# beta_update_net: Updates beta coefficients. In LassoNet: 3CoSE Algorithm

## Description

This function updates β for given penalty parameters.

## Usage

 1 beta.update.net(x,y,beta,lambda1,lambda2,M1,n.iter,iscpp,tol) 

## Arguments

 x input data matrix of size n \times p; n - number of observations; p - number of covariates y response vector or size n \times 1 beta initial value for β; default - zero vector of size n \times 1 lambda1 lasso penalty parameter lambda2 network penalty parameter M1 penalty matrix n.iter maximum number of iterations for β step; default - 1e5 iscpp binary choice for using cpp function in coordinate updates; 1 - use C++ (default), 0 - use R tol convergence tolerance level; default - 1e-6

## Details

Updates the coefficient vector β given the data and penalty parameters λ1 and λ2. Convergence criterion is defined as ∑_{i=1}^p |β_{i,j} - β_{i,j-1}| ≤q to.

## Value

 beta updated β vector convergence binary variable; 1 - yes steps number of steps until convergence

## Author(s)

Maintainer: Jonas Striaukas <jonas.striaukas@gmail.com>

## References

Weber, M., Striaukas, J., Schumacher, M., Binder, H. "Network-Constrained Covariate Coefficient and Connection Sign Estimation" (2018) <doi:10.2139/ssrn.3211163>

## Examples

 1 2 3 4 5 6 7 8 9 p<-200 n<-100 beta.0=array(1,c(p,1)) x<-matrix(rnorm(n*p),n,p) y<-rnorm(n,mean=0,sd=1) lambda1<-1 lambda2<-1 M1<-diag(p) updates<-beta.update.net(x, y, beta.0, lambda1, lambda2, M1) 

LassoNet documentation built on Jan. 19, 2020, 5:06 p.m.