Description Usage Arguments Value See Also Examples
View source: R/generateClusteredNetwork.R
This function generates clustered networks. It first generates n cluster
centeres via a latin hypercube design to ensure space-filling property, i. e.,
to ensure, that the clusters are placed far from each other.
It then distributes points to the clusters according to
gaussian distributions using the cluster centers as the mean vector and
the distance to the nearest neighbour cluster center as the variance.
This procedure works well if the box constraints of the hypercube are
not too low (see the lower bound for the upper
parameter).
1 2 3 4 5 6 |
n.cluster |
[ |
n.points |
[ |
n.dim |
[ |
generator |
[ |
lower |
[ |
upper |
[ |
sigmas |
[ |
n.depots |
[ |
distribution.strategy |
[ |
cluster.centers |
[ |
out.of.bounds.handling |
[
Default is “mirror”. |
name |
[ |
... |
[ |
[ClusteredNetwork
]
Object of type ClusteredNetwork
.
1 2 3 | x = generateClusteredNetwork(n.points = 20L, n.cluster = 2L)
y = generateClusteredNetwork(n.points = 40L, n.cluster = 3L, n.depots = 2L)
z = generateClusteredNetwork(n.points = 200L, n.cluster = 10L, out.of.bounds.handling = "reset")
|
Loading required package: BBmisc
Loading required package: mvtnorm
Loading required package: lhs
Loading required package: checkmate
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