predict.bdgraph {BDgraph} | R Documentation |
Predict function for S3
class "bdgraph
"
Description
Provides predict values of the results for function bdgraph
.
Usage
## S3 method for class 'bdgraph'
predict( object, iter = 1, ... )
Arguments
object |
object of |
iter |
number of predictions. |
... |
additional parameters. |
Value
a matrix
containing the predicted datasets, corresponding to the samples from the joint posterior disribtuion.
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
See Also
bdgraph
, bdgraph.mpl
, bdgraph.dw
Examples
## Not run:
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )
bdgraph.obj <- bdgraph( data = data.sim )
predict( bdgraph.obj, iter = 20 )
## End(Not run)