LassoNet_fixed: Estimates coefficients over the grid values of penalty...
In LassoNet: 3CoSE Algorithm

Description Usage Arguments Details Value Author(s) References Examples

See lasso.net.grid

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

`x`	n \times p input data matrix
`y`	response vector or size n \times 1
`beta.0`	initial value for β; 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 β updating; default - 1e5
`iscpp`	binary choice for using cpp function in coordinate updates; 1 - use C++ (default), 0 - use R.
`tol`	convergence in β tolerance level; default - 1e-6

Function loops through the grid of values of penalty parameters λ1 and λ2 until convergence is reached. Warm starts are stored for each iterator. The warm starts are stored once the coordinate updating converges.

`beta`	Matrix of β coefficients. Columns denote different λ1 coefficients, rows - λ2 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 β updates to converge. (only stores the last values from connection signs iterations)
`convergence.in.grid`	matrix with stored values for convergence in β coefficients. If at least one β did not converge in sign matrix iterations, 0 (false) is stored, otherwise 1 (true)

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

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

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)