Draw from a posterior predictive distribution
Source:R/post_predict.R
SamplePosteriorPredictiveChains.Rd
Simulate samples from a posterior predictive distribution for a feature \(f(g)\) a graph \(g\).
Examples
data <- bnlearn::learning.test
dag <- UniformlySampleDAG(colnames(data))
partitioned_nodes <- DAGtoPartition(dag)
scorer <- CreateScorer(
scorer = BNLearnScorer,
data = data
)
results <- SampleChains(10, partitioned_nodes, PartitionMCMC(), scorer)
dag_chains <- PartitiontoDAG(results, scorer)
# Sample the edge probability.
SamplePosteriorPredictiveChains(dag_chains, function(dag) { return(dag) })
#> [[1]]
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 0 0 0 0 0 1 1 0 1 0 1 0 1
#> [2,] 0 0 0 0 0 1 1 0 1 0 1 0 1
#> [3,] 0 0 0 0 0 1 1 0 1 0 0 0 1
#> [4,] 0 0 0 0 0 1 1 0 0 1 0 0 1
#> [5,] 0 0 0 0 0 1 1 0 0 1 0 0 1
#> [6,] 0 0 0 0 0 1 1 0 0 1 0 0 1
#> [7,] 0 0 0 0 0 1 1 0 0 1 0 0 1
#> [8,] 0 0 0 0 0 0 1 0 0 1 0 0 1
#> [9,] 0 0 0 0 0 0 1 0 0 1 0 0 1
#> [10,] 0 0 0 0 0 0 1 0 0 1 0 0 1
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,] 0 0 1 0 0 1 0 0 0 1 0 1
#> [2,] 0 0 1 0 0 1 0 0 0 1 0 1
#> [3,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [4,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [5,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [6,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [7,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [8,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [9,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [10,] 0 0 1 0 0 1 0 0 0 0 0 0
#> [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
#> [1,] 0 0 0 0 1 0 0 0 0 0 0
#> [2,] 0 0 0 0 1 0 0 0 0 0 0
#> [3,] 1 0 0 0 1 0 0 0 0 0 0
#> [4,] 1 0 0 0 1 0 0 0 0 0 0
#> [5,] 1 0 0 0 1 0 0 0 0 0 0
#> [6,] 1 0 0 0 1 0 0 0 0 0 0
#> [7,] 1 0 0 0 1 0 0 0 0 0 0
#> [8,] 1 0 0 0 1 0 0 0 0 0 0
#> [9,] 1 0 0 0 1 0 0 0 0 0 0
#> [10,] 1 0 0 0 1 0 0 0 0 0 0
#> attr(,"class")
#> [1] "cia_post_chain"
#>
#> [[2]]
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 0 0 0 0 0 0 1 0 1 0 1 0 1
#> [2,] 0 0 0 0 0 0 1 0 1 0 1 0 1
#> [3,] 0 0 0 0 0 0 1 0 0 1 1 0 0
#> [4,] 0 0 0 0 0 0 1 0 0 1 1 0 0
#> [5,] 0 0 0 0 0 1 1 0 0 1 1 0 0
#> [6,] 0 0 0 0 1 0 1 0 0 1 1 0 0
#> [7,] 0 0 0 0 1 0 1 0 0 1 1 0 0
#> [8,] 0 0 0 0 1 0 1 0 0 1 1 0 0
#> [9,] 0 0 0 0 1 0 1 0 0 1 1 0 0
#> [10,] 0 0 0 0 0 0 1 0 0 1 1 0 0
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,] 0 0 1 0 0 1 0 0 0 1 0 1
#> [2,] 0 0 1 0 0 1 0 0 0 1 0 1
#> [3,] 0 0 0 1 0 1 0 1 0 0 0 1
#> [4,] 0 0 0 1 0 1 0 1 0 0 0 1
#> [5,] 0 0 0 1 0 1 0 1 0 0 0 1
#> [6,] 0 0 0 0 1 1 0 1 0 0 0 0
#> [7,] 0 0 0 0 1 1 0 1 0 0 0 0
#> [8,] 0 0 0 0 1 1 0 1 0 0 0 0
#> [9,] 0 0 0 0 1 1 0 1 0 0 0 0
#> [10,] 0 0 0 0 1 1 0 1 0 0 0 1
#> [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
#> [1,] 0 0 0 0 1 1 0 0 0 0 0
#> [2,] 0 0 0 0 1 1 0 0 0 0 0
#> [3,] 0 0 0 0 1 1 0 0 0 0 0
#> [4,] 0 0 0 0 1 1 0 0 0 0 0
#> [5,] 0 0 0 0 1 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 1 0 0 0 1 0
#> [7,] 0 0 0 0 0 1 0 0 0 1 0
#> [8,] 0 0 0 0 0 1 0 0 0 1 0
#> [9,] 0 0 0 0 0 1 0 0 0 1 0
#> [10,] 0 0 0 0 0 1 0 0 0 1 0
#> attr(,"class")
#> [1] "cia_post_chain"
#>
#> attr(,"class")
#> [1] "cia_post_chains"