modselect {heteromixgm}

modselect

Description

Model selection using the AIC, BIC and eBIC.

Usage

modselect(est, X, l1, l2, gamma)

Arguments

est

Estimates of model obtained from cgmmd() function

X

A list of K n_k by p data matrices.

l1

Vector containing l1 penalty values.

l2

Vector containing l2 penalty values.

gamma

EBIC gamma parameter.

Value

theta_aic

Estimated precision matrices using the AIC for model selection.

theta_bic

Estimated precision matrices using the BIC for model selection.

theta_ebic

Estimated precision matrices using the EBIC for model selection.

Author(s)

Sjoerd Hermes, Joost van Heerwaarden and Pariya Behrouzi
Maintainer: Sjoerd Hermes [e-mail]

References

1. Hermes, S., van Heerwaarden, J., & Behrouzi, P. (2024). Copula graphical models for heterogeneous mixed data. Journal of Computational and Graphical Statistics, 1-15.

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)