bdgraph.sim: Synthetic graph data generator

Description

Implements a synthetic graph data generation for multivariate distributions with different types of underlying graph structures, including "random", "cluster", "scale-free", "hub", "fixed", and "circle". Based on the underling graph structure, it generates four different types of datasets, including multivariate Gaussian, non-Gaussian, discrete, or mixed data.

Usage

bdgraph.sim( n = 2, p = 10, type = "Gaussian", graph = "random", prob = 0.2, 
			 size = NULL, mean = 0, class = NULL, cut = 4, b = 3, 
			 D = diag(p), K = NULL, sigma = NULL, vis = FALSE )

Arguments

The number of samples required. The default value is 2.

The number of variables (nodes). The default value is 10.

type

Type of data with four options "Gaussian" (as a default), "non-Gaussian", "discrete", and "mixed". For option "Gaussian", data are generated from multivariate normal distribution. Fo

graph

The graph structure with option "random" (default), "cluster", "scale-free", "hub", "fixed", and "circle". It also could be an adjacency matrix corresponding to a graph s

prob

If graph="random", it is the probability that a pair of nodes has a link. The default value is 0.2.

size

The number of links in the true graph (graph size).

mean

A vector specifies the mean of the variables. The default value is a zero vector.

class

If graph="cluster", it is the number of classes.

cut

If type="discrete", it is the number of categories for simulating discrete data. The default value is 4.

The degree of freedom for G-Wishart distribution, $W_G(b, D)$. The default is 3.

The positive definite $(p \times p)$ "scale" matrix for G-Wishart distribution, $W_G(b, D)$. The default is an identity matrix.

If graph="fixed", it is a positive-definite symmetric matrix specifies as a true precision matrix.

sigma

If graph="fixed", it is a positive-definite symmetric matrix specifies as a true covariance matrix.

vis

Visualize the true graph structure. The default value is FALSE.

Value

An object with S3 class "sim" is returned:
dataGenerated data as a ($n \times p$) matrix.
sigmaThe covariance matrix of the generated data.
KThe precision matrix of the generated data.
GThe adjacency matrix corresponding to the true graph structure.

References

Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138 Mohammadi, A. and E. Wit (2015). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, arXiv:1501.05108 Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2015). Bayesian Gaussian Copula Graphical Modeling for Dupuytren Disease, arXiv:1501.04849

Examples

Run this code

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 10, prob = 0.3, vis = TRUE )
print( data.sim )
   
# Generating multivariate normal data from a 'hub' graph
data.sim <- bdgraph.sim( n = 3, p = 6, graph = "hub", vis = FALSE )
round( data.sim $ data, 2 )
   
# Generating mixed data from a 'hub' graph 
data.sim <- bdgraph.sim( n = 10, p = 8, graph = "hub", type = "mixed" )
round( data.sim $ data, 2 )