grain {gRain} | R Documentation |
The 'grain' builds a graphical independence network.
grain(x, data, description="grain", control=list(), trace=0, ...)
x |
An argument to build an independence network from. |
data |
An optional data set (currently must be an array/table) |
description |
A text describing the network |
control |
A list defining controls, see 'details' below. |
trace |
Debugging information. |
... |
Additional arguments, currently not used. |
If 'smooth' is non-zero then entries of 'values' which a zero are replaced by the value of 'smooth' - BEFORE any normalization takes place.
An object of class "grain"
Søren Højsgaard, sorenh@agrsci.dk
cptable
,
setFinding
,
getFinding
,
pFinding
,
retractFinding
,
gmData
## Asia (chest clinique) example ## 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) plist <- cptspec(list(a, t.a, s, l.s, b.s, e.lt, x.e, d.be)) pn <- grain(plist) pn summary(pn) plot(pn) ## Create network from gmData (with data) and graph specification. ## There are different ways: ## data(HairEyeColor) d <- HairEyeColor dag <- dagList(list(~Hair, ~Eye+Hair, ~Sex+Hair)) class(dag) ug <- ugList(list(~Eye+Hair, ~Sex+Hair)) class(ug) ## Create directly from dag ## b1 <- grain(dag,d) class(b1) ## 3) Build model from undirected (decomposable) graph b3 <- grain(ug,d) class(b3) ## Simple example - one clique only in triangulated graph ## plist <- cptspec(list(a, t.a)) pn <- grain(plist) querygrain(pn) ## Simple example - disconnected network ## plist <- cptspec(list(a, t.a, s)) pn <- grain(plist) querygrain(pn)