grad_hess_beta: Gradient and Hessian of the log-likelihood with respect to...

In GlarmaVarSel: Variable Selection in Sparse GLARMA Models

Description

This function calculates the gradient and Hessian of the log-likelihood with respect to beta.

Usage

grad_hess_beta(Y, X, beta0, gamma0)

Arguments

Y	Observation matrix
X	Design matrix
beta0	Initial beta vector
gamma0	Initial gamma vector

Value

grad_L_beta	Vector of the gradient of L with respect to beta
hess_L_beta	Matrix of the Hessian of L with respect to beta

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)
glm_pois<-glm(Y~t(X)[,2:(p+1)],family = poisson)
beta0<-as.numeric(glm_pois$coefficients)
result = grad_hess_beta(Y, X, beta0, gamma0)
grad = result$grad_L_beta
Hessian = result$hess_L_beta

GlarmaVarSel documentation built on Sept. 16, 2021, 9:07 a.m.