| bf {BDgraph} | R Documentation |
Compute the Bayes factor between the structure of two graphs.
bf( num, den, bdgraph.obj, log = TRUE )
num,
den |
adjacency matrix corresponding to the true graph structure in which a_{ij}=1 if there is a link between notes i and j, otherwise a_{ij}=0.
It can be an object with |
bdgraph.obj |
object of |
log |
character value. If TRUE the Bayes factor is given as log(BF). |
single numeric value, the Bayes factor of the two graph structures num and den.
Reza Mohammadi a.mohammadi@uva.nl
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
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, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, 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, 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, doi: 10.1214/18-AOAS1164
bdgraph, bdgraph.mpl, compare, bdgraph.sim
## Not run:
# Generating multivariate normal data from a 'circle' graph
data.sim <- bdgraph.sim( n = 50, p = 6, graph = "circle", vis = TRUE )
# Running sampling algorithm
bdgraph.obj <- bdgraph( data = data.sim )
graph_1 <- graph.sim( p = 6, vis = TRUE )
graph_2 <- graph.sim( p = 6, vis = TRUE )
bf( num = graph_1, den = graph_2, bdgraph.obj = bdgraph.obj )
## End(Not run)