nnfun.lpp | R Documentation |

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

## S3 method for class 'lpp' nnfun(X, ..., k=1, value=c("index", "mark"))

`X` |
A point pattern on a linear network
(object of class |

`k` |
Integer. The algorithm finds the |

`value` |
String (partially matched) specifying whether to return the
index of the neighbour ( |

`...` |
Other arguments are ignored. |

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`

.

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`

.

X <- runiflpp(3, simplenet) f <- nnfun(X) f plot(f) plot(nnfun(chicago, value="m"))

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