Calculate Generalized Approximate Cross Validation Error Estimation for SCAD SVM model

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

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.

Author

Natalia Becker
natalie_becker@gmx.de

Examples


# simulate data
train<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=12)
print(str(train)) 
#> List of 3
#>  $ x   : num [1:100, 1:200] 1.2048 -0.1389 0.864 0.0725 1.4274 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:100] "pos1" "pos2" "pos3" "pos4" ...
#>   .. ..$ : chr [1:200] "1" "2" "3" "4" ...
#>  $ y   : Named num [1:200] 1 1 -1 1 -1 1 1 1 1 -1 ...
#>   ..- attr(*, "names")= chr [1:200] "1" "2" "3" "4" ...
#>  $ seed: num 12
#> NULL
  
# train data  
ff <- scadsvc(as.matrix(t(train$x)), y=train$y, lambda=0.01)
#> [1] "start iterations:"
#> [1] "scad converged in 9 iterations"
print(str(ff))
#> List of 9
#>  $ w      : Named num [1:92] 0.91 1.225 0.88 0.517 1.14 ...
#>   ..- attr(*, "names")= chr [1:92] "pos1" "pos2" "pos3" "pos4" ...
#>  $ b      : num -0.034
#>  $ xind   : int [1:92] 1 2 3 4 5 6 7 8 9 10 ...
#>  $ index  : int [1:85] 6 19 20 25 40 48 49 54 56 57 ...
#>  $ xqx    : num [1:200, 1:200] 110.635 20.309 -14.916 37.297 0.445 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:200] "1" "2" "3" "4" ...
#>   .. ..$ : chr [1:200] "1" "2" "3" "4" ...
#>  $ fitted : num [1:200] 11.29 4.29 -3.86 8.48 -1.53 ...
#>  $ type   : num 0
#>  $ lambda1: num 0.01
#>  $ iter   : num 9
#>  - attr(*, "class")= chr [1:2] "scadsvm" "penSVM"
#> NULL

# estimate gacv error
(gacv<- findgacv.scad(train$y, model=ff))
#> [1] 0