cnSearchOrder: Network Search for Given Node Order

Description

The function implements a MLE based algorithm to search for optimal networks complying with a given node order. It returns a list of networks, with complexities up to some maximal value, that best fit the data.

Usage

cnSearchOrder(data, perturbations=NULL, 
	maxParentSet=2, maxComplexity=0, nodeOrder=NULL, 
	parentsPool=NULL, fixedParentsPool=NULL, echo=FALSE)

Arguments

data

a matrix in row-nodes format or a data.frame in column-nodes format

perturbations

a binary matrix with the dimensions of data. A value 1 marks that the node in the corresponding sample as perturbed

maxParentSet

an integer, the maximal number of parents per node with default value 2

maxComplexity

an integer, the maximal network complexity for the search

nodeOrder

a vector of node order; the search is among the networks with this topological order

parentsPool

a list of parent sets to choose from

fixedParentsPool

a list of parent sets to choose from

echo

a logical that sets on/off some functional progress and debug information

Value

A catNetworkEvaluate object

Details

The data can be a matrix of character categories with rows specifying the node-variables and columns assumed to be independent samples from an unknown network, or a data.frame with columns specifying the nodes and rows being the samples.

The number of node categories are obtained from the sample. The function returns a list of networks, one for each admissible complexity within the specified range. The networks in the list are the Maximum Likelihood estimates in the class of networks having the given topological order of the nodes and complexity. When maxComplexity is not given, thus zero, its value is reset to the maximum possible complexity for the given parent set size. When nodeOrder is not given or NULL, the order of the nodes in the data is taken, 1,2,....

Parameters parentsPool and fixedParentsPool allow the user to put some exclusion/inclusion constrains on the possible parenthood of the nodes. They should be given as lists of index vectors, one for each node in the data sample.

Examples

Run this code

cnet <- cnRandomCatnet(numnodes=12, maxParents=3, numCategories=2)
  psamples <- cnSamples(object=cnet, numsamples=100)
  nodeOrder <- sample(1:12)
  nets <- cnSearchOrder(data=psamples, perturbations=NULL, 
		maxParentSet=2, maxComplexity=36, nodeOrder)
  ## next we find the network with complexity of the original one and plot it
  cc <- cnComplexity(object=cnet)
  cnFind(object=nets, complexity=cc)