# nnlasso.normal.lambda: Coefficients of non-negative penalized generalized linear... In nnlasso: Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models

## 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

 ```1 2``` ```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

nnlasso documentation built on May 2, 2019, 8:19 a.m.