# bw.diggle

##### Cross Validated Bandwidth Selection for Kernel Density

Uses cross-validation to select a smoothing bandwidth for the kernel estimation of point process intensity.

##### Usage

`bw.diggle(X, ..., correction="good", hmax=NULL, nr=512)`

##### Arguments

- X
- A point pattern (object of class
`"ppp"`

). - ...
- Ignored.
- correction
- Character string passed to
`Kest`

determining the edge correction to be used to calculate the $K$ function. - hmax
- Numeric. Maximum value of bandwidth that should be considered.
- nr
- Integer. Number of steps in the distance value $r$ to use in computing numerical integrals.

##### Details

This function selects an appropriate bandwidth `sigma`

for the kernel estimator of point process intensity
computed by `density.ppp`

.

The bandwidth $\sigma$ is chosen to minimise the mean-square error criterion defined by Diggle (1985). The algorithm uses the method of Berman and Diggle (1989) to compute the quantity $$M(\sigma) = \frac{\mbox{MSE}(\sigma)}{\lambda^2} - g(0)$$ as a function of bandwidth $\sigma$, where $\mbox{MSE}(\sigma)$ is the mean squared error at bandwidth $\sigma$, while $\lambda$ is the mean intensity, and $g$ is the pair correlation function. See Diggle (2003, pages 115-118) for a summary of this method.

The result is a numerical value giving the selected bandwidth.
The result also belongs to the class `"bw.optim"`

which can be plotted to show the (rescaled) mean-square error
as a function of `sigma`

.

##### Value

- A numerical value giving the selected bandwidth.
The result also belongs to the class
`"bw.optim"`

which can be plotted.

##### Definition of bandwidth

The smoothing parameter `sigma`

returned by `bw.diggle`

(and displayed on the horizontal axis of the plot)
corresponds to `h/2`

, where `h`

is the smoothing
parameter described in Diggle (2003, pages 116-118) and
Berman and Diggle (1989).
In those references, the smoothing kernel
is the uniform density on the disc of radius `h`

. In
`density.ppp`

, the smoothing kernel is the
isotropic Gaussian density with standard deviation `sigma`

.
When replacing one kernel by another, the usual
practice is to adjust the bandwidths so that the kernels have equal
variance (cf. Diggle 2003, page 118). This implies that `sigma = h/2`

.

##### References

Berman, M. and Diggle, P. (1989)
Estimating weighted integrals of the
second-order intensity of a spatial point process.
*Journal of the Royal Statistical Society, series B*
**51**, 81--92.

Diggle, P.J. (1985)
A kernel method for smoothing point process data.
*Applied Statistics* (Journal of the Royal Statistical Society,
Series C) **34** (1985) 138--147.

Diggle, P.J. (2003)
*Statistical analysis of spatial point patterns*,
Second edition. Arnold.

##### See Also

##### Examples

```
data(lansing)
attach(split(lansing))
b <- bw.diggle(hickory)
plot(b, ylim=c(-2, 0), main="Cross validation for hickories")
plot(density(hickory, b))
```

*Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)*