gelnet (version 1.2.1)

gelnet.lin: GELnet for linear regression

Description

Constructs a GELnet model for linear regression using coordinate descent.

Usage

gelnet.lin(X, y, l1, l2, a = rep(1, n), d = rep(1, p), P = diag(p),
  m = rep(0, p), max.iter = 100, eps = 1e-05, w.init = rep(0, p),
  b.init = sum(a * y)/sum(a), fix.bias = FALSE, silent = FALSE)

Arguments

X

n-by-p matrix of n samples in p dimensions

y

n-by-1 vector of response values

l1

coefficient for the L1-norm penalty

l2

coefficient for the L2-norm penalty

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

max.iter

maximum number of iterations

eps

convergence precision

w.init

initial parameter estimate for the weights

b.init

initial parameter estimate for the bias term

fix.bias

set to TRUE to prevent the bias term from being updated (default: FALSE)

silent

set to TRUE to suppress run-time output to stdout (default: FALSE)

Value

• A list with two elements: [object Object],[object Object]

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.