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