extlasso.norm.lambda: Coefficients of penalized generalized linear models for a...

In extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty

Coefficients of penalized generalized linear models for a given lambda for normal family

Description

The function computes regression coefficients for a penalized generalized linear models for a given lambda value for response variable following normal distribution.

Usage

extlasso.norm.lambda(n,p,p1,x,y,xpx,dxpx,xpy,beta.old, 
tau,alpha,lambda1,tol,maxiter,eps,xbeta.old)

Arguments

n	Number of observations
p	Number of predictors.
p1	Number of active predictors
x	A n by p1 matrix of predictors.
y	A vector of n observations.
xpx	Matrix X'X
dxpx	Diagonals of X'X
xpy	Vector X'y
beta.old	A vector of initial values of beta.
tau	Elastic net paramter. Default is 1
alpha	Approximation to be used for absolute value. Default is 10^-6.
lambda1	The value of lambda
tol	Tolerance criterion. Default is 10^-6
maxiter	Maximum number of iterations. Default is 10000.
eps	value for which beta is set to zero if -eps<beta<eps. Default is 10^-6
xbeta.old	A n by 1 vector of xbeta values.

Details

This function is internal and used by extlasso.normal function. User need not call this function.

Value

A list with following components

beta.new	Coefficient estimates
conv	"yes" means converged and "no" means did not converge
iter	Number of iterations to estimate the coefficients
ofv.new	Objective function value at solution
xbeta.new	xbeta values at solution

Author(s)

B N Mandal and Jun Ma

extlasso documentation built on May 13, 2022, 9:08 a.m.