MultiGlarmaVarSel-package: Variable Selection in Sparse Multivariate GLARMA Models

In MultiGlarmaVarSel: Variable Selection in Sparse Multivariate GLARMA Models

Variable Selection in Sparse Multivariate GLARMA Models

Description

MultiGlarmaVarSel consists of four functions: "variable_selection.R", "grad_hess_L_gamma.R", "grad_hess_L_eta.R", and "NR_gamma.R" For further information on how to use these functions, we refer the reader to the vignette of the package.

Details

This package consists of four functions: "variable_selection.R", "grad_hess_L_gamma.R", "grad_hess_L_eta.R" and "NR_gamma.R" For further information on how to use these functions, we refer the reader to the vignette of the package.

Author(s)

Marina Gomtsyan

Maintainer: Marina Gomtsyan <[e-mail]>

References

M. Gomtsyan et al. "Variable selection in sparse multivariate GLARMA models: Application to germination control by environment", arXiv:2208.14721

Examples

data(Y)
I=3
J=100
T=dim(Y)[2]
q=1
X=matrix(0,nrow=(I*J),ncol=I)
for (i in 1:I)
{
  X[((i-1)*J+1):(i*J),i]=rep(1,J)
}
gamma_0 = matrix(0, nrow = 1, ncol = q)
result=variable_selection(Y, X, gamma_0, k_max=1, 
n_iter=100, method="min", nb_rep_ss=1000, threshold=0.6)
estim_active = result$estim_active
eta_est = result$eta_est
gamma_est = result$gamma_est

MultiGlarmaVarSel documentation built on Sept. 2, 2022, 9:07 a.m.