variable_selection {NBtsVarSel}

Variable selection

Description

This function performs variable selection, estimates new vectors of beta and gamma and a new alpha

Usage

variable_selection(Y, X, gamma.init, alpha.init = NULL, k.max = 1, method = "cv", 
tr = 0.3, n.iter = 100, n.rep = 1000)

Arguments

Y

Observation matrix

X

Design matrix

gamma.init

Initial gamma vector

alpha.init

Optional initial alpha value. The default is NULL

k.max

Number of iteration to repeat the whole 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 "cv"

tr

Threshold for stability selection. The default is 0.3

n.iter

Number of iteration for Newton-Raphson algorithm. The default is 100

n.rep

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

Value

estim_active

Estimated active coefficients

beta_est

Vector of estimated beta values

gamma_est

Vector of estimated gamma values

alpha_est

Estimation of alpha

Author(s)

Marina Gomtsyan

Maintainer: Marina Gomtsyan <[e-mail]>

References

M. Gomtsyan "Variable selection in a specific regression time series of counts.", arXiv:2307.00929

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, gamma.init=gamma0, alpha.init=NULL, k.max=1, method="cv", 
tr=0.3, n.iter=100, n.rep=1000)
beta_est = result$beta_est
Estim_active = result$estim_active
gamma_est = result$gamma_est
alpha_est = result$alpha_est