variable_selection {MultiGlarmaVarSel}

Variable selection

Description

This function performs variable selection, estimates a new vector eta and a new vector gamma

Usage

variable_selection(Y, X, gamma, k_max = 1, n_iter = 100, 
method = "min", nb_rep_ss = 1000, threshold = 0.6)

Arguments

Y

Observation matrix

X

Design matrix

gamma

Initial gamma vector

k_max

Number of iteration to repeat the whole algorithm

n_iter

Number of iteration for Newton-Raphson algorithm

method

Stability selection method: "min" or "cv". In "min" the smallest lambda is chosen, in "cv" cross-validation lambda is chosen for stability selection. The default is "min"

nb_rep_ss

Number of replications in stability selection step. The default is 1000

threshold

Threshold for stability selection. The default is 0.9

Value

estim_active

Vector of stimated active coefficients

eta_est

Vector of estimated eta values

gamma_est

Vector of estimated gamma values

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