# 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))
# }
```

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

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