cnFindBIC: Find Network by BIC

In catnet: Categorical Bayesian Network Inference

Find Network by BIC

Description

This is a model selection routine that finds a network in a set of networks using the BIC criteria.

Usage

 cnFindBIC(object, numsamples)

Arguments

object	A list of catNetworkNode objects or catNetworkEvaluate
numsamples	The number of samples used for estimating object

Details

The function returns the network with maximal BIC value from a list of networks as obtained from one of the search-functions cnSearchOrder, cnSearchSA and cnSearchSAcluster. The formula used for the BIC is log(Likelihood) - 0.5*Complexity*log(numNodes).

Value

A catNetwork object with optimal BIC value.

Author(s)

N. Balov, P. Salzman

See Also

cnFindAIC, cnFind

Examples

  library(catnet)
  cnet <- cnRandomCatnet(numnodes=12, maxParents=3, numCategories=2)
  psamples <- cnSamples(object=cnet, numsamples=10)
  nodeOrder <- sample(1:12)
  nets <- cnSearchOrder(data=psamples, perturbations=NULL, 
	maxParentSet=2, maxComplexity=36, nodeOrder)
  bicnet <- cnFindBIC(object=nets, numsamples=dim(psamples)[2])
  bicnet

catnet documentation built on Nov. 10, 2022, 5:08 p.m.