PnodeLinkScale: Accesses the link function scale parameter associated with a...

In ralmond/Peanut: Parameterized Bayesian Networks, Abstract Classes

Accesses the link function scale parameter associated with a Pnode

Description

In constructing a conditional probability table using the discrete partial credit framework (see calcDPCTable), the effective thetas for each row of the table is converted into a vector of probabilities using the link function. The function PnodeLink accesses the scale parameter of the link function associated with a Pnode.

Usage

PnodeLinkScale(node)
PnodeLinkScale(node) <- value

Arguments

node	A Pnode object.
value	A positive numeric value, or NULL if the scale parameter is not used for the link function.

Details

The link function used in constructing the conditional probability table is controlled by the value of PnodeLink(node). One of the arguments to the link function is a scale parameter, the expression PnodeLinkScale(node) provides the link scale parameter associated with the node.

This is mostly for future expansion. Currently, neither of the two link functions defined in the CPTtools package, partialCredit and gradedResponse, require a link scale parameter. However, the DiBello-normal model (see normalLink) defines the residual variance as a link scale parameter.

Value

The value of the link scale parameter, or NULL if it is not needed.

Note that the setter form may destructively modify the Pnode object (this depends on the implementation).

Note

The functions PnodeLinkScale and PnodeLinkScale<- are abstract generic functions, and need specific implementations. See the PNetica-package for an example. Even though they are not currently used, they must be defined and return a value (even just NULL).

A third normal link function, which would use the scale parameter, is planned but not yet implemented.

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, PnodeQ, PnodeRules, PnodeLinkScale, PnodeLnAlphas, PnodeBetas, BuildTable, PnodeParentTvals, maxCPTParam, calcDPCTable, mapDPC, Compensatory, OffsetConjunctive

Examples

## Not run: 
library(PNetica) ## Requires implementation
sess <- NeticaSession()
startSession(sess)

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

theta1 <- NewDiscreteNode(tNet,"theta1",
                         c("VH","High","Mid","Low","VL"))
PnodeStateValues(theta1) <- effectiveThetas(PnodeNumStates(theta1))
PnodeProbs(theta1) <- rep(1/PnodeNumStates(theta1),PnodeNumStates(theta1))
theta2 <- NewDiscreteNode(tNet,"theta2",
                         c("VH","High","Mid","Low","VL"))
PnodeStateValues(theta2) <- effectiveThetas(PnodeNumStates(theta2))
PnodeProbs(theta2) <- rep(1/PnodeNumStates(theta2),PnodeNumStates(theta2))

partial3 <- NewDiscreteNode(tNet,"partial3",
                            c("FullCredit","PartialCredit","NoCredit"))
PnodeParents(partial3) <- list(theta1,theta2)
partial3 <- Pnode(partial3,rules="Compensatory", link="gradedResponse")
PnodePriorWeight(partial3) <- 10

stopifnot(
  is.null(PnodeLinkScale(partial3))
)

PnodeLinkScale(partial3) <- 1.0

stopifnot(
  all(abs(PnodeLinkScale(partial3)-1)<.0001)
)

DeleteNetwork(tNet)

## End(Not run)

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