Calculate Generalized Approximate Cross Validation Error Estimation for SCAD SVM model

Description

calculate generalized approximate cross validation error (GACV) estimation for SCAD SVM model

Usage

1
findgacv.scad(y, model)

Arguments

y

vector of class labels (only for 2 classes)

model

list, describing SCAD SVM model, produced by function scadsvc

Value

returns the GACV value

Author(s)

Natalia Becker natalia.becker@dkfz.de

References

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.

See Also

scadsvc, predict.penSVM, sim.data

Examples

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# 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(str(ff))

# estimate gacv error
(gacv<- findgacv.scad(train$y, model=ff))