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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