network
is the list of
nodes. The nodes summarizes the local properties of the node given the
parents of the node.node (idx,parents,type,name=paste(idx),
levels=2,levelnames=paste(1:levels),position=c(0,0))
## S3 method for class 'node':
print (x,filename=NA,master=FALSE,condposterior=TRUE,condprior=TRUE,...)
## S3 method for class 'node':
plot (x,cexscale=10,notext=FALSE,scale=10,...)
prob.node (x,nw,df,equalcases=FALSE,vif=1.0,smalldf=NA)
"discrete"
or "continuous"
.type="discrete"
, this is the number of levels
for the discrete variable.type="discrete"
this is a vector of
strings (as long as levels
) with the names of the
levels.network
and drawnetwork
.TRUE
) set the probability
equal to the
observed frequency. If FALSE
, observed frequencies are used.timeslice
.timeslice
.FALSE
, do not display text on plots.continuous
or discrete
.insert
function.network
.jointprior
using the master prior procedure (see
localmaster
).learnnode
.learnnode
.makesimprob
and used by
simulation
.A <- factor(rep(c("A1","A2"),50))
B <- factor(rep(rep(c("B1","B2"),25),2))
thisnet <- network( data.frame(A,B) )
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