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,
nwequalA <- factor(rep(c("A1","A2"),50))
B <- factor(rep(rep(c("B1","B2"),25),2))
thisnet <- network( data.frame(A,B) )Run the code above in your browser using DataLab