dantzig.generator: Dantzig data generator

In fastclime: A Fast Solver for Parameterized LP Problems, Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation and Dantzig Selector

Description

Generates sparse linear regression model for testing dantzig function

Usage

dantzig.generator(n = 50, d = 100, sparsity = 0.1, sigma0=1)

Arguments

n	The number of observations (sample size). The default value is 50.
d	The number of variables (dimension). The default value is 100.
sparsity	d is either the number of nonzero entries out of d or the proportion of nonzero entries in BETA
sigma0	sigma0 is the standard deviation of the noise vector

Details

Generates sparse linear regression model for testing dantzig function

Value

An object with S3 class "dantzig.generator" is returned:

X0	X0 is the n by d matrix for the generated data
y	A y is a n response vector for the generated data
BETA	BETA is a length d regression coefficient vector
s	s is the number of nonzero entries out of d
pos	A vector containing the indices of the nonzero entries (may contain repeats)

Author(s)

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

See Also

dantzig

Examples

	
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L = dantzig.generator(n = 50, d = 100, sparsity = 0.1)	

fastclime documentation built on May 2, 2019, 1:06 p.m.