# nndist.pp3

##### Nearest neighbour distances in three dimensions

Computes the distance from each point to its nearest neighbour in a three-dimensional point pattern. Alternatively computes the distance to the second nearest neighbour, or third nearest, etc.

##### Usage

```
# S3 method for pp3
nndist(X, …, k=1)
```

##### Arguments

- X
Three-dimensional point pattern (object of class

`"pp3"`

).- …
Ignored.

- k
Integer, or integer vector. The algorithm will compute the distance to the

`k`

th nearest neighbour.

##### Details

This function computes the Euclidean distance from each point
in a three-dimensional
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; this function
`nndist.pp3`

is the method for the class `"pp3"`

.

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 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 `G3est`

.

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

.

##### Value

Numeric vector or matrix containing the nearest neighbour distances for each point.

If `k = 1`

(the default), the return value is a
numeric vector `v`

such that `v[i]`

is the
nearest neighbour distance for the `i`

th data point.

If `k`

is a single integer, then the return value is a
numeric vector `v`

such that `v[i]`

is the
`k`

th nearest neighbour distance for the
`i`

th data point.

If `k`

is a vector, then the return value is a
matrix `m`

such that `m[i,j]`

is the
`k[j]`

th nearest neighbour distance for the
`i`

th data point.

##### Warnings

An infinite or `NA`

value is returned if the
distance is not defined (e.g. if there is only one point
in the point pattern).

##### See Also

##### Examples

```
# NOT RUN {
X <- runifpoint3(40)
# nearest neighbours
d <- nndist(X)
# second nearest neighbours
d2 <- nndist(X, k=2)
# first, second and third nearest
d1to3 <- nndist(X, k=1:3)
# }
```

*Documentation reproduced from package spatstat, version 1.49-0, License: GPL (>= 2)*