fl.lambda: Coefficients of fused lasso penalized regression for a given...

In extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty

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<beta<eps. Default is 10^-6
xbeta.old	A n by 1 vector of xbeta values. Optional

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.