cnFindAIC: Find Network by AIC
In sdnet: Soft-Discretization-Based Bayesian Network Inference

Description Usage Arguments Details Value Author(s) See Also Examples

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

1	cnFindAIC(object, numsamples)

`object`	A `list` of `catNetwork` objects or `catNetworkEvaluate` or `dagEvaluate`
`numsamples`	an `integer`

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.

A catNetwork object with optimal AIC value.

N. Balov

cnFind, cnFindBIC

  cnet <- cnRandomCatnet(numnodes=12, maxpars=3, numcats=2)
  psamples <- cnSamples(object=cnet, numsamples=10)
  nodeOrder <- sample(1:12)
  nets <- cnSearchOrder(data=psamples, pert=NULL, 
	maxParentSet=2, maxComplexity=36, nodeOrder)
  aicnet <- cnFindAIC(object=nets)
  aicnet