bdgraph.mpl | R Documentation |
This function consists of several sampling algorithms for Bayesian model determination in undirected graphical models based on mariginal pseudo-likelihood.
To speed up the computations, the birth-death MCMC sampling algorithms are implemented in parallel using OpenMP in C++
.
bdgraph.mpl( data, n = NULL, method = "ggm", transfer = TRUE, algorithm = "bdmcmc", iter = 5000, burnin = iter / 2, g.prior = 0.2, g.start = "empty", jump = NULL, alpha = 0.5, save = FALSE, cores = NULL, operator = "or", verbose = TRUE )
data |
there are two options: (1) an (n \times p) |
n |
number of observations. It is needed if the " |
method |
character with two options " |
transfer |
for only |
algorithm |
character with two options " |
iter |
number of iteration for the sampling algorithm. |
burnin |
number of burn-in iteration for the sampling algorithm. |
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. |
g.start |
corresponds to a starting point of the graph. It could be an (p \times p) matrix, " |
jump |
it is only for the BDMCMC algorithm ( |
alpha |
value of the hyper parameter of Dirichlet, which is a prior distribution. |
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 |
operator |
character with two options " |
verbose |
logical: if TRUE (default), report/print the MCMC running time. |
An object with S3
class "bdgraph
" is returned:
p_links |
upper triangular matrix which corresponds the estimated posterior probabilities of all possible links. |
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. |
Reza Mohammadi a.mohammadi@uva.nl, Adrian Dobra, and Johan Pensar
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, doi: 10.1214/18-AOAS1164
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, doi: 10.1214/14-BA889
Mohammadi, A. and Dobra, A. (2017). The R
Package BDgraph for Bayesian Structure Learning in Graphical Models, ISBA Bulletin, 24(4):11-16
Pensar, J. et al (2017) Marginal pseudo-likelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195-215, doi: 10.1214/16-BA1032
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, doi: 10.18637/jss.v089.i03
bdgraph
, bdgraph.dw
, bdgraph.sim
, summary.bdgraph
, compare
# Generating multivariate normal data from a 'random' graph data.sim <- bdgraph.sim( n = 70, p = 5, size = 7, vis = TRUE ) bdgraph.obj <- bdgraph.mpl( data = data.sim, iter = 500 ) summary( bdgraph.obj ) # To compare the result with true graph compare( bdgraph.obj, data.sim, main = c( "Target", "BDgraph" ) )
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