R: Plot function for 'S3' class "'bdgraph'"

plot.bdgraph {BDgraph}

R Documentation

Plot function for `S3` class "`bdgraph`"

Description

Visualizes structure of the selected graphs which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability.

Usage

 ## S3 method for class 'bdgraph'
plot( x, cut = 0.5, number.g = NULL, main = NULL, 
           layout = igraph::layout_with_fr, vertex.size = 2, vertex.color = "orange", 
           vertex.frame.color = "orange", vertex.label = NULL, vertex.label.dist = 0.5, 
           vertex.label.color = "blue", edge.color = "lightblue", ... )

Arguments

`x`	object of `S3` class "`bdgraph`", from function `bdgraph`.
`cut`	threshold for including the links in the selected graph based on the estimated posterior probabilities of the links; See the examples.
`number.g`	number of graphs with the highest probabilities. This option works for the case running function `bdgraph()` with option `save = TRUE`; See the examples.
`main`	Graphical parameter (see plot).
`layout`	vertex placement which is according to `R` package `igraph`; For different layouts, see `layout` of `R` package `igraph`.
`vertex.size`	vertex size which is according to `R` package `igraph`.
`vertex.color`	vertex color which is according to `R` package `igraph`.
`vertex.frame.color`	vertex frame color which is according to `R` package `igraph`.
`vertex.label`	vertex label. The default vertex labels are the vertex ids.
`vertex.label.dist`	vertex label distance which is according to `R` package `igraph`.
`vertex.label.color`	vertex label color which is according to `R` package `igraph`.
`edge.color`	edge color which is according to `R` package `igraph`.
`...`	additional plotting parameters. For the complete list, see `igraph.plotting` of `R` package `igraph`.

Author(s)

Reza Mohammadi a.mohammadi@uva.nl 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, 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

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. 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

Examples

## Not run: 
set.seed( 100 )

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 100, p = 15, graph = "random", prob = 0.2, vis = TRUE )
   
bdgraph.obj <- bdgraph( data = data.sim )
   
plot( bdgraph.obj )
   
bdgraph.obj <- bdgraph( data = data.sim, save = TRUE )
   
plot( bdgraph.obj, cut = 0.5 )

plot( bdgraph.obj, number.g = 4 )

## End(Not run)

[Package BDgraph version 2.65 Index]

Plot function for S3 class "bdgraph"