bdgraph.sim {BDgraph} | R Documentation |
Simulating multivariate distributions with different types of underlying graph structures, including
"random"
, "cluster"
, "scale-free"
, "lattice"
, "hub"
, "star"
, "circle"
, "AR(1)"
, and "AR(2)"
.
Based on the underling graph structure, it generates four different types of datasets, including multivariate Gaussian, non-Gaussian, discrete, or mixed data.
This function can be used also for only simulating graphs by option n=0
, as a default.
bdgraph.sim( p = 10, graph = "random", n = 0, type = "Gaussian", prob = 0.2, size = NULL, mean = 0, class = NULL, cut = 4, b = 3, D = diag( p ), K = NULL, sigma = NULL, vis = FALSE )
p |
The number of variables (nodes). |
graph |
The graph structure with options
|
n |
The number of samples required. Note that for the case |
type |
Type of data with four options |
prob |
If |
size |
The number of links in the true graph (graph size). |
mean |
A vector specifies the mean of the variables. |
class |
If |
cut |
If |
b |
The degree of freedom for G-Wishart distribution, W_G(b, D). |
D |
The positive definite (p \times p) "scale" matrix for G-Wishart distribution, W_G(b, D). The default is an identity matrix. |
K |
If |
sigma |
If |
vis |
Visualize the true graph structure. |
An object with S3
class "sim"
is returned:
data |
Generated data as an (n x p) matrix. |
sigma |
The covariance matrix of the generated data. |
K |
The precision matrix of the generated data. |
G |
The adjacency matrix corresponding to the true graph structure. |
Reza Mohammadi a.mohammadi@uva.nl and Ernst Wit
Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R
Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):1-30
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C, 66(3):629-645
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Pensar, J. et al (2017) Marginal pseudo-likelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195-215
graph.sim
, bdgraph
, bdgraph.mpl
## Not run: # Generating multivariate normal data from a 'random' graph data.sim <- bdgraph.sim( p = 10, n = 50, prob = 0.3, vis = TRUE ) print( data.sim ) # Generating multivariate normal data from a 'hub' graph data.sim <- bdgraph.sim( p = 6, n = 3, graph = "hub", vis = FALSE ) round( data.sim $ data, 2 ) # Generating mixed data from a 'hub' graph data.sim <- bdgraph.sim( p = 8, n = 10, graph = "hub", type = "mixed" ) round( data.sim $ data, 2 ) # Generating only a 'scale-free' graph (with no data) graph.sim <- bdgraph.sim( p = 8, graph = "scale-free" ) plot( graph.sim ) graph.sim $ G ## End(Not run)