findgacv.scad

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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

scadsvc, predict.penSVM, sim.data

Aliases
  • findgacv.scad
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))

# }
Documentation reproduced from package penalizedSVM, version 1.1.2, License: GPL (>= 2)

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