heteromixgm (version 2.0.2)

modselect: modselect

Description

Model selection using the AIC, BIC and eBIC.

Usage

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

Value

selectmat

Matrix containing the "optimal" l1 and l2 values for each information criterion.

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.

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.

Author

Sjoerd Hermes, Joost van Heerwaarden and Pariya Behrouzi
Maintainer: Sjoerd Hermes

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

# \donttest{
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)
# }