Description Usage Arguments Details Author(s) References Examples
Heatmap plot of the adjacency matrices with rows/columns reorganized according to the group membership associated to a dynamic stochastic block model.
1  adjacency.plot(dynsbm, Y, present=NULL, col=heat.colors(9))

dynsbm 
An object of class 
Y 
An object of class 
present 

col 
A list of colors such as that generated by 
The T adjacency matrices are represented. The row/lines are reordered following the group membership (nodes of group 1 followed by nodes of group 2 and so on). Red lines correspond to group delineation.
The reordering is independent for each time step. The adjacency matrices do not contain the row/columns corresponding to absent nodes.
If dynsbm
was estimated with edge.type=="binary"
, the
matrices cells are colored in white for value O or in the first color of the
col
argument vector for value 1. If dynsbm
was estimated
with edge.type=="discrete"
or edge.type=="continuous"
, the
matrices cells are colored with a colored gradient for value >0.
Authors: Catherine Matias, Vincent Miele
Maintainer: Vincent Miele <vincent.miele@univlyon1.fr>
Catherine Matias and Vincent Miele, Statistical clustering of temporal networks through a dynamic stochastic block model, Journal of the Royal Statistical Society: Series B (2017) http://dx.doi.org/10.1111/rssb.12200 http://arxiv.org/abs/1506.07464
Vincent Miele and Catherine Matias, Revealing the hidden structure of dynamic ecological networks, Royal Society Open Science (2017) http://dx.doi.org/10.1098/rsos.170251 https://arxiv.org/abs/1701.01355
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## 1  binary case
data(simdataT5Q4N40binary)
## estimation for Q=1..5 groups
list.dynsbm < select.dynsbm(simdataT5Q4N40binary,
Qmin=1, Qmax=5, edge.type="binary", nstart=1)
## Not run:
## better to use nstart>1 starting points
## but estimation can take 12 minutes
list.dynsbm < select.dynsbm(simdataT5Q4N40binary,
Qmin=1, Qmax=5, edge.type="binary", nstart=25)
## End(Not run)
## selection of Q=4
dynsbm < list.dynsbm[[4]]
## plotting intra/inter connectivity patterns
adjacency.plot(dynsbm, simdataT5Q4N40binary)
####################
## 2  continuous case
data(simdataT5Q4N40continuous)
## estimation for Q=1..5 groups
list.dynsbm < select.dynsbm(simdataT5Q4N40continuous,
Qmin=1, Qmax=5, edge.type="continuous", nstart=1)
## Not run:
## better to use nstart>1 starting points
## but estimation can take 12 minutes
list.dynsbm < select.dynsbm(simdataT5Q4N40continuous,
Qmin=1, Qmax=5, edge.type="continuous", nstart=25)
## End(Not run)
## selection of Q=4
dynsbm < list.dynsbm[[4]]
## plotting intra/inter connectivity patterns
adjacency.plot(dynsbm, simdataT5Q4N40continuous)
####################
## 3  discrete case
data(simdataT5Q4N40discrete)
## estimation for Q=1..5 groups
list.dynsbm < select.dynsbm(simdataT5Q4N40discrete,
Qmin=1, Qmax=5, edge.type="discrete", K=4, nstart=1)
## Not run:
## better to use nstart>1 starting points
## but estimation can take 12 minutes
list.dynsbm < select.dynsbm(simdataT5Q4N40discrete,
Qmin=1, Qmax=5, edge.type="discrete", K=4, nstart=25)
## End(Not run)
## selection of Q=4
dynsbm < list.dynsbm[[4]]
## plotting intra/inter connectivity patterns
adjacency.plot(dynsbm, simdataT5Q4N40discrete)

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