# Bayesian network data structure A Bayesian network is represented as an object of class `network`. Methods for printing and plotting are defined. latin1 ```r network(df,specifygraph=FALSE,inspectprob=FALSE, doprob=TRUE,yr=c(0,350),xr=yr) ## S3 method for class 'network' print(x,filename=NA,condposterior=FALSE, condprior=FALSE,...) ## S3 method for class 'network' plot(x,arrowlength=.25, notext=FALSE, sscale=7,showban=TRUE,yr=c(0,350),xr=yr, unitscale=20,cexscale=8,...) ``` ## Arguments - `df`: a data frame, where the columns define the variables. A continuous variable should have type `numeric` and discrete varibles should have type `factor`. - `specifygraph`: a logical. If `TRUE`, provides a call to `drawnetwork` to interactively specify a directed acyclic graph and possibly a ban list (see below). - `inspectprob`: a logical. If `TRUE`, provides a plot of the graph and possibility to inspect the calculated probability distribution by clicking on the nodes. - `doprob`: a logical. If `TRUE`, do not calculate a probability distribution. Used for example in `rnetwork`. - `x`: an object of class `network`. - `filename`: a string or `NA`. If not `NA`, output is printed to a file. - `condprior`: a logical. If `TRUE`, the conditional prior is printed, see `conditional`. - `condposterior`: a logical. If `TRUE`, the conditional posterior is printed, see `learn`. - `sscale`: a numeric. The nodes are initially placed on a circle with radius `sscale`. - `unitscale`: a numeric. Scale parameter for chopping off arrow heads. - `cexscale`: a numeric. Scale parameter to set the size of the nodes. - `arrowlength`: a numeric containing the length of the arrow heads. - `xr`: a numeric vector with two components containing the range on x-axis. - `yr`: a numeric vector with two components containing the range on y-axis. - `notext`: a logical. If `TRUE`, no text is displayed in the nodes on the plot. - `showban`: a logical. If `TRUE`, banned arrows are shown in red. - ``...``: additional plot arguments, passed to `plot.node`. ## Returns The `netork` creator function returns an object of class `network`, which is a list with the following elements (properties), - **nodes**: a list of objects of class `node`. If `doprob` is `TRUE`, the nodes are given the property `prob` which is the initial probability distribution used by `jointprior`. - **n**: an integer containing the number of nodes in the network. - **discrete**: a numeric vector of indices of discrete nodes. - **continuous**: a numeric vector of indices of continuous nodes. - **banlist**: a numeric matrix with two columns. Each row contains the indices `i -> j` of arrows that may not be allowed in the directed acyclic graph. - **score**: a numeric added by `learn` and is the log network score. - **relscore**: a numeric added by `nwfsort` and is the relative network score -- compared with the best network in a network family. ## See Also `networkfamily`, `node`, `rnetwork`, `learn`, `drawnetwork`, `jointprior`, `heuristic`, `nwequal` ## Author(s) Susanne Gammelgaard Bottcher, Claus Dethlefsen rpackage.deal@gmail.com . ## Examples ```r A <- factor(rep(c("A1","A2"),50)) B <- factor(rep(rep(c("B1","B2"),25),2)) thisnet <- network( data.frame(A,B) ) set.seed(109) sex <- gl(2,4,label=c("male","female")) age <- gl(2,2,8) yield <- rnorm(length(sex)) weight <- rnorm(length(sex)) mydata <- data.frame(sex,age,yield,weight) mynw <- network(mydata) # adjust prior probability distribution localprob(mynw,"sex") <- c(0.4,0.6) localprob(mynw,"age") <- c(0.6,0.4) localprob(mynw,"yield") <- c(2,0) localprob(mynw,"weight")<- c(1,0) print(mynw) plot(mynw) prior <- jointprior(mynw) mynw <- getnetwork(learn(mynw,mydata,prior)) thebest <- getnetwork(autosearch(mynw,mydata,prior)) print(mynw,condposterior=TRUE) ## Not run: savenet(mynw,file("yield.net")) ```

network function