scadsvc {penalizedSVM}R Documentation

Fit SCAD SVM model

Description

SVM with variable selection (clone selection) using SCAD penalty.

Usage

scadsvc(lambda = 0.01, x, y, a = 3.7, tol = 10^(-4), class.weights = NULL)

Arguments

lambda tuning parameter in SCAD function (default : 0.01)
x n-by-d data matrix to train (n chips/patients, d clones/genes)
y vector of class labels -1 or 1's (for n chips/patiens )
a tuning parameter in scad function (default: 3.7)
tol the cut-off value to be taken as 0
class.weights a named vector of weights for the different classes, used for asymetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named. (default: NULL)

Details

Adopted from Matlab code: http://www4.stat.ncsu.edu/~hzhang/software.html

Value

a list of

w coefficients of the hyperplane
b intercept of the hyperplane
xind the index of the selected features (genes) in the data matrix.
index the index of the resulting support vectors in the data matrix.
type type of svm, from svm function
lam.opt optimal lambda
gacv corresponding gacv

Author(s)

Axel Benner

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.

See Also

findgacv.scad, 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    
model <- scadsvc(as.matrix(t(train$x)), y=train$y, lambda=0.01)
print(model)


[Package penalizedSVM version 1.0 Index]