network {deal} | 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 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 . |
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. |
Susanne Gammelgaard Bottcher alma@math.aau.dk,
Claus Dethlefsen cld@rn.dk.
Further information about deal can be found at:
http://www.math.aau.dk/~dethlef/novo/deal.
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"))