quad.int.ineq {PACLasso}

Initialize Quadratic Programming Fit with Inequality Constraints

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