findgacv.scad
From penalizedSVM v1.1.2
by Natalia Becker
Calculate Generalized Approximate Cross Validation Error Estimation for SCAD SVM model
calculate generalized approximate cross validation error (GACV) estimation for SCAD SVM model
- Keywords
- models
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
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
Examples
# NOT RUN {
# 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))
# }
Community examples
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