get.networks | R Documentation |
Given a start time, end time, and increment (or vectors of onsets and termini) to express sampling intervals, return a list of collapsed networks from a networkDynamic object.
get.networks(dnet, start = NULL, end = NULL, time.increment = NULL,
onsets=NULL, termini=NULL,...)
dnet |
A |
start |
numeric value giving the start of the sampling interval |
end |
numeric value giving the end of the sampling interval |
time.increment |
value for the offset (and duration) between sucessive samples. Will default to 1 if not otherwise specified |
onsets |
A numeric vector containing the onset times of the networks to be extracted. This must be accompanied by |
termini |
A numeric vector containing the terminus times of the networks to be extracted. This must be accompanied by |
... |
Additional arguments to |
The sampling ("slicing") intervals may be defined using either the start
, end
, and time.increment
parameters, or by providing parallel vectors of onsets
and termini
. If values are not specefied but a net.obs.period
attribute exists to describe the 'natural' sampling parameters, start
and end
will defult to the max an min of the observations element, with time.increment
set to its corresponding value in the net.obs.period
.
A list
of static network
objects corresponding to the specified sampling intervals of the networkDynamic
See Note in network.collapse
.
Kirk Li, Skye Bender-deMoll
See Also as network.collapse
for obtaining a slice of static network, network.extract
for extracting sub-ranges of a networkDynamic, get.vertex.attribute.active
for more on TEA attributes, and as.network.networkDynamic
for stripping the the networkDynamic class from an object.
# create a networkDynamic with some basic activity and time extended attributes (TEA)
test <- network.initialize(5)
add.edges.active(test, tail=c(1,2,3), head=c(2,3,4),onset=0,terminus=1)
activate.edges(test,onset=3,terminus=5)
activate.edges(test,onset=-2,terminus=-1)
activate.edge.attribute(test,'weight',5,onset=3,terminus=4)
activate.edge.attribute(test,'weight',3,onset=4,terminus=5,e=1:2)
# obtain the list of networks
list <- get.networks(test,start=0, end=5)
# aggregate over a longer time period with specified rule
list <- get.networks(test,start=0, end=6,time.increment=2,rule='latest')
# use 'at' style extraction of momentary slices via onsets & termini
list <- get.networks(test,onsets=0:5,termini=0:5)
# ensure that all networks returned will be the same size
list <- get.networks(test,onsets=0:5,termini=0:5,retain.all.vertices=TRUE)
# find out how many edges in each sampling point with apply
sapply(get.networks(test,start=0,end=5),network.edgecount)
# generate a list of matrices
lapply(get.networks(test,start=0,end=5),as.matrix)
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