# extlasso.binom.lambda: Coefficients of penalized generalized linear models for a... In extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty

 extlasso.binom.lambda R Documentation

## Coefficients of penalized generalized linear models for a given lambda for binomial family

### Description

The function computes regression coefficients for a penalized generalized linear models for a given lambda value for response variable following binomial distribution.

### Usage

```extlasso.binom.lambda(n,p,p1,sumy,beta0.old,beta1.old,x,y,
dxkx0,dxkx1,tau,lambda1,alpha,tol,maxiter,eps,xbeta.old,mu1)```

### Arguments

 `n` Number of observations `p` Number of predictors `p1` Number of active 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 `dxkx1` Diagonals of X'X `tau` Elastic net paramter. Default is 1 `lambda1` The value of lambda `alpha` Approximation to be used for absolute value. Default is 10^-6 `tol` Tolerance criterion. Default is 10^-6 `maxiter` Maximum number of iterations. Default is 10000 `eps` value for which beta is set to zero if -eps

### Details

This function is internal and used by extlasso.binomial 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

### Author(s)

B N Mandal and Jun Ma

extlasso documentation built on May 13, 2022, 9:08 a.m.