PnodeParentTvals: Fetches a list of numeric variables corresponding to parent...

In ralmond/PNetica: Parameterized Bayesian Networks Netica Interface

Fetches a list of numeric variables corresponding to parent states

Description

In constructing a conditional probability table using the discrete partial credit framework (see calcDPCTable), each state of each parent variable is mapped onto a real value called the effective theta. The PnodeParentTvals method for Netica nodes returns the result of applying NodeLevels to each of the nodes in NodeParents(node).

Usage

## S4 method for signature 'NeticaNode'
PnodeParentTvals(node)

Arguments

node	A Pnode which is also a NeticaNode.

Details

While the best practices for assigning values to the states of the parent nodes is probably to assign equal spaced values (using the function effectiveThetas for this purpose), this method needs to retain some flexibility for other possibilities. However, in general, the best choice should depend on the meaning of the parent variable, and the same values should be used everywhere the parent variable occurs.

Netica already provides the NodeLevels function which allows the states of a NeticaNode to be associated with numeric values. This method merely gathers them together. The method assumes that all of the parent variables have had their NodeLevels set and will generate an error if that is not true.

Value

PnodeParentTvals(node) should return a list corresponding to the parents of node, and each element should be a numeric vector corresponding to the states of the appropriate parent variable. If there are no parent variables, this will be a list of no elements.

Note

The implementation is merely: lapply(NodeParents(node), NodeLevels).

Author(s)

Russell Almond

References

Almond, R. G. (2015) An IRT-based Parameterization for Conditional Probability Tables. Paper presented at the 2015 Bayesian Application Workshop at the Uncertainty in Artificial Intelligence Conference.

Almond, R.G., Mislevy, R.J., Steinberg, L.S., Williamson, D.M. and Yan, D. (2015) Bayesian Networks in Educational Assessment. Springer. Chapter 8.

See Also

Pnode.NeticaNode, Pnode, effectiveThetas, BuildTable,NeticaNode-method, maxCPTParam,NeticaNode-method

Examples

sess <- NeticaSession()
startSession(sess)
tNet <- CreateNetwork("TestNet", session=sess)

theta1 <- NewDiscreteNode(tNet,"theta1",
                         c("VH","High","Mid","Low","VL"))
## This next function sets the effective thetas for theta1
NodeLevels(theta1) <- effectiveThetas(NodeNumStates(theta1))
NodeProbs(theta1) <- rep(1/NodeNumStates(theta1),NodeNumStates(theta1))
theta2 <- NewDiscreteNode(tNet,"theta2",
                         c("High","Mid","Low"))
## This next function sets the effective thetas for theta2
NodeLevels(theta2) <- effectiveThetas(NodeNumStates(theta2))
NodeProbs(theta2) <- rep(1/NodeNumStates(theta2),NodeNumStates(theta2))

partial3 <- NewDiscreteNode(tNet,"partial3",
                            c("FullCredit","PartialCredit","NoCredit"))
NodeParents(partial3) <- list(theta1,theta2)

## Usual way to set rules is in constructor
partial3 <- Pnode(partial3,rules="Compensatory", link="partialCredit")

PnodeParentTvals(partial3)
do.call("expand.grid",PnodeParentTvals(partial3))

DeleteNetwork(tNet)
stopSession(sess)

ralmond/PNetica documentation built on Sept. 19, 2023, 8:27 a.m.