drawnetwork allows the user to specify a Bayesian network through a point and click interface.drawnetwork(nw,df,prior,trylist=vector("list",size(nw)),
unitscale=20,cexscale=8,
arrowlength=.25,nocalc=FALSE,
yr=c(0,350),xr=yr,...)network to be edited.network.jointprior.maketrylist.TRUE, no learning procedure is called, see eg. rnetwork.getnetwork and gettrylist. The elements arenetwork with the final network.maketrylist.banlist. It is a matrix with two
columns. Each row is the 'from' node index and the 'to' node index,
where the indices are the column number in the data frame.
Note that the network score changes as the network is re-learned
whenever a change is made (unless nocalc is TRUE).networkdata(rats)
rats.nw <- network(rats)
rats.prior <- jointprior(rats.nw,12)
rats.nw <- getnetwork(learn(rats.nw,rats,rats.prior))
newrat <- getnetwork(drawnetwork(rats.nw,rats,rats.prior))Run the code above in your browser using DataLab