network
. Methods for printing and plotting are defined.network(df,specifygraph=FALSE,inspectprob=FALSE,equalcases=FALSE,vif=1.0,
doprob=TRUE,tvar=NA,smalldf=NA,yr=c(0,350),xr=yr)
## S3 method for class 'network':
print(x,filename=NA,master=FALSE,condposterior=FALSE,
condprior=FALSE,...)
## S3 method for class 'network':
plot (x,scale=10,arrowlength=.25,
notext=FALSE,
sscale=.7*scale,showban=TRUE,yr=c(0,350),xr=yr,
unitscale=20,cexscale=8,...)
prob.network (x,df,equalcases=FALSE,vif=1.0,smalldf=NA)
numeric
and discrete varibles
should have type factor
.TRUE
) set the probability
equal to the
observed frequency. If FALSE
, observed frequencies are used.TRUE
, do not calculate a probability distribution. Used
for example in simulation
.timeslice
.timeslice
.-scale
to
scale
.sscale
.notext==TRUE
.plot.node
.doprob==TRUE
, the nodes are given the
attribute prob
which is the initial probability distribution used
by jointprior
. Arguments equalcases
and
vif
are used to calculate prob
.i -> j
of arrows that may not be allowed in the
directed acyclic graph.learn
and is the log-network
score.nwfsort
and is the relative
log-network score -- compared to the best network in a networkfamily.networkfamily
,
node
,
simulation
,
learn
,
drawnetwork
,
jointprior
,
heuristic
,
nwequal
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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