Peanut-package: Parameterized Bayesian Networks, Abstract Classes

In ralmond/Peanut: Parameterized Bayesian Networks, Abstract Classes

Parameterized Bayesian Networks, Abstract Classes

Description

This provides support of learning conditional probability tables parameterized using CPTtools. This provides and object oriented layer on top of a CPTtools, to facilitate calculations with Parameterized models for Bayesian networks. Peanut is a collection of abstract classes and generic functions defining a protocol, with the intent that the protocol can be implemented with different Bayes net engines. The companion pacakge PNetica provides an implementation using Netica and RNetica.

Details

The DESCRIPTION file: This package was not yet installed at build time.

Peanut (a corruption of Parameterized network or Pnet) is an object oriented layer on top of the tools for constructing conditional probability tables for Bayesian networks in the CPTtools package. In particular, it defines a Pnode (parameterized node) object which stores all of the arguments necessary to use to the calcDPCTable function to build the conditional probability table for the node.

The Pnet object is a Bayesian network containing a number of Pnodes. It supports two key operations, BuildAllTables which sets the values of the conditional probabilities based on current parameters and GEMfit which adjusts the parameters to match a set of cases.

Like the DBI package, this class consists mostly of generic functions which need to be implement for specific Bayes net implementations. The package PNetica provides an implementation of the Peanut generic functions using the RNetica package. All of the Netica-dependent code is isolated in the PNetica package, to make it easier to create other implementations.

Index

Index: This package was not yet installed at build time.

Author(s)

Russell Almond

Maintainer: Russell Almond <[e-mail]>

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., Yan, D. and Williamson, D. M. (2015) Bayesian Networks in Educational Assessment. Springer. (ISBN 978-1-4939-2124-9).

See Also

PNetica

An implementation of the Peanut object model using RNetica.

CPTtools

A collection of implementation independent Bayes net utilities.

Examples

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

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


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

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

partial3 <- Pnode(partial3,Q=TRUE, link="partialCredit")
PnodePriorWeight(partial3) <- 10
BuildTable(partial3)

## Set up so that first skill only needed for first transition, second
## skill for second transition; adjust alphas to match
PnodeQ(partial3) <- matrix(c(TRUE,TRUE,
                             TRUE,FALSE), 2,2, byrow=TRUE)
PnodeLnAlphas(partial3) <- list(FullCredit=c(-.25,.25),
                                PartialCredit=0)
BuildTable(partial3)
partial4 <- NewDiscreteNode(tNet,"partial4",
                            c("Score4","Score3","Score2","Score1"))
NodeParents(partial4) <- list(theta1,theta2)
partial4 <- Pnode(partial4, link="partialCredit")
PnodePriorWeight(partial4) <- 10

## Skill 1 used for first transition, Skill 2 used for second
## transition, both skills used for the 3rd.

PnodeQ(partial4) <- matrix(c(TRUE,TRUE,
                             FALSE,TRUE,
                             TRUE,FALSE), 3,2, byrow=TRUE)
PnodeLnAlphas(partial4) <- list(Score4=c(.25,.25),
                                Score3=0,
                                Score2=-.25)
BuildTable(partial4)

## Fitting Model to data

irt10.base <- ReadNetworks(system.file("testnets", "IRT10.2PL.base.dne",
                                       package="PNetica"),
                           session=sess)
irt10.base <- as.Pnet(irt10.base)  ## Flag as Pnet, fields already set.
irt10.theta <- PnetFindNode(irt10.base,"theta")
irt10.items <- PnetPnodes(irt10.base)
## Flag items as Pnodes
for (i in 1:length(irt10.items)) {
  irt10.items[[i]] <- as.Pnode(irt10.items[[i]])
  ## Add node to list of observed nodes
  PnodeLabels(irt10.items[[1]]) <-
     union(PnodeLabels(irt10.items[[1]]),"onodes")

}


casepath <- system.file("testdat", "IRT10.2PL.200.items.cas", package="PNetica")


BuildAllTables(irt10.base)
PnetCompile(irt10.base) ## Netica requirement

item1 <- irt10.items[[1]]
priB <- PnodeBetas(item1)
priA <- PnodeAlphas(item1)
priCPT <- NodeProbs(item1)

gemout <- GEMfit(irt10.base,casepath)


DeleteNetwork(irt10.base)
DeleteNetwork(tNet)
stopSession(sess)

## End(Not run)

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