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

Description Usage Arguments Details Value Author(s)

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.

1 2	nnlasso.normal.lambda(n,p,x,y,xpx,xpy,beta.old,tau, lambda1,tol,maxiter,xbeta.old,eps,SE)

`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

This function is internal and used by nnlasso.normal function. User need not call this function.

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