gRain (version 0.3.0)

gmInstance: Graphical Independence Network

Description

The 'newgmInstance' builds a graphical independence network.

Usage

newgmInstance(x, gmData, description="ProbNet",  control=list(), trace=0,...)

Arguments

x

An argument to build an independence network from.

gmData

A gmData object (see Examples below)

description

A text describing the network

control

A list defining controls, see 'details' below.

trace

Debugging information.

...

Additional arguments, currently not used.

Value

• An object of class "gmInstance"

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.

See Also

cpt, ctab, gmData

Examples

## Asia (chest clinique) example - using a gmData object
##
chestNames <- c("asia", "smoke", "tub", "lung", "bronc", "either", "xray", "dysp")
gmd <- newgmData(chestNames,valueLabels=c("yes","no"))
summary(gmd)

p.a    <-cpt('asia', values=c(0.01,0.99),gmData=gmd)
p.t.a  <-cpt('tub',pa='asia', values=c(0.05,0.95,0.01,0.99),gmData=gmd)
p.s    <-cpt('smoke', values=c(0.5,0.5), gmData=gmd)
p.l.s  <-cpt('lung',pa='smoke', values=c(0.1,0.9,0.01,0.99), gmData=gmd)
p.b.s  <-cpt('bronc',pa='smoke', values=c(0.6,0.4,0.3,0.7), gmData=gmd)
p.e.lt <-cpt('either',pa=c('lung','tub'),values=c(1,0,1,0,1,0,0,1),gmData=gmd)
p.x.e  <-cpt('xray',pa='either', values=c(0.98,0.02,0.05,0.95), gmData=gmd)
p.d.be <-cpt('dysp',pa=c('bronc','either'), values=c(0.9,0.1,0.7,0.3,0.8,0.2,0.1,0.9), gmData=gmd)
 
cptlist <- list(p.a, p.t.a, p.s, p.l.s, p.b.s, p.e.lt, p.x.e, p.d.be)
cptlist[[1]]
cptlist[[2]]

bn <- newgmInstance(cptspec(cptlist), gmd)
bn

summary(bn)
plot(bn)


## Asia (chest clinique) example - without using a gmData object
##
yn <- c("yes","no")
a    <- cpt(~asia, values=c(1,99),levels=yn)
t.a  <- cpt(~tub+asia, values=c(5,95,1,99),levels=yn)
s    <- cpt(~smoke, values=c(5,5), levels=yn)
l.s  <- cpt(~lung+smoke, values=c(1,9,1,99), levels=yn)
b.s  <- cpt(~bronc+smoke, values=c(6,4,3,7), levels=yn)
e.lt <- cpt(~either+lung+tub,values=c(1,0,1,0,1,0,0,1),levels=yn)
x.e  <- cpt(~xray+either, values=c(98,2,5,95), levels=yn)
d.be <- cpt(~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 <- newgmInstance(plist)
pn


summary(pn)
plot(pn)


## Create network from gmData (with data) and graph specification.
## There are different ways:
##
data(HairEyeColor)
d   <- as.gmData(HairEyeColor)
dag <- newdagsh(list(~Hair, ~Eye+Hair, ~Sex+Hair))
class(dag)
ug <- newugsh(list(~Eye+Hair, ~Sex+Hair))
class(ug)

## 1) Create directly from dag
b1  <- newgmInstance(dag,d)
class(b1)

## 2) Extract cpt's for dag from gmData and build network from cpt's
x<-dag2cptspec(dag,d)
class(x)
b2 <- newgmInstance(x,d)
class(b2)

## 3) Build model from undirected (decomposable) graph
b3  <- newgmInstance(ug,d)
class(b3)