heteromixgm {heteromixgm}

heteromixgm

Description

This function implements either the Gibbs or approximation method within the Gaussian copula graphical model to estimate the conditional expectation for the data that not follow Gaussianity assumption (e.g. ordinal, discrete, continuous non-Gaussian, or mixed dataset).

Usage

heteromixgm(X, method, lambda1, lambda2, ncores)

Arguments

X

A list containing K n_k \times p matrices (K is the number of groups, n_k is the sample size for group k and p is the number of variables

method

Choice between "Gibbs" and "Approximate" indicating which method to use.

lambda1

Vector containing values (in [0,1]) for the sparsity penalization of each \Theta^k.

lambda2

Vector containing values (in [0,1]) for the similarity penalization between the \Theta^k.

ncores

Number of cores to be used during parallel computing.

Value

Z

New transformation of the data based on given or default Sigma.

ES

Expectation of covariance matrix( diagonal scaled to 1) of the Gaussian copula graphical model.

Sigma

The covariance matrix of the latent variable given the data.

Theta

The inverse covariance matrix of the latent variable given the data.

loglik

Value of the Log likelihood under the estimated parameters.

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

data(maize)
l1 <- c(0.4)
l2 <- c(0,0.1)
ncores <- 1
est <- heteromixgm(maize, "Approximate", l1, l2, ncores)