This function computes the Euclidean distance from each point
in a point pattern to its nearest neighbour (the nearest other
point of the pattern). If `k`

is specified, it computes the
distance to the `k`

th nearest neighbour.

The function `nndist`

is generic, with
a method for point patterns (objects of class `"ppp"`

),
and a default method for coordinate vectors.

There are also methods for line segment patterns,
`nndist.psp`

,
three-dimensional point patterns, `nndist.pp3`

,
higher-dimensional point patterns, `nndist.ppx`

and point patterns on a linear network, `nndist.lpp`

;
these are described in their own help files.
Type `methods(nndist)`

to see all available methods.

The method for planar point patterns `nndist.ppp`

expects a single
point pattern argument `X`

and returns the vector of its
nearest neighbour distances.

The default method expects that `X`

and `Y`

will determine
the coordinates of a set of points. Typically `X`

and
`Y`

would be numeric vectors of equal length. Alternatively
`Y`

may be omitted and `X`

may be a list with two components
named `x`

and `y`

, or a matrix or data frame with two columns.

The argument `k`

may be a single integer, or an integer vector.
If it is a vector, then the \(k\)th nearest neighbour distances are
computed for each value of \(k\) specified in the vector.

If the argument `by`

is given, it should be a `factor`

,
of length equal to the number of points in `X`

.
This factor effectively partitions `X`

into subsets,
each subset associated with one of the levels of `X`

.
The algorithm will then compute, for each point of `X`

,
the distance to the nearest neighbour *in each subset*.

The argument `method`

is not normally used. It is
retained only for checking the validity of the software.
If `method = "interpreted"`

then the distances are
computed using interpreted R code only. If `method="C"`

(the default) then C code is used.
The C code is faster by two to three orders of magnitude
and uses much less memory.

If there is only one point (if `x`

has length 1),
then a nearest neighbour distance of `Inf`

is returned.
If there are no points (if `x`

has length zero)
a numeric vector of length zero is returned.

To identify *which* point is the nearest neighbour of a given point,
use `nnwhich`

.

To use the nearest neighbour distances for statistical inference,
it is often advisable to use the edge-corrected empirical distribution,
computed by `Gest`

.

To find the nearest neighbour distances from one point pattern
to another point pattern, use `nncross`

.