Setting and removing evidence in grain objects.
setEvi(
object,
nodes = NULL,
states = NULL,
evidence = NULL,
propagate = TRUE,
details = 0
)setEvi_(object, evidence = NULL, propagate = TRUE, details = 0)
retractEvi(object, items = NULL, propagate = TRUE)
retractEvi_(object, items = NULL, propagate = TRUE)
absorbEvi(object, propagate = TRUE)
absorbEvi_(object, propagate = TRUE)
pEvidence(object)
pEvi(object)
getEvidence(object)
dropEvi(object) <- value
addEvi(object) <- value
evidence(object)
# S3 method for grain
evidence(object)
evidence(object) <- value
# S3 method for grain
evidence(object) <- value
addEvi(
object,
nodes = NULL,
states = NULL,
evidence = NULL,
propagate = TRUE,
details = 0
)
dropEvi(object, items = NULL, propagate = TRUE)
getEvi(object)
insertEvi(evi.list, pot, hostclique)
getHostClique(set.list, cliques)
A "grain" object
A vector of nodes; those nodes for which the (conditional) distribution is requested.
A vector of states (of the nodes given by 'nodes')
An alternative way of specifying findings (evidence), see examples below.
Should the network be propagated?
Debugging information
Items in the evidence list to be removed. Here,
NULL means remove everything. If items is a
character vector (of nodes) then evidence on these nodes is
removed. If items is a numeric vector then those items
in the evidence list is removed. Notice that 0 means nothing is
removed.
The evidence in the form of a named list or an evidence-object.
A "grain_ev" object.
A list of clique potentials (a potential is an array).
A numerical vector indicating in which element of 'pot' each eviendence item in 'evi.list' should be inserted in.
A list of sets (a set is a character vector).
A list of sets (a set is a character vector).
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/.
setEvidence, getEvidence,
retractEvidence, pEvidence,
setFinding, getFinding,
retractFinding, pFinding,
# NOT RUN {
## 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))
bn <- grain(plist)
## 1) These forms are identical
e1 <- list(dysp="no", xray="no")
setEvi(bn, evidence=e1)
setEvi(bn, nodes=c("dysp","xray"), states=c("no", "no"))
setEvidence(bn, nodes=c("dysp","xray"), states=c("no", "no"))
# Notice: setFinding is old school but it was used in the
# "Graphical Models with R" book.
setFinding(bn, nodes=c("dysp","xray"), states=c("no", "no"))
## 2) Updating evidence
# Notice that only 'asia' is set because 'dysp' was set earlier
e2 <- list(dysp="yes", asia="yes")
bn1 <- setEvi(bn, evidence=e1)
bn1
bn2 <- setEvi(bn1, evidence=e2)
bn2
## 3) Shorter forms
bn2 <- bn
evidence(bn2)
evidence(bn2) <- e1
evidence(bn2)
evidence(bn2) <- e2
evidence(bn2)
evidence(bn2) <- NULL
evidence(bn2)
## 4) Alternative forms:
setEvi(bn, evidence=list("asia"=c(1, 0), "xray"="yes"))
## 5) 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
b =setEvi(bn, nodes=c("asia","xray"), states=list(c(0.8,0.1), "yes"))
as.data.frame( evidence( b ) )
# }
Run the code above in your browser using DataLab