variable_selection {GlarmaVarSel}

Variable selection

Description

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

Usage

variable_selection(Y, X, gamma0, k_max = 2, n_iter = 100, method = "min", 
  nb_rep_ss = 1000, threshold = 0.8, parallel = FALSE, nb.cores = 1)

Arguments

Y

Observation matrix

X

Design matrix

gamma0

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: "fast", "min" or "cv". In "min" the smallest lambda is chosen, in "cv" cross-validation lambda is chosen for stability selection. "fast" is a fater stability selection approach. 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

parallel

Whether to parallelize stability selection step or not. The default is FALSE

nb.cores

Number of cores for parallelization. The default is 1

Value

estim_active

Estimated active coefficients

beta_est

Vector of estimated beta values

gamma_est

Vector of estimated gamma values

Author(s)

Marina Gomtsyan, Celine Levy-Leduc, Sarah Ouadah, Laure Sansonnet

Maintainer: Marina Gomtsyan <[e-mail]>

References

M. Gomtsyan et al. "Variable selection in sparse GLARMA models", arXiv:2007.08623v1

Examples

n=50
p=30
X = matrix(NA,(p+1),n)
f = 1/0.7
for(t in 1:n){X[,t]<-c(1,cos(2*pi*(1:(p/2))*t*f/n),sin(2*pi*(1:(p/2))*t*f/n))}
gamma0 = c(0)
data(Y)
result = variable_selection(Y, X, gamma0, k_max=2, n_iter=100, method="min",
nb_rep_ss=1000, threshold=0.7, parallel=FALSE, nb.cores=1)
beta_est = result$beta_est
Estim_active = result$estim_active
gamma_est = result$gamma_est