LassoNet (version 0.8.3)

lasso.net.fixed: 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

Value

beta

Matrix of \(\beta\) coefficients. Columns denote different \(\lambda\)1 coefficients, rows - \(\lambda\)2 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)

Details

Function loops through the grid of values of penalty parameters \(\lambda\)1 and \(\lambda\)2 until convergence is reached. Warm starts are stored for each iterator. The warm starts are stored once the coordinate updating converges.

References

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

Examples

# NOT RUN {
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)
# }