grain-main: Graphical Independence Network

In DataSciBurgoon/gRain: Graphical Independence Networks

Description

Creating grain objects (graphical independence network).

Usage

grain(x, ...)

## S3 method for class 'cpt_spec'
grain(x, control = list(), smooth = 0, compile = TRUE, details = 0, ...)

## S3 method for class 'CPTspec'
grain(x, control = list(), smooth = 0, compile = TRUE, details = 0, ...)

## S3 method for class 'pot_spec'
grain(x, control = list(), smooth = 0, compile = TRUE, details = 0, ...)

## S3 method for class 'graphNEL'
grain(
  x,
  control = list(),
  smooth = 0,
  compile = TRUE,
  details = 0,
  data = NULL,
  ...
)

## S3 method for class 'dModel'
grain(
  x,
  control = list(),
  smooth = 0,
  compile = TRUE,
  details = 0,
  data = NULL,
  ...
)

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.
...	Additional arguments, currently not used.
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.
compile	Should network be compiled.
details	Debugging information.
data	An optional data set (currently must be an array/table)

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.

Value

An object of class "grain"

Note

A change from earlier versions of this package is that grain objects are now compiled upon creation.

Author(s)

Søren Højsgaard, [e-mail]

References

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/.

See Also

cptable, compile.grain, propagate.grain, setFinding, setEvidence, getFinding, pFinding, retractFinding, extractCPT, extractPOT, compileCPT, compilePOT

Examples

## Asia (chest clinic) network created from conditional probability tables

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(a, t.a, s, l.s, b.s, e.lt, x.e, d.be)
chest.bn  <- grain(chest.cpt)

## Create network from data and graph specification.

## There are different ways; see documentation in the "See all"
## links.

data(lizard, package="gRbase")
# DAG: height <- species -> diam
daG <- dag(~species + height:species + diam:species)

# UG : [height:species][diam:species]
uG  <- ug(~height:species + diam:species)

bn.uG   <- grain(uG, data=lizard)
bn.daG  <- grain(daG, data=lizard)

DataSciBurgoon/gRain documentation built on March 25, 2020, 12:02 a.m.