network | R Documentation |

A Bayesian network is represented as an object of class
`network`

. Methods for printing and plotting are defined.

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,...)

`df` |
a data frame, where the columns define the variables. A
continuous variable should have type |

`specifygraph` |
a logical. If |

`inspectprob` |
a logical. If |

`doprob` |
a logical. If |

`x` |
an object of class |

`filename` |
a string or |

`condprior` |
a logical. If |

`condposterior` |
a logical. If |

`sscale` |
a numeric. The nodes are initially placed on a circle
with radius |

`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 |

`showban` |
a logical. If |

`...` |
additional plot arguments, passed to |

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 |

`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 |

`score` |
a numeric added by |

`relscore` |
a numeric added by |

Susanne Gammelgaard Bottcher,

Claus Dethlefsen rpackage.deal@gmail.com.

`networkfamily`

,
`node`

,
`rnetwork`

,
`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) ) 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"))

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