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

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Description

The function computes regression coefficients for a penalized generalized linear models subject to non-negativity constraints for a given lambda value for response variable following normal distribution.

Usage

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nnlasso.normal.lambda(n,p,x,y,xpx,xpy,beta.old,tau,
lambda1,tol,maxiter,xbeta.old,eps,SE)

Arguments

n

Number of observations

p

Number of predictors.

x

A n by p1 matrix of predictors.

y

A vector of n observations.

xpx

Matrix X'X

xpy

Vector X'y

beta.old

A vector of initial values of beta.

tau

Elastic net paramter. Default is 1

lambda1

The value of lambda

tol

Tolerance criterion. Default is 10^-6

maxiter

Maximum number of iterations. Default is 10000.

xbeta.old

A n by 1 vector of xbeta values.

eps

A small value below which a coefficient would be considered as zero. Default is eps=1e-6

SE

Logical. If SE=TRUE, standard errors of the coefficients will be produced. Default is SE=FALSE

Details

This function is internal and used by nnlasso.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

vcov

Variance-covariance matrix of the coefficient estimates

Author(s)

Baidya Nath Mandal and Jun Ma

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