Create a Linear Network
Creates an object of class "linnet" representing
a network of line segments.
linnet(vertices, m, edges, sparse=FALSE, warn=TRUE)
vertices |
Point pattern (object of class |
m |
Adjacency matrix. A matrix or sparse matrix
of logical values equal to |
edges |
Edge list. A two-column matrix of integers,
specifying all pairs of vertices
that should be joined by an edge.
(Specify either |
sparse |
Optional. Logical value indicating whether to use a sparse matrix representation of the network. See Details. |
warn |
Logical value indicating whether to issue a warning if the resulting network is not connected. |
An object of class "linnet" represents a network of
straight line segments in two dimensions. The function linnet creates
such an object from the minimal information: the spatial location
of each vertex (endpoint, crossing point or meeting point of lines)
and information about which vertices are joined by an edge.
If sparse=FALSE (the default), the algorithm will compute
and store various properties of the network, including
the adjacency matrix m and a matrix giving the
shortest-path distances between each pair of vertices in the network.
This is more efficient for small datasets. However it can require
large amounts of memory and can take a long time to execute.
If sparse=TRUE, then the shortest-path distances will not be computed,
and the network adjacency matrix m will be stored as a
sparse matrix. This saves a lot of time and memory when creating the
linear network.
If the argument edges is given, then it will also determine
the ordering of the line segments when they are stored or extracted.
For example, edges[i,] corresponds to as.psp(L)[i].
Object of class "linnet" representing the linear network.
Ang Qi Wei aqw07398@hotmail.com and Adrian Baddeley Adrian.Baddeley@curtin.edu.au
simplenet for an example of a linear network.
methods.linnet for
methods applicable to linnet objects.
Special tools: thinNetwork,
insertVertices,
joinVertices,
connected.linnet, lixellate.
delaunayNetwork for the Delaunay triangulation
as a network.
# letter 'A' specified by adjacency matrix v <- ppp(x=(-2):2, y=3*c(0,1,2,1,0), c(-3,3), c(-1,7)) m <- matrix(FALSE, 5,5) for(i in 1:4) m[i,i+1] <- TRUE m[2,4] <- TRUE m <- m | t(m) letterA <- linnet(v, m) plot(letterA) # letter 'A' specified by edge list edg <- cbind(1:4, 2:5) edg <- rbind(edg, c(2,4)) letterA <- linnet(v, edges=edg)
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