findgacv.scad {penalizedSVM} | R Documentation |
calculate generalized approximate cross validation error (GACV) estimation for SCAD SVM Model
findgacv.scad(y, model)
y |
vector of class labels (only for 2 classes) |
model |
list, describing SCAD SVM model, produced by function scadsvc |
returns the GACV value
Natalia Becker natalia.becker@dkfz.de
Zhang, H. H., Ahn, J., Lin, X. and Park, C. (2006). Gene selection using support vector machines with nonconvex penalty. Bioinformatics, 22, pp. 88-95.
Wahba G., Lin, Y. and Zhang, H. (2000). GACV for support vector machines, or, another way to look at margin-like quantities, in A. J. Smola, P. Bartlett, B. Schoelkopf and D. Schurmans (eds), Advances in Large Margin Classifiers, MIT Press, pp. 297-309.
scadsvc
, predict.penSVM
, sim.data
# simulate data train<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=12) print(str(train)) # train data ff <- scadsvc(as.matrix(t(train$x)), y=train$y, lambda=0.01) print(ff) # estimate gacv error (gacv<- findgacv.scad(train$y, model=ff))