fastclime (version 1.4.1.1)

dantzig: A solver for the Dantzig selector estimator

Description

Implementation of the Primal Dual (i.e. Self Dual) Simplex Method on Dantzig selector

Usage

dantzig(X, y, lambda = 0.01, nlambda = 50)

Value

An object with S3 class "dantzig" is returned:

X

X is the n by d data matrix.

y

y is a length n response vector.

BETA0

BETA0 is a d by validn matrix where each column has an estimated regression coefficient vector given a lambda interval.

n0

n0 is the number of rows in the n by d data matrix.

d0

d0 is the number of columns in the n by d data matrix.

validn

validn is the number of solutions along the solution path. The maximum is nlambda.

lambdalist

lambdalist is the decrementing path of the lambda solution values.

Arguments

X

x is an n by d data matrix

y

y is a length n response vector

lambda

The parametric simplex method will stop when the calculated parameter is smaller than lambda. The default value is 0.01.

nlambda

This is the number of the maximum path length one would like to achieve. The default length is 50.

Author

Haotian Pang, Han Liu, Robert Vanderbei and Di Qi
Maintainer: Haotian Pang<[e-mail]>

Details

This program applies the parametric simplex linear programming method to the Dantzig selector to solve for the regression coefficient vector. The solution path of the problem corresponds to the parameter in the parametric simplex method.

See Also

dantzig.selector

Examples

#generate data
a = dantzig.generator(n = 200, d = 100, sparsity = 0.1)

#regression coefficient estimation
b = dantzig(a$X0, a$y, lambda = 0.1, nlambda = 100)