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

Description Usage Arguments Details Value Author(s)

View source: R/extlasso.R

Description

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

Usage

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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 2, 2019, 11:39 a.m.

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