heteromixgm (version 2.0.2)

data_sim: data_sim

Description

Simulate mixed multi-group data.

Usage

data_sim(network, n, p, K, ncat, rho, gamma_g = NULL, gamma_o, gamma_b = NULL,
gamma_p = NULL, prob = NULL, nclass = NULL)

Value

z

A list of \(K\) \(n\) by \(p\) matrices representing the latent Gaussian transformed (observed) data.

theta

A list of \(K\) \(n\) by \(p\) matrices representing the precision matrices corresponding to the latent Gaussian (unobserved) data.

Arguments

network

Type of network, either "circle", "Random", "Cluster", "Scale-free", "AR1" or "AR2".

n

Number of observations.

p

Number of variables.

K

Number of groups.

ncat

Number of categories for ordinal variables.

rho

Dissimilarity parameter inducing dissimilarity between the K datasets.

gamma_g

Proportion of Gaussian variables in the data.

gamma_o

Proportion of ordinal variables in the data.

gamma_b

Proportion of binomial variables in the data.

gamma_p

Proportion of Poisson variables in the data..

prob

Edge occurency probability in random graph.

nclass

Number of clusters in cluster graph.

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

data_sim(network = "Random", n = 10, p = 50, K = 3, ncat = 6, rho = 0.25,
gamma_o = 0.5, gamma_b = 0.1, gamma_p = 0.2, prob = 0.05)