cnFind-method {catnet}R Documentation

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

[Package catnet version 1.15.7 Index]