scadsvc {penalizedSVM}

Fit SCAD SVM model

Description

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

Usage

scadsvc(lambda1 = 0.01, x, y, a = 3.7, tol= 10^(-4), class.weights= NULL,
 seed=123, maxIter=700, verbose=TRUE)

Arguments

lambda1

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)

seed

seed

maxIter

maximal iteration, default: 700

verbose

verbose, default: TRUE

Details

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

Value

w

coefficients of the hyperplane.

b

intercept of the hyperplane.

xind

the index of the selected features (genes) in the data matrix.

xqx

internal calculations product xqx = 0.5 * x1 * inv_Q * t(x1), see code for more details.

fitted

fit of hyperplane f(x) for all _training_ samples with reduced set of features.

index

the index of the resulting support vectors in the data matrix.

type

type of svm, from svm function.

lambda1

optimal lambda1.

gacv

corresponding gacv.

iter

nuber of iterations.

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(str(model))

print(model)