# nnfun.lpp

0th

Percentile

##### Nearest Neighbour Map on Linear Network

Compute the nearest neighbour function of a point pattern on a linear network.

Keywords
spatial, math
##### Usage
# S3 method for lpp
nnfun(X, ..., k=1)
##### Arguments
X

A point pattern on a linear network (object of class "lpp").

k

Integer. The algorithm finds the kth nearest neighbour in X from any spatial location.

Other arguments are ignored.

##### Details

The (geodesic) nearest neighbour function of a point pattern X on a linear network L tells us which point of X is closest to any given location.

If X is a point pattern on a linear network L, the nearest neighbour function of X is the mathematical function $f$ defined for any location $s$ on the network by f(s) = i, where X[i] is the closest point of X to the location s measured by the shortest path. In other words the value of f(s) is the identifier or serial number of the closest point of X.

The command nnfun.lpp is a method for the generic command nnfun for the class "lpp" of point patterns on a linear network.

If X is a point pattern on a linear network, f <- nnfun(X) returns a function in the R language, with arguments x,y, …, that represents the nearest neighbour function of X. Evaluating the function f in the form v <- f(x,y), where x and y are any numeric vectors of equal length containing coordinates of spatial locations, yields a vector of identifiers or serial numbers of the data points closest to these spatial locations. More efficiently f can take the arguments x, y, seg, tp where seg and tp are the local coordinates on the network.

The result of f <- nnfun(X) also belongs to the class "linfun". It can be printed and plotted immediately as shown in the Examples. It can be converted to a pixel image using as.linim.

##### Value

A function in the R language, with arguments x,y and optional arguments seg,tp. It also belongs to the class "linfun" which has methods for plot, print etc.

linfun, methods.linfun.

To compute the distance to the nearest neighbour, see distfun.lpp.

• nnfun.lpp
##### Examples
# NOT RUN {
data(letterR)
X <- runiflpp(3, simplenet)
f <- nnfun(X)
f
plot(f)
# }

Documentation reproduced from package spatstat, version 1.57-1, License: GPL (>= 2)

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