calcExpTables: Calculate expected tables for a Pnet.NeticaBN
| calcExpTables.NeticaBN | R Documentation |
Calculate expected tables for a Pnet.NeticaBN
Description
The performs the E-step of the GEM algorithm by running the Netica
EM algorithm (see LearnCPTs) using the data in
cases. After this is run, the conditional probability table
for each Pnode.NeticaNode should be the mean of the
Dirichlet distribution and the scale parameter should be the value of
NodeExperience(node).
Usage
## S4 method for signature 'NeticaBN'
calcExpTables(net, cases, Estepit = 1,
tol = sqrt(.Machine$double.eps))
Arguments
net |
A |
cases |
A character scalar giving the file name of a Netica case file
(see |
Estepit |
An integer scalar describing the number of steps the Netica should take in the internal EM algorithm. |
tol |
A numeric scalar giving the stopping tolerance for the internal Netica EM algorithm. |
Details
The key to this method is realizing that the EM algorithm built into
the Netica (see LearnCPTs) can perform the
E-step of the outer GEMfit generalized EM
algorithm. It does this in every iteration of the algorithm, so one
can stop after the first iteration of the internal EM algorithm.
This method expects the cases argument to be a pathname
pointing to a Netica cases file containing the training or test data (see
write.CaseFile). Also, it expects that there
is a nodeset (see NetworkNodesInSet) attached
to the network called “onodes” which
references the observable variables in the case file.
Before calling this method, the function BuildTable
needs to be called on each Pnode to both ensure that the
conditional probability table is at a value reflecting the current
parameters and to reset the value of
NodeExperience(node) to the starting value
of GetPriorWeight(node).
Note that Netica does allow
NodeExperience(node) to have a different value
for each row the the conditional probability table. However, in this
case, each node must have its own prior weight (or exactly the same
number of parents). The prior weight counts as a number of cases, and
should be scaled appropriately for the number of cases in cases.
The parameters Estepit and tol are passed
LearnCPTs. Note that the outer EM
algorithm assumes that the expected table counts given the current
values of the parameters, so the default value of one is sufficient.
(It is possible that a higher value will speed up convergence, the
parameter is left open for experimentation.) The tolerance is largely
irrelevant as the outer EM algorithm does the tolerance test.
Value
The net argument is returned invisibly.
As a side effect, the internal conditional probability tables in the network are updated as are the internal weights given to each row of the conditional probability tables.
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.
See Also
Pnet, Pnet.NeticaBN,
GEMfit, calcPnetLLike,
maxAllTableParams,
calcExpTables,
NetworkNodesInSet
write.CaseFile,
LearnCPTs
Examples
sess <- NeticaSession()
startSession(sess)
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 <- NetworkFindNode(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]])
}
CompileNetwork(irt10.base) ## Netica requirement
casepath <- system.file("testdat","IRT10.2PL.200.items.cas",
package="PNetica")
## Record which nodes in the casefile we should pay attention to
NetworkNodesInSet(irt10.base,"onodes") <-
NetworkNodesInSet(irt10.base,"observables")
item1 <- irt10.items[[1]]
priorcounts <- sweep(NodeProbs(item1),1,NodeExperience(item1),"*")
calcExpTables(irt10.base,casepath)
postcounts <- sweep(NodeProbs(item1),1,NodeExperience(item1),"*")
## Posterior row sums should always be larger.
stopifnot(
all(apply(postcounts,1,sum) >= apply(priorcounts,1,sum))
)
DeleteNetwork(irt10.base)
stopSession(sess)
