# fl.lambda: Coefficients of fused lasso penalized regression for a given... In extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty

 fl.lambda R Documentation

## Coefficients of fused lasso penalized regression for a given pair of lambda1 and lambda2 values

### Description

The function computes regression coefficients for a fused lasso penalized regression model for a given pair of lambda1 and lambda2 values.

### Usage

```fl.lambda(n,p,x,y,xpx,dxpx,xpy,beta.old,ofv.old,alpha,
lambda1,lambda2,tol,maxiter,eps,xbeta.old)```

### Arguments

 `n` Number of observations `p` Number of predictors. `x` A n by l matrix of predictors. Here n is number of observations, l is number of active variables. `y` a vector of n observations. `xpx` The X'X matrix `dxpx` A vector of order l of diagonal elements of x'x `xpy` A vector of order l containing x'y `beta.old` A vector initial values of beta. Optional `ofv.old` Objective function value at beta.old `alpha` Approximation to be used for absolute value. Default is 10^-6. `lambda1` The value of lambda1 `lambda2` The value of lambda2 `tol` Tolerance criterion. Default is 10^-7 `maxiter` Maximum number of iterations. Default is 100000. `eps` Value for which beta is set to zero if -eps

### Details

This function is internal and used by fusedlasso 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

### Author(s)

B N Mandal and Jun Ma

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