networkfamily {deal} | R Documentation |
Generates and learns all networks for a set of variables.
Description
Method for generating and learning all networks that are
possible for a given set of variables. These may be
plotted or printed. Also, functions for
sorting according to the network score (see nwfsort
) and for
making a network family unique (see the unique
method for
networkfamily
objects) are available.
Usage
networkfamily(data,nw=network(data), prior=jointprior(nw),
trylist=vector("list",size(nw)), timetrace=TRUE)
## S3 method for class 'networkfamily'
print(x,...)
## S3 method for class 'networkfamily'
plot(x,layout=,
cexscale=5,arrowlength=0.1,sscale=7,...)
Arguments
nw |
an object of class |
data |
a data frame used for learning the network, see
|
prior |
a list containing parameter priors, generated by
|
trylist |
a list used internally for reusing learning of nodes,
see |
timetrace |
a logical. If |
x |
an object of class |
layout |
a numeric two dimensional vector with the number of plots in the rows
and columns of each plotting page. Default set to |
cexscale |
a numeric. A scaling parameter to set the size of the nodes. |
arrowlength |
a numeric, which gives the length of the arrow heads. |
sscale |
a numeric. The nodes are initially placed on a circle
with radius |
... |
additional plot arguments passed to the plot method for network objects. |
Details
networkfamily
generates and learns all possible networks with
the nodes given as in the initial network nw
. This is done by
successively trying to generate the networks with all possible arrows
to/from each node (see addarrows
). If there is a ban list
present in nw
(see network
), then this is
respected, as are the restrictions described in insert
.
After generation of all possible networks, a test for cycles (see
cycletest
) is performed and only networks with directed
acyclic graphs are returned.
Value
The function networkfamily
returns a list with two components,
nw |
an object of class |
trylist |
an updated list used internally for reusing learning
of nodes, see |
Note
Generating all possible networks can be very time consuming!
Author(s)
Susanne Gammelgaard Bottcher,
Claus Dethlefsen rpackage.deal@gmail.com.
See Also
network
,
genlatex
,
heuristic
,
nwfsort
,
unique.networkfamily
,
elementin
,
addarrows
,
cycletest
Examples
data(rats)
allrats <- getnetwork(networkfamily(rats))
plot(allrats)
print(allrats)