# nndist

##### Nearest neighbour distances

Computes the distance from each point to its nearest neighbour in a point pattern.

##### Usage

```
nndist(X, ..., method="C")
## S3 method for class 'ppp':
nndist(X, \dots, method="C")
## S3 method for class 'default':
nndist(X, Y=NULL, \dots, method="C")
```

##### Arguments

- X,Y
- Arguments specifying the locations of
a set of points.
For
`nndist.ppp`

, the argument`X`

should be a point pattern (object of class`"ppp"`

). For`nndist.default`

, typically`X`

and < - ...
- Ignored by
`nndist.ppp`

and`nndist.default`

. - method
- String specifying which method of calculation to use.
Values are
`"C"`

and`"interpreted"`

.

##### Details

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

The function `nndist`

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

)
and a default method.

The method for point patterns 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 `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`

.

##### Value

- Numeric vector of the nearest neighbour distances for each point.

##### Warnings

An infinite value is returned if there is only one point in the point pattern.

##### See Also

##### Examples

```
data(cells)
d <- nndist(cells)
x <- runif(100)
y <- runif(100)
d <- nndist(x, y)
# Stienen diagram
plot(cells %mark% (nndist(cells)/2), markscale=1)
```

*Documentation reproduced from package spatstat, version 1.11-2, License: GPL version 2 or newer*