R: Convergence plot

plotcoda {BDgraph} | R Documentation

## Convergence plot

### Description

Visualizes the cumulative occupancy fractions of all possible links in the graph. It can be used for monitoring the convergence of the sampling algorithms, BDMCMC and RJMCMC.

### Usage

     plotcoda( bdgraph.obj, thin = NULL, control = TRUE, main = NULL, ... )

### Arguments

`bdgraph.obj` | An object of `S3` class `"bdgraph"`, from function `bdgraph`. It also can be an object of `S3` class `"ssgraph"`, from the function `ssgraph` of `R` package `ssgraph`.
`thin` | An option for getting fast result for a cumulative plot according to part of the iteration.
`control` | Logical: if TRUE (default) and the number of nodes is greater than 15, then 100 links randomly is selected for visualization.
`main` | Graphical parameter (see plot).
`...` | System reserved (no specific usage).

### Details

Note that a spending time for this function depends on the number of nodes. For fast result, you can choose bigger value for the `'thin'` option.

### Author(s)

Reza Mohammadi a.mohammadi@uva.nl

### 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

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138

Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845

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

### See Also

`bdgraph`, `bdgraph.mpl`, `traceplot`

### Examples

    ## Not run:
    # Generating multivariate normal data from a 'circle' graph
    data.sim <- bdgraph.sim( n = 50, p = 6, graph = "circle", vis = TRUE )

    bdgraph.obj <- bdgraph( data = data.sim, iter = 10000, burnin = 0 , save = TRUE )

    plotcoda( bdgraph.obj )

    ## End(Not run)

[Package BDgraph version 2.62 Index]