lpsvm {penalizedSVM}R Documentation

Fit L1-norm SVM

Description

SVM mit variable selection (clone selection) using L1-norm penalty. ( a fast Newton algorithm NLPSVM from Fung and Mangasarian )

Usage

lpsvm(A, d, k = 5, nu = 0, output = 1, delta = 10^-3, epsi = 10^-4, my.seed = 123)

Arguments

A n-by-d data matrix to train (n chips/patients, d clones/genes)
d vector of class labels -1 or 1's (for n chips/patiens )
k k-fold for cv, default k=5
nu weighted parameter, 1 - easy estimation, 0 - hard estimation, any other value - used as nu by the algorithm. default : 0
output 0 - no output, 1 - produce output, default is 0
delta some small value, default 10^-3
epsi tuning parameter
my.seed seed

Details

k: k-fold for cv, is a way to divide the data set into test and training set.
if k = 0: simply run the algorithm without any correctness calculation, this is the default.
if k = 1: run the algorithm and calculate correctness on the whole data set.
if k = any value less than the number of rows in the data set: divide up the data set into test and training using k-fold method.
if k = number of rows in the data set: use the 'leave one out' (loo) method

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.
epsi optimal tuning parameter epsilon
iter number of iterations
k k-fold for cv
trainCorr for cv: average train correctness
testCorr for cv: average test correctness
nu weighted parameter

Note

Adapted from MATLAB code http://www.cs.wisc.edu/dmi/svm/lpsvm/

Author(s)

Natalia Becker

References

Fung, G. and Mangasarian, O. L. (2004). A feature selection newton method for support vector machine classification. Computational Optimization and Applications Journal 28(2) pp. 185-202.

See Also

sim.data

Examples


train<-sim.data(n = 20, ng = 100, nsg = 10, corr=FALSE, seed=12)
print(str(train)) 
        
# train data    
model <- lpsvm(A=t(train$x), d=train$y, k=5, nu=0,output=0, delta=10^-3, epsi=0.001, my.seed=12)
print(model)


[Package penalizedSVM version 1.0 Index]