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

Description Usage Arguments Details Value Author(s)

View source: R/extlasso.R

Description

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

Usage

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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 2, 2019, 11:39 a.m.