quad.int.ineq: Initialize Quadratic Programming Fit with Inequality...

In PACLasso: Penalized and Constrained Lasso Optimization

Description

This function is called internally by lars.ineq to get the quadratic programming fit if the user requests implementation of the algorithm starting at the smallest lambda value and proceeding forwards.

Usage

quad.int.ineq(x, y, C.full, b, lambda, d = 10^-5)

Arguments

x	independent variable matrix of data to be used in calculating PaC coefficient paths
y	response vector of data to be used in calculating PaC coefficient paths
C.full	complete constraint matrix C (with inequality constraints of the form C.full*beta >= b))
b	constraint vector b
lambda	value of lambda
d	very small diagonal term to allow for SVD (default 10^-7)

Value

beta the initial beta vector of coefficients to use for the PaC algorithm

Examples

random_data = generate.data(n = 500, p = 20, m = 10)
quad_start = quad.int.ineq(random_data$x, random_data$y,
random_data$C.full, random_data$b, lambda = 0.01)
quad_start

PACLasso documentation built on May 2, 2019, 2:29 p.m.