cnFind: Find Network by Complexity

In catnet: Categorical Bayesian Network Inference

Find Network by Complexity

Description

This is a model selection routine that finds a network in a set of networks for a given complexity.

Usage

 cnFind(object, complexity = 0, alpha=0, factor=1)
 cnFindKL(object, numsamples)

Arguments

object	catNetworkEvaluate or list of catNetworks
complexity	an integer, target complexity
alpha	a character or numeric
factor	a numeric
numsamples	an integer

Details

The complexity must be at least the number of nodes of the networks. If no network with the requested complexity exists in the list, then the one with the closest complexity is returned. Alternatively, one can apply some standard model selection with alpha="BIC" and alpha=AIC.

Value

A catNetwork object.

Author(s)

N. Balov, P. Salzman

See Also

cnFindAIC, cnFindBIC

Examples

  cnet <- cnRandomCatnet(numnodes=10, maxParents=2, numCategories=2)
  psamples <- cnSamples(object=cnet, numsamples=100)
  netlist <- cnSearchOrder(data=psamples, maxParentSet=2)
  bnet <- cnFind(object=netlist, complexity=cnComplexity(cnet))
  bnet

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