gRain (version 1.3-0)

grain-main: Graphical Independence Network

Description

The 'grain' builds a graphical independence network.

Usage

grain(x, data = NULL, control = list(), smooth = 0, details = 0, ...)
# S3 method for CPTspec
grain(x, data = NULL, control = list(), smooth = 0,
  details = 0, ...)
# S3 method for POTspec
grain(x, data = NULL, control = list(), smooth = 0,
  details = 0, ...)
# S3 method for graphNEL
grain(x, data = NULL, control = list(), smooth = 0,
  details = 0, ...)
# S3 method for dModel
grain(x, data = NULL, control = list(), smooth = 0,
  details = 0, ...)
is.grain(object)

Arguments

x

An argument to build an independence network from. Typically a list of conditional probability tables, a DAG or an undirected graph. In the two latter cases, data must also be provided.

data

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

control

A list defining controls, see 'details' below.

smooth

A (usually 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.

object

Any R object.

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<f8>ren H<f8>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, compile.grain, propagate.grain, setFinding, setEvidence, getFinding, pFinding, retractFinding

Examples

# NOT RUN {
## Asia (chest clinic) 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))
bn    <- grain(plist)
bn
summary(bn)
plot(bn)
bnc <- compile(bn, 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:

bnc2 <- compile(bn, root=c("lung", "bronc", "tub"), propagate=TRUE)

system.time({
  for (i in 1:200)
    querygrain(bnc, nodes=c("lung","bronc", "tub"), type="joint")})
system.time({
  for (i in 1:200)
    querygrain(bnc2, nodes=c("lung","bronc", "tub"), type="joint")})


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

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


## Create network from data and graph specification.
## There are different ways:
data(HairEyeColor)
hec <- HairEyeColor
daG <- dag( ~Hair + Eye:Hair + Sex:Hair )
class( daG )
uG <- ug( ~Eye:Hair + Sex:Hair )
class( uG )

## Create directly from dag:
b1  <- grain( daG, hec )
class( b1 )

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

# }