| Type: | Package |
| Title: | Variable Selection in Sparse GLARMA Models |
| Version: | 1.0 |
| Date: | 2021-09-15 |
| Author: | M. Gomtsyan, C. Levy-Leduc, S. Ouadah, L. Sansonnet |
| Maintainer: | Marina Gomtsyan <marina.gomtsyan@agroparistech.fr> |
| Description: | Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <doi:10.48550/arXiv.2007.08623>. |
| License: | GPL-2 |
| Depends: | R (≥ 3.5.0), Matrix, glmnet, stats, ggplot2 |
| VignetteBuilder: | knitr |
| Suggests: | knitr, markdown, formatR, doMC |
| NeedsCompilation: | no |
| Packaged: | 2021-09-15 15:26:37 UTC; marina |
| Repository: | CRAN |
| Date/Publication: | 2021-09-16 07:40:02 UTC |
Variable Selection in Sparse GLARMA Models
Description
GlarmaVarSel consists of four functions: "variable_selection.R", "grad_hess_beta.R", "grad_hess_gamma.R" and "NR_gamma.R" For further information on how to use these functions, we refer the reader to the vignette of the package.
Details
GlarmaVarSel consists of four functions: "variable_selection.R", "grad_hess_beta.R", "grad_hess_gamma.R" and "NR_gamma.R" For further information on how to use these functions, we refer the reader to the vignette of the package.
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)
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$gamma_est
Newton-Raphson method for estimation of gamma
Description
This function estimates gamma with Newton-Raphson method
Usage
NR_gamma(Y, X, beta0, gamma0, n_iter)
Arguments
Y |
Observation matrix |
X |
Design matrix |
beta0 |
Initial beta vector |
gamma0 |
Initial gamma vector |
n_iter |
Number of iterations of the algorithm. Default=100 |
Value
gamma |
Estimated gamma vector |
Author(s)
Marina Gomtsyan, Celine Levy-Leduc, Sarah Ouadah, Laure Sansonnet
Maintainer: Marina Gomtsyan <marina.gomtsyan@agroparistech.fr>
References
M. Gomtsyan et al. "Variable selection in sparse GLARMA models", arXiv:2007.08623v1
Examples
n=50
p=30
X = matrix(NA,(p+1),n)
f = 1/0.7
for(t in 1:n){X[,t]<-c(1,cos(2*pi*(1:(p/2))*t*f/n),sin(2*pi*(1:(p/2))*t*f/n))}
gamma0 = c(0)
data(Y)
glm_pois<-glm(Y~t(X)[,2:(p+1)],family = poisson)
beta0<-as.numeric(glm_pois$coefficients)
gamma_est = NR_gamma(Y, X, beta0, gamma0, n_iter=100)
Observation matrix Y
Description
An example of observation matrix
Usage
data("Y")Format
The format is: num [1:50] 11 8 3 3 3 4 4 4 3 1 ...
References
M. Gomtsyan et al. "Variable selection in sparse GLARMA models", arXiv:2007.08623v1
Examples
data(Y)
Gradient and Hessian of the log-likelihood with respect to beta
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 <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$coefficients)
result = grad_hess_beta(Y, X, beta0, gamma0)
grad = result$grad_L_beta
Hessian = result$hess_L_beta
Gradient and Hessian of the log-likelihood with respect to gamma
Description
This function calculates the gradient and Hessian of the log-likelihood with respect to gamma
Usage
grad_hess_gamma(Y, X, beta0, gamma0)
Arguments
Y |
Observation matrix |
X |
Design matrix |
beta0 |
Initial beta vector |
gamma0 |
Initial gamma vector |
Value
grad_L_gamma |
Vector of the gradient of L with respect to gamma |
hess_L_gamma |
Matrix of the Hessian of L with respect to gamma |
Author(s)
Marina Gomtsyan, Celine Levy-Leduc, Sarah Ouadah, Laure Sansonnet
Maintainer: Marina Gomtsyan <marina.gomtsyan@agroparistech.fr>
References
M. Gomtsyan et al. "Variable selection in sparse GLARMA models", arXiv:2007.08623v1
Examples
n=50
p=30
X = matrix(NA,(p+1),n)
f = 1/0.7
for(t in 1:n){X[,t]<-c(1,cos(2*pi*(1:(p/2))*t*f/n),sin(2*pi*(1:(p/2))*t*f/n))}
gamma0 = c(0)
data(Y)
glm_pois<-glm(Y~t(X)[,2:(p+1)],family = poisson)
beta0<-as.numeric(glm_pois$coefficients)
result = grad_hess_gamma(Y, X, beta0, gamma0)
grad = result$grad_L_gamma
Hessian = result$hess_L_gamma
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 <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)
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