modselect {heteromixgm} | R Documentation |
modselect
Description
Model selection using the AIC and eBIC.
Usage
modselect(est, X, l1, l2, gamma)
Arguments
est |
Estimates of model obtained from cgmmd() function |
X |
A list of |
l1 |
Vector containing l1 penalty values. |
l2 |
Vector containing l2 penalty values. |
gamma |
EBIC gamma parameter. |
Value
aic_idx |
Index of the estimated model corresponding to the optimal model as per the AIC. |
ebic_idx |
Index of the estimated model corresponding to the optimal model as per the eBIC. |
l1_aic |
Optimal l1 value as per the AIC. |
l2_aic |
Optimal l2 value as per the AIC. |
l1_ebic |
Optimal l1 value as per the eBIC. |
l2_ebic |
Optimal l1 value as per the eBIC. |
Author(s)
Sjoerd Hermes, Joost van Heerwaarden and Pariya Behrouzi
Maintainer: Sjoerd Hermes sjoerd.hermes@wur.nl
References
1. Hermes, S., van Heerwaarden, J., and Behrouzi, P. (2022). Copula graphical
models for heterogeneous mixed data. arXiv preprint, arXiv:2210.13140.
Examples
X <- list(matrix(runif(25), 5, 5),matrix(runif(25), 5, 5),matrix(runif(25),
5, 5))
l1 <- c(0.4)
l2 <- c(0,0.1)
gamma <- 0.5
ncores <- 1
est <- heteromixgm(X, "Approximate", l1, l2, ncores)
modselect(est, X, l1, l2, gamma)