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

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 Poisson distribution.

Usage

1
2
nnlasso.poisson.lambda(n,p,sumy,beta0.old,beta1.old,
x,y,dxkx0,tau,lambda1,tol,maxiter,xbeta.old,mu1,eps,SE)

Arguments

n

Number of observations

p

Number of 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

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

mu1

The value of mu at beta.old

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.poisson 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

vcov

Variance-covariance matrix of the coefficient estimates

Author(s)

Baidya Nath Mandal and Jun Ma

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.