cnSearchSAcluster: Stochastic Network Search. Parallel Processing.

Description

This function provides a MLE based network search in the space of node orders by Simulated Annealing and also employing parallel processing.

Usage

cnSearchSAcluster(data, perturbations, 
	maxParentSet, maxComplexity=0, 
	parentsPool=NULL, fixedParentsPool=NULL, 
	selectMode = "BIC", 
	tempStart=1, tempCoolFact=0.9, tempCheckOrders=10, maxIter=200, 
	orderShuffles=1, stopDiff=0, 
	priorSearch = NULL, 
	clusterNodes=2, clusterHost="localhost", 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 designates the node in the corresponding sample as perturbed

maxParentSet

an integer, maximal number of parents per node

maxComplexity

an integer, maximal network complexity for the search

parentsPool

a list of parent sets to choose from

fixedParentsPool

a list of parent sets to choose from

selectMode

a character, optimization network selection criterion such as "AIC" and "BIC"

tempStart

a numerical value, the initial temperature for the annealing

tempCoolFact

a numerical value, the temperature multiplicative decreasing factor

tempCheckOrders

an integer, the number of iteration with constant temperature

maxIter

an integer, the total number of iterations, thus orders, to be searched

orderShuffles

an integer, the number of order shuffles per iteration with 0 indicating random order at each iteration

stopDiff

a numerical value, stopping epsilon criterion

priorSearch

a catNetworkEvaluate object from a previous search

clusterNodes

an integer, the number of processing units to be used

clusterHost

IP address of the cluster host; 'localhost' as default

echo

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

Value

A catNetworkEvaluate object.

Details

This is essentially the parallel version of cnSearchSA.

Examples

Run this code

cnet <- cnRandomCatnet(numnodes=12, maxParents=3, numCategories=2)
  psamples  <- cnSamples(object=cnet, numsamples=100)
  nets <- cnSearchSAcluster(data=psamples, perturbations=NULL, 
		maxParentSet=2, maxComplexity=30)
  cnFind(object=nets, complexity=30)