cnSearchHist: Parent Histogram Matrix
In sdnet: Soft-Discretization-Based Bayesian Network Inference

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

Estimation of the parent matrix of nodes from data. The frequency of node edges is obtained by fitting networks consistent to randomly generated node orders.

cnSearchHist(data, pert=NULL, 
	maxParentSet=1, parentSizes=NULL, maxComplexity=0, 
	nodeCats=NULL, parentsPool=NULL, fixedParents=NULL, 
	score = "BIC", weight="loglik", 
	maxIter=32, numThreads=2, echo=FALSE)

`data`	a `matrix` in row-nodes format or a `data.frame` in column-nodes format
`pert`	a binary `matrix` with the dimensions of `data`. A value `1` designates the corresponding node in the sample as perturbed
`maxParentSet`	an `integer`, the maximal number of parents per node
`parentSizes`	an `integer` vector, maximal number of parents per node
`maxComplexity`	an `integer`, the maximal network complexity for the search
`nodeCats`	a `list` of node categories
`parentsPool`	a list of parent sets to choose from
`fixedParents`	a list of parent sets to choose from
`score`	a `character`, network selection score such as "AIC" and "BIC"
`weight`	a `character`, specifies how the
`maxIter`	an `integer`, the number of single order searches to be performed
`numThreads`	an `integer` value, the number of parallel threads
`echo`	a boolean that sets on/off some functional progress and debug information

The function performs niter calls of cnSearchOrder for randomly generated node orders (uniformly over the space of all possible node orders), selects networks according to score and sum their parent matrices weighted by weight. Three scoring criteria are currently supported: "BIC", "AIC" and maximum complexity for any other value of score. The weight can be 1) "likelihhod", then the parent matrices are multiplied by the network likelihood, 1) "score", then the parent matrices are multiplied by the exponential of the network score, 3) any other value of weight uses multiplier 1. In this case the entries in the output matrix count the presence of the corresponding parent-child pairs.

The function can runs numThreads number of parallel threads each processing different order. cnSearchHist function can be useful for empirical estimation of the relationships in some multivariate categorical data.

A matrix

N. Balov

cnMatParents, cnSearchOrder

  library(sdnet)
  cnet <- cnRandomCatnet(numnodes=8, maxpars=3, numcats=2)
  psamples  <- cnSamples(object=cnet, numsamples=100)
  mhisto <- cnSearchHist(data=psamples, pert=NULL, 
		maxParentSet=2, maxComplexity=100)
  mhisto