gRain (version 1.3-0)

querygrain: Query a network

Description

Query an independence network, i.e. obtain the conditional distribution of a set of variables - possibly (and typically) given finding (evidence) on other variables.

Usage

querygrain(object, nodes = nodeNames(object), type = "marginal",
  evidence = NULL, exclude = TRUE, normalize = TRUE, result = "array",
  details = 0)
# S3 method for grain
querygrain(object, nodes = nodeNames(object),
  type = "marginal", evidence = NULL, exclude = TRUE, normalize = TRUE,
  result = "array", details = 0)

Arguments

object

A "grain" object

nodes

A vector of nodes; those nodes for which the (conditional) distribution is requested.

type

Valid choices are "marginal" which gives the marginal distribution for each node in nodes; "joint" which gives the joint distribution for nodes and "conditional" which gives the conditional distribution for the first variable in nodes given the other variables in nodes.

evidence

An alternative way of specifying findings (evidence), see examples below.

exclude

If TRUE then nodes on which evidence is given will be excluded from nodes (see above).

normalize

Should the results be normalized to sum to one.

result

If "data.frame" the result is returned as a data frame (or possibly as a list of dataframes).

details

Debugging information

Value

A list of tables with potentials.

References

S<U+00F8>ren H<U+00F8>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

setEvidence, getEvidence, retractEvidence, pEvidence

Examples

# NOT RUN {
testfile <- system.file("huginex", "chest_clinic.net", package = "gRain")
chest <- loadHuginNet(testfile, details=0)
qb <- querygrain(chest)
qb

lapply(qb, as.numeric) # Safe
sapply(qb, as.numeric) # Risky

## setFinding / setEvidence

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))
chest <- grain(plist)


## 1) These two forms are identical
setEvidence(chest, c("asia","xray"), c("yes", "yes"))
setFinding(chest, c("asia","xray"), c("yes", "yes"))

## 2) Suppose we do not know with certainty whether a patient has
## recently been to Asia. We can then introduce a new variable
## "guess.asia" with "asia" as its only parent. Suppose
## p(guess.asia=yes|asia=yes)=.8 and p(guess.asia=yes|asia=no)=.1
## If the patient is e.g. unusually tanned we may set
## guess.asia=yes and propagate. This corresponds to modifying the
## model by the likelihood (0.8, 0.1) as
setEvidence(chest, c("asia","xray"), list(c(0.8,0.1), "yes"))

## 3) Hence, the same result as in 1) can be obtained with
setEvidence(chest, c("asia","xray"), list(c(1, 0), "yes"))

## 4) An alternative specification using evidence is
setEvidence(chest, evidence=list("asia"=c(1, 0), "xray"="yes"))

# }