gelnet_lin_opt: GELnet optimizer for linear regression

In ArtemSokolov/gelnet: Generalized Elastic Nets

Description

Constructs a GELnet model for linear regression using coordinate descent.

Usage

gelnet_lin_opt(X, z, l1, l2, max_iter = 100L, eps = 1e-05,
  fix_bias = FALSE, silent = FALSE, verbose = FALSE,
  nonneg = FALSE, w_init = NULL, b_init = NULL, a = NULL,
  d = NULL, P = NULL, m = NULL)

Arguments

X	n-by-p matrix of n samples in p dimensions
z	n-by-1 vector of response values
l1	coefficient for the L1-norm penalty
l2	coefficient for the L2-norm penalty
max_iter	maximum number of iterations
eps	convergence precision
fix_bias	set to TRUE to prevent the bias term from being updated (default: FALSE)
silent	set to TRUE to suppress run-time output; overwrites verbose (default: FALSE)
verbose	set to TRUE to see extra output; is overwritten by silent (default: FALSE)
nonneg	set to TRUE to enforce non-negativity constraints on the weights (default: FALSE )
w_init	initial parameter estimate for the weights
b_init	initial parameter estimate for the bias term
a	n-by-1 vector of sample weights
d	p-by-1 vector of feature weights
P	p-by-p feature association penalty matrix
m	p-by-1 vector of translation coefficients

Details

The method operates through cyclical coordinate descent. The optimization is terminated after the desired tolerance is achieved, or after a maximum number of iterations.

Value

A list with two elements:

w

p-by-1 vector of p model weights

b

scalar, bias term for the linear model

ArtemSokolov/gelnet documentation built on Sept. 13, 2019, 4:01 a.m.