BayesNetBP (version 1.2.1)

ComputeKLDs: Compute signed and symmetric Kullback-Leibler divergence

Description

Compute signed and symmetric Kullback-Leibler divergence of variables over a spectrum of evidence

Usage

ComputeKLDs(tree, var0, vars, seq, pbar = TRUE)

Arguments

tree

a object

var0

the variable to have evidence absrobed

vars

the variables to have divergence computed

seq

a vector of numeric values as the evidences

pbar

logical(1) whether to show progress bar

Value

a data.frame of the divergence

Details

Compute signed and symmetric Kullback-Leibler divergence of variables over a spectrum of evidence. The signed and symmetric Kullback-Leibler divergence is also known as Jeffery's signed information (JSI) for continuous variables.

Examples

data(liver)
cst <- ClusterTreeCompile(dag=liver$dag, node.class=liver$node.class)
models <- LocalModelCompile(data=liver$data, dag=liver$dag, node.class=liver$node.class)
tree.init <- ElimTreeInitialize(tree=cst$tree.graph, 
                                dag=cst$dag, 
                                model=models, 
                                node.sets=cst$cluster.sets, 
                                node.class=cst$node.class)
tree.init.p <- PropagateDBN(tree.init)
klds <- ComputeKLDs(tree=tree.init.p, var0="Nr1i3", 
                    vars=setdiff(tree.init.p@node, "Nr1i3"),
                    seq=seq(-3,3,0.5))
head(klds)