cnNew: New catNetwork
In sdnet: Soft-Discretization-Based Bayesian Network Inference

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

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

1	cnNew(nodes, cats, pars, probs=NULL, p.delta1=0.01, p.delta2=0.01, dagonly=FALSE)

`nodes`	a `vector` of nodes names
`cats`	a `list` of node categories
`pars`	a `list` of node pars
`probs`	a `list` of probabilities
`p.delta1`	a `numeric`
`p.delta2`	a `numeric`
`dagonly`	a `logical`, selects between catNetwork and DAG

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 pars 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.

A catNetwork object.

N. Balov

catNetwork-class, cnRandomCatnet

cnet <- cnNew(
	nodes = c("a", "b", "c"),
	cats = list(c("1","2"), c("1","2"), c("1","2")), 
	pars = 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))))
	)