findgacv.scad: Calculate Generalized Approximate Cross Validation Error...

In penalizedSVM: Feature Selection SVM using Penalty Functions

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

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
[e-mail]

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

# 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))

penalizedSVM documentation built on March 31, 2023, 7:51 p.m.