10^seq(-2,1))
lambda.grid2=lambda.grid1

scadfit1=svmfs(X,Y, fs.method="scad+L2", grid.search="discrete",
               lambda1.set = lambda.grid1 , lambda2.set = lambda.grid2, 
               bounds=NULL, parms.coding = "none", inner.val.method = "cv", cross.inner =10, show="none", calc.class.weights = TRUE, class.weights = NULL, seed=1)

#returns null
scadfit1

#is scalar
scadfit1$model$w

Additionally, the w vector is scalar for some reason. If anyone has tried this method or could help me understand what I am doing wrong, I would really appreciate it.

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