cnNew: New catNetwork

In catnet: Categorical Bayesian Network Inference

New catNetwork

Description

Creates a new catNetwork with specified nodes, categories, parent sets and probability structure.

Usage

cnNew(nodes, cats, parents, probs=NULL, p.delta1=0.01, p.delta2=0.01)

Arguments

nodes	a vector of nodes names
cats	a list of node categories
parents	a list of node parents
probs	a list of probabilities
p.delta1	a numeric
p.delta2	a numeric

Details

If probs is not specified, then a random probability model is assigned with conditional probability values in the union of the intervals [p.delta1, 0.5-p.delta2] and [0.5+p.delta2, 1-p.delta1]. Because of the nested list hierarchy of the probability structure, specifying the probability argument explicitly can be very elaborated task for large networks. In the following example we create a small network with only three nodes. The first node has no parents and only its marginal distribution is given, c(0.2,0.8). Note that all inner most vectors in the probs argument, such as (0.4,0.6), represent conditional distributions and thus sum to 1.

Value

A catNetwork object.

Author(s)

N. Balov, P. Salzman

See Also

catNetwork-class, cnRandomCatnet

Examples

library(catnet)
cnet <- cnNew(
	nodes = c("a", "b", "c"),
	cats = list(c("1","2"), c("1","2"), c("1","2")), 
	parents = list(NULL, c(1), c(1,2)), 
	probs = list(	c(0.2,0.8), 
			list(c(0.6,0.4),c(0.4,0.6)), 
			list(list(c(0.3,0.7),c(0.7,0.3)), 
			list(c(0.9,0.1),c(0.1,0.9))))
	)

catnet documentation built on Nov. 10, 2022, 5:08 p.m.