PostProcessChains: Index chains for further analysis

In cia: Learn and Apply Directed Acyclic Graphs for Causal Inference

Index chains for further analysis

Description

This allows you to remove a burnin and thin the chains after processing. This is mostly redundant as you can now index the cia_chain(s) objects directly.

Usage

PostProcessChains(chains, n_burnin = 0, n_thin = 1)

Arguments

chains	cia_chain(s) object.
n_burnin	Number of steps to remove at the start as a burnin. Default is 0.
n_thin	Number of steps between retained states. Default is 1.

Value

A cia_chain(s) object.

Examples

data <- bnlearn::learning.test

dag <- UniformlySampleDAG(colnames(data))
partitioned_nodes <- DAGtoPartition(dag)

scorer <- CreateScorer(
  scorer = BNLearnScorer,
  data = data
  )

results <- SampleChains(100, partitioned_nodes, PartitionMCMC(), scorer)
thinned_results <- PostProcessChains(results, n_thin = 2)

cia documentation built on April 4, 2025, 5:23 a.m.