cnFindAIC: Find Network by AIC

In catnet: Categorical Bayesian Network Inference

Find Network by AIC

Description

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

Usage

 cnFindAIC(object, numsamples)

Arguments

object	A list of catNetwork objects or catNetworkEvaluate
numsamples	an integer

Details

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

Value

A catNetwork object with optimal AIC value.

Author(s)

N. Balov, P. Salzman

See Also

cnFind, cnFindBIC

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)
  aicnet <- cnFindAIC(object=nets)
  aicnet

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