Trace plot for graph size for the objects of S3 class "bdgraph", from function bdgraph. It is a tool for monitoring the convergence of the sampling algorithms, BDMCMC and RJMCMC.
traceplot ( bdgraph.obj, acf =FALSE, pacf =FALSE, main =NULL,...)
Arguments
bdgraph.obj: object of S3 class "bdgraph", from function bdgraph. It also can be an object of S3 class "ssgraph", from the function ssgraph::ssgraph() of R package ssgraph::ssgraph().
acf: visualize the autocorrelation functions for graph size.
pacf: visualize the partial autocorrelations for graph size.
main: graphical parameter (see plot).
...: system reserved (no specific usage).
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, 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, tools:::Rd_expr_doi("10.1214/14-BA889")
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, tools:::Rd_expr_doi("10.1214/18-AOAS1164")
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, tools:::Rd_expr_doi("10.1111/rssc.12171")
Mohammadi, A. and Dobra, A. (2017). The R Package BDgraph for Bayesian Structure Learning in Graphical Models, ISBA Bulletin, 24(4):11-16
## Not run:# Generating multivariate normal data from a 'random' graphdata.sim <- bdgraph.sim( n =50, p =6, size =7, vis =TRUE)bdgraph.obj <- bdgraph( data = data.sim, iter =10000, burnin =0, save =TRUE)traceplot( bdgraph.obj )traceplot( bdgraph.obj, acf =TRUE, pacf =TRUE)## End(Not run)