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

 extlasso.norm.lambda R Documentation

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

### Description

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

### Usage

```extlasso.norm.lambda(n,p,p1,x,y,xpx,dxpx,xpy,beta.old,
tau,alpha,lambda1,tol,maxiter,eps,xbeta.old)```

### Arguments

 `n` Number of observations `p` Number of predictors. `p1` Number of active predictors `x` A n by p1 matrix of predictors. `y` A vector of n observations. `xpx` Matrix X'X `dxpx` Diagonals of X'X `xpy` Vector X'y `beta.old` A vector of initial values of beta. `tau` Elastic net paramter. Default is 1 `alpha` Approximation to be used for absolute value. Default is 10^-6. `lambda1` The value of lambda `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.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

### Author(s)

B N Mandal and Jun Ma

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