grain_propagate: Propagate a graphical independence network (a Bayesian...
In DataSciBurgoon/gRain: Graphical Independence Networks

Description Usage Arguments Details Value Author(s) References See Also Examples

Propagation refers to calibrating the cliques of the junction tree so that the clique potentials are consistent on their intersections; refer to the reference below for details.

## S3 method for class 'grain'
propagate(object, details = object$details, engine = "cpp", ...)

propagateLS(cqpotList, rip, initialize = TRUE, details = 0)

`object`	A grain object
`details`	For debugging info
`engine`	Either "R" or "cpp"; "cpp" is the default and the fastest.
`...`	Currently not used
`cqpotList`	Clique potential list
`rip`	A rip ordering
`initialize`	Always true

The propagate method invokes propagateLS which is a pure R implementation of the Lauritzen-Spiegelhalter algorithm. The c++ based version is several times faster than the purely R based version.

A compiled and propagated grain object.

Søren Højsgaard, sorenh@math.aau.dk

Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. http://www.jstatsoft.org/v46/i10/.

grain, compile

yn   <- c("yes","no")
a    <- cptable(~asia,              values=c(1,99), levels=yn)
t.a  <- cptable(~tub+asia,          values=c(5,95,1,99), levels=yn)
s    <- cptable(~smoke,             values=c(5,5), levels=yn)
l.s  <- cptable(~lung+smoke,        values=c(1,9,1,99), levels=yn)
b.s  <- cptable(~bronc+smoke,       values=c(6,4,3,7), levels=yn)
e.lt <- cptable(~either+lung+tub,   values=c(1,0,1,0,1,0,0,1), levels=yn)
x.e  <- cptable(~xray+either,       values=c(98,2,5,95), levels=yn)
d.be <- cptable(~dysp+bronc+either, values=c(9,1,7,3,8,2,1,9), levels=yn)
chest.cpt <- compileCPT(list(a, t.a, s, l.s, b.s, e.lt, x.e, d.be))
chest.bn  <- grain(chest.cpt)
bn1  <- compile(chest.bn, propagate=FALSE)
bn2  <- propagate(bn1)
bn3  <- compile(chest.bn, propagate=TRUE)