grad_hess_gamma {GlarmaVarSel} | R Documentation |
Gradient and Hessian of the log-likelihood with respect to gamma
Description
This function calculates the gradient and Hessian of the log-likelihood with respect to gamma
Usage
grad_hess_gamma(Y, X, beta0, gamma0)
Arguments
Y |
Observation matrix |
X |
Design matrix |
beta0 |
Initial beta vector |
gamma0 |
Initial gamma vector |
Value
grad_L_gamma |
Vector of the gradient of L with respect to gamma |
hess_L_gamma |
Matrix of the Hessian of L with respect to gamma |
Author(s)
Marina Gomtsyan, Celine Levy-Leduc, Sarah Ouadah, Laure Sansonnet
Maintainer: Marina Gomtsyan <marina.gomtsyan@agroparistech.fr>
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$coefficients)
result = grad_hess_gamma(Y, X, beta0, gamma0)
grad = result$grad_L_gamma
Hessian = result$hess_L_gamma
[Package GlarmaVarSel version 1.0 Index]