variable_selection function

Variable selection

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

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

Returns

• estim_active: Vector of stimated active coefficients

• eta_est: Vector of estimated eta values

• gamma_est: Vector of estimated gamma values

References

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

Author(s)

Marina Gomtsyan

Maintainer: Marina Gomtsyan marina.gomtsyan@agroparistech.fr

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