fl.lambda

A n by l matrix of predictors. Here n is number of observations, l is number of active variables.

A vector of order l of diagonal elements of x'x

dxpx

A vector of order l containing x'y

A vector initial values of beta. Optional

beta.old

Objective function value at beta.old

ofv.old

Approximation to be used for absolute value. Default is 10^-6.

alpha

lambda1

lambda2

Tolerance criterion. Default is 10^-7

Maximum number of iterations. Default is 100000.

maxiter

Value for which beta is set to zero if -eps&lt;beta&lt;eps. Default is 10^-6

A n by 1 vector of xbeta values. Optional

xbeta.old

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

internal

Estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. This package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. This package also contains functions for cross validation to select a suitable lambda value given the data. Also provides a function for estimation in fused lasso penalized linear regression. For more details, see Mandal, B. N.(2014). Computational methods for L1 penalized GLM model fitting, unpublished report submitted to Macquarie University, NSW, Australia.

fl.lambda: Coefficients of fused lasso penalized regression for a given pair of lambda1 and lambda2 values

Description

Usage

Arguments

Value

Details