cnSetProb: Set Probability from Data
In sdnet: Soft-Discretization-Based Bayesian Network Inference

Description Usage Arguments Details Value Author(s) Examples

The function sets the probability structure of a network from data according to the Maximum Likelihood criterion.

1	cnSetProb(object, data, pert=NULL, nodeCats=NULL, softmode=FALSE)

`object`	a `catNetwork`
`data`	a data `matrix` or `data.frame`
`pert`	a binary matrix with the dimensions of `data`
`nodeCats`	a `list` of node categories
`softmode`	a `logical`, turns on/off the soft quantization mode

The function generates a new probability table for object and returns an updated catNetwork. The graph structure of the object is kept unchanged.

The data can be a matrix in the node-rows format, or a data.frame in the node-column format. If given, the nodeCats is used as a list of categories. In that case, nodeCats should include the node categories presented in the data.

catNetwork

N. Balov

  cnet <- cnRandomCatnet(numnodes=10, maxpars=3, numcats=3)
  psamples <- matrix(as.integer(1+rbinom(10*100, 2, 0.4)), nrow=10)
  rownames(psamples) <- cnet@nodes
  newcnet <- cnSetProb(object=cnet, data=psamples)