extlasso.binom.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 binomial family

Description

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

Usage

extlasso.binom.lambda(n,p,p1,sumy,beta0.old,beta1.old,x,y, 
dxkx0,dxkx1,tau,lambda1,alpha,tol,maxiter,eps,xbeta.old,mu1)

Arguments

n	Number of observations
p	Number of predictors
p1	Number of active predictors
sumy	Sum of y values
beta0.old	Initial value of intercept
beta1.old	A vector of initial values of slope coefficients
x	A n by p1 matrix of predictors
y	A vector of n observations
dxkx0	In case of a model with intercept, first diagonal of X'X
dxkx1	Diagonals of X'X
tau	Elastic net paramter. Default is 1
lambda1	The value of lambda
alpha	Approximation to be used for absolute value. Default is 10^-6
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
mu1	The value of mu at beta.old

Details

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

Value

A list with following components

beta0.new	Intercept estimate
beta1.new	Slope 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
mu1	Value of mu at solution

Author(s)

B N Mandal and Jun Ma

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