nnlasso.binomial.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 binomial distribution.

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

`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 p 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

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

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