lasso.net.fixed {LassoNet}

Estimates coefficients over the grid values of penalty parameters.

Description

See lasso.net.grid

Usage

lasso.net.fixed(x,y,beta.0,lambda1,lambda2,M1,n.iter,iscpp,tol)

Arguments

x

n \times p input data matrix

y

response vector or size n \times 1

beta.0

initial value for \beta; default - zero vector of size n \times 1

lambda1

lasso penalty coefficient

lambda2

network penalty coefficient

M1

penalty matrix

n.iter

maximum number of iterations for \beta updating; default - 1e5

iscpp

binary choice for using cpp function in coordinate updates; 1 - use C++ (default), 0 - use R.

tol

convergence in \beta tolerance level; default - 1e-6

Details

Function loops through the grid of values of penalty parameters \lambda1 and \lambda2 until convergence is reached. Warm starts are stored for each iterator. The warm starts are stored once the coordinate updating converges.

Value

beta

Matrix of \beta coefficients. Columns denote different \lambda1 coefficients, rows - \lambda2 coefficients

mse

Mean squared error value

iterations

matrix with stored number of steps for sign matrix to converge

update.steps

matrix with stored number of steps for \beta updates to converge. (only stores the last values from connection signs iterations)

convergence.in.grid

matrix with stored values for convergence in \beta coefficients. If at least one \beta did not converge in sign matrix iterations, 0 (false) is stored, otherwise 1 (true)

Author(s)

Maintainer: Jonas Striaukas <[e-mail]>

References

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

Examples

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=c(0,1)
lambda2=c(0,1)
M1=diag(p)
lasso.net.fixed(x, y, beta.0, lambda1, lambda2, M1)