scadsvc {penalizedSVM} | R Documentation |
SVM with variable selection (clone selection) using SCAD penalty.
scadsvc(lambda = 0.01, x, y, a = 3.7, tol = 10^(-4), class.weights = NULL)
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) |
Adopted from Matlab code: http://www4.stat.ncsu.edu/~hzhang/software.html
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 |
Axel Benner
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.
findgacv.scad
, predict.penSVM
, sim.data
# 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)