gRain (version 1.0-3)

grain: Graphical Independence Network

Description

The 'grain' builds a graphical independence network.

Usage

grain(x, data, control=list(), smooth=0, details=0, ...)

Arguments

x

An argument to build an independence network from.

data

An optional data set (currently must be an array/table)

control

A list defining controls, see 'details' below.

smooth

A (usuall small) number to add to the counts of a table if the grain is built from a graph plus a dataset.

details

Debugging information.

...

Additional arguments, currently not used.

Value

• An object of class "grain"

Details

If 'smooth' is non-zero then entries of 'values' which a zero are replaced by the value of 'smooth' - BEFORE any normalization takes place.

References

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

See Also

cptable, setFinding, getFinding, pFinding, retractFinding

Examples

## 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 <- compileCPT(list(a, t.a, s, l.s, b.s, e.lt, x.e, d.be))
pn <- grain(plist)
pn
summary(pn)
plot(pn)
pnc <- compile(pn, propagate=TRUE)

## If we want to query the joint distribution of the disease nodes,
## computations can be speeded up by forcing these nodes to be in
## the same clique of the junction tree:

pnc2 <- compile(pn, root=c("lung", "bronc", "tub"), propagate=TRUE)

system.time({
  for (i in 1:200) 
    querygrain(pnc, nodes=c("lung","bronc", "tub"), type="joint")})
system.time({
  for (i in 1:200) 
    querygrain(pnc2, nodes=c("lung","bronc", "tub"), type="joint")})


## 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)

## Build model from undirected (decomposable) graph
b3  <- grain(uG,d)
class(b3)

## Simple example - one clique only in triangulated graph:
plist <- compileCPT(list(a, t.a))
pn <- grain(plist)
querygrain(pn)

## Simple example - disconnected network:
plist <- compileCPT(list(a, t.a, s))
pn <- grain(plist)
querygrain(pn)