bdgraph: Search algorithm in graphical models

In BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Search algorithm in graphical models

Description

As the main function of the BDgraph package, this function consists of several MCMC sampling algorithms for Bayesian model determination in undirected graphical models. To speed up the computations, the birth-death MCMC sampling algorithms are implemented in parallel using OpenMP in C++.

Usage

bdgraph( data, n = NULL, method = "ggm", algorithm = "bdmcmc", iter = 5000, 
         burnin = iter / 2, not.cont = NULL, g.prior = 0.2, df.prior = 3, 
         g.start = "empty", jump = NULL, save = FALSE, 
         cores = NULL, threshold = 1e-8, verbose = TRUE, nu = 1 )

Arguments

data	there are two options: (1) an (n \times p) matrix or a data.frame corresponding to the data, (2) an (p \times p) covariance matrix as S=X'X which X is the data matrix (n is the sample size and p is the number of variables). It also could be an object of class "sim", from function bdgraph.sim. The input matrix is automatically identified by checking the symmetry.
n	number of observations. It is needed if the "data" is a covariance matrix.
method	character with two options "ggm" (default) and "gcgm". Option "ggm" is for Gaussian graphical models based on Gaussianity assumption. Option "gcgm" is for Gaussian copula graphical models for the data that not follow Gaussianity assumption (e.g. continuous non-Gaussian, count, or mixed dataset).
algorithm	character with two options "bdmcmc" (default) and "rjmcmc". Option "bdmcmc" is based on birth-death MCMC algorithm. Option "rjmcmc" is based on reverible jump MCMC algorithm. Option "bd-dmh" is based on birth-death MCMC algorithm using double Metropolis Hasting. Option "rj-dmh" is based on reverible jump MCMC algorithm using double Metropolis Hasting.
iter	number of iteration for the sampling algorithm.
burnin	number of burn-in iteration for the sampling algorithm.
not.cont	for the case method = "gcgm", a vector with binary values in which 1 indicates not continuous variables.
g.prior	for determining the prior distribution of each edge in the graph. There are two options: a single value between 0 and 1 (e.g. 0.5 as a noninformative prior) or an (p \times p) matrix with elements between 0 and 1.
df.prior	degree of freedom for G-Wishart distribution, W_G(b,D), which is a prior distribution of the precision matrix.
g.start	corresponds to a starting point of the graph. It could be an (p \times p) matrix, "empty" (default), or "full". Option "empty" means the initial graph is an empty graph and "full" means a full graph. It also could be an object with S3 class "bdgraph" of R package BDgraph or the class "ssgraph" of R package ssgraph::ssgraph(); this option can be used to run the sampling algorithm from the last objects of previous run (see examples).
jump	it is only for the BDMCMC algorithm (algorithm = "bdmcmc"). It is for simultaneously updating multiple links at the same time to update graph in the BDMCMC algorithm.
save	logical: if FALSE (default), the adjacency matrices are NOT saved. If TRUE, the adjacency matrices after burn-in are saved.
cores	number of cores to use for parallel execution. The case cores = "all" means all CPU cores to use for parallel execution.
threshold	threshold value for the convergence of sampling algorithm from G-Wishart for the precision matrix.
verbose	logical: if TRUE (default), report/print the MCMC running time.
nu	prior parameter for option method = "tgm".

Value

An object with S3 class "bdgraph" is returned:

p_links	upper triangular matrix which corresponds the estimated posterior probabilities of all possible links.
K_hat	posterior estimation of the precision matrix.

For the case "save = TRUE" is returned:

sample_graphs	vector of strings which includes the adjacency matrices of visited graphs after burn-in.
graph_weights	vector which includes the waiting times of visited graphs after burn-in.
all_graphs	vector which includes the identity of the adjacency matrices for all iterations after burn-in. It is needed for monitoring the convergence of the BD-MCMC algorithm.
all_weights	vector which includes the waiting times for all iterations after burn-in. It is needed for monitoring the convergence of the BD-MCMC algorithm.

Author(s)

Reza Mohammadi [e-mail] and Ernst Wit

References

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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v089.i03")}

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/14-BA889")}

Mohammadi, R., Massam, H. and Letac, G. (2023). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")}

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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssc.12171")}

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/18-AOAS1164")}

Vogels, L., Mohammadi, R., Schoonhoven, M., and Birbil, S.I. (2023) Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison, arXiv preprint, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2307.02603")}

Mohammadi, R., Schoonhoven, M., Vogels, L., and Birbil, S.I. (2023) Large-scale Bayesian Structure Learning for Gaussian Graphical Models using Marginal Pseudo-likelihood, arXiv preprint, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2307.00127")}

Mohammadi, A. and Dobra A. (2017). The R Package BDgraph for Bayesian Structure Learning in Graphical Models, ISBA Bulletin, 24(4):11-16

See Also

bdgraph.mpl, bdgraph.dw, bdgraph.sim, summary.bdgraph, compare

Examples

## Not run: 
set.seed( 10 )

# - - Example 1

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 100, p = 10, size = 15, vis = TRUE )
   
bdgraph.obj <- bdgraph( data = data.sim, iter = 1000, save = TRUE )
  
summary( bdgraph.obj )

# Confusion Matrix
conf.mat( actual = data.sim, pred = bdgraph.obj )

conf.mat.plot( actual = data.sim, pred = bdgraph.obj )
   
# To compare our result with true graph
compare( bdgraph.obj, data.sim, main = c( "Target", "BDgraph" ), vis = T )

# Running algorithm with starting points from previous run
bdgraph.obj2 <- bdgraph( data = data.sim, g.start = bdgraph.obj )
    
compare( list( bdgraph.obj, bdgraph.obj2 ), data.sim, 
         main = c( "Target", "Frist run", "Second run" ) )

# - - Example 2

# Generating mixed data from a 'scale-free' graph
data.sim <- bdgraph.sim( n = 200, p = 7, type = "mixed", graph = "scale-free", vis = TRUE )
   
bdgraph.obj <- bdgraph( data = data.sim, method = "gcgm" )
  
summary( bdgraph.obj )
   
compare( bdgraph.obj, data.sim, vis = T )

conf.mat( actual = data.sim, pred = bdgraph.obj )

conf.mat.plot( actual = data.sim, pred = bdgraph.obj )

## End(Not run)	  

BDgraph documentation built on Sept. 11, 2024, 5:30 p.m.