# Kscaled

##### Locally Scaled K-function

Estimates the template $K$ function of a locally-scaled point process.

- Keywords
- spatial, nonparametric

##### Usage

```
Kscaled(X, lambda=NULL, ..., r = NULL, breaks = NULL,
correction=c("border", "isotropic", "translate"),
sigma=NULL, varcov=NULL)
Lscaled(...)
```

##### Arguments

- X
- The observed data point pattern,
from which an estimate of the locally scaled $K$ function
will be computed.
An object of class
`"ppp"`

or in a format recognised by`as.ppp()`

. - lambda
- Optional.
Values of the estimated intensity function.
Either a vector giving the intensity values
at the points of the pattern
`X`

, a pixel image (object of class`"im"`

) giving the intensity values at all locatio - ...
- Arguments passed from
`Lscaled`

to`Kscaled`

and from`Kscaled`

to`density.ppp`

if`lambda`

is omitted. - r
- vector of values for the argument $r$ at which the locally scaled $K$ function should be evaluated. Not normally given by the user; there is a sensible default.
- breaks
- An alternative to the argument
`r`

. Not normally invoked by the user. See Details. - correction
- A character vector containing any selection of the
options
`"border"`

,`"isotropic"`

,`"Ripley"`

,`"translate"`

,`"none"`

or`"best"`

. It specifies the edge correct - sigma,varcov
- Optional arguments passed to
`density.ppp`

to control the smoothing bandwidth, when`lambda`

is estimated by kernel smoothing.

##### Details

`Kscaled`

computes an estimate of the $K$ function
for a locally scaled point process.
`Lscaled`

computes the corresponding $L$ function
$L(r) = \sqrt{K(r)/\pi}$.

Locally scaled point processes are a class of models for inhomogeneous point patterns, introduced by Hahn et al (2003). They include inhomogeneous Poisson processes, and many other models.

The template $K$ function of a locally-scaled process is a counterpart of the ``ordinary'' Ripley $K$ function, in which the distances between points of the process are measured on a spatially-varying scale (such that the locally rescaled process has unit intensity).

The template $K$ function is an indicator of interaction between the points. For an inhomogeneous Poisson process, the theoretical template $K$ function is approximately equal to $K(r) = \pi r^2$. Values $K_{\rm scaled}(r) > \pi r^2$ are suggestive of clustering.

`Kscaled`

computes an estimate of the template $K$ function
and `Lscaled`

computes the corresponding $L$ function
$L(r) = \sqrt{K(r)/\pi}$.
The locally scaled interpoint distances are computed
using an approximation proposed by Hahn (2007). The Euclidean
distance between two points is multiplied by the average of the
square roots of the intensity values at the two points.

The argument `lambda`

should supply the
(estimated) values of the intensity function $\lambda$.
It may be either
[object Object],[object Object],[object Object],[object Object]
If `lambda`

is a numeric vector, then its length should
be equal to the number of points in the pattern `X`

.
The value `lambda[i]`

is assumed to be the
the (estimated) value of the intensity
$\lambda(x_i)$ for
the point $x_i$ of the pattern $X$.
Each value must be a positive number; `NA`

's are not allowed.

If `lambda`

is a pixel image, the domain of the image should
cover the entire window of the point pattern. If it does not (which
may occur near the boundary because of discretisation error),
then the missing pixel values
will be obtained by applying a Gaussian blur to `lambda`

using
`blur`

, then looking up the values of this blurred image
for the missing locations.
(A warning will be issued in this case.)

If `lambda`

is a function, then it will be evaluated in the
form `lambda(x,y)`

where `x`

and `y`

are vectors
of coordinates of the points of `X`

. It should return a numeric
vector with length equal to the number of points in `X`

.

If `lambda`

is omitted, then it will be estimated using
a `leave-one-out' kernel smoother, as described in Baddeley, Moller
and Waagepetersen (2000). The estimate `lambda[i]`

for the
point `X[i]`

is computed by removing `X[i]`

from the
point pattern, applying kernel smoothing to the remaining points using
`density.ppp`

, and evaluating the smoothed intensity
at the point `X[i]`

. The smoothing kernel bandwidth is controlled
by the arguments `sigma`

and `varcov`

, which are passed to
`density.ppp`

along with any extra arguments.
Edge corrections are used to correct bias in the estimation
of $K_{\rm scaled}$. First the interpoint distances are
rescaled, and then edge corrections are applied as in `Kest`

.
See `Kest`

for details of the edge corrections
and the options for the argument `correction`

.
The pair correlation function can also be applied to the
result of `Kscaled`

; see `pcf`

and `pcf.fv`

.

##### Value

- An object of class
`"fv"`

(see`fv.object`

). Essentially a data frame containing at least the following columns, r the vector of values of the argument $r$ at which the pair correlation function $g(r)$ has been estimated theo vector of values of $\pi r^2$, the theoretical value of $K_{\rm scaled}(r)$ for an inhomogeneous Poisson process - and containing additional columns
according to the choice specified in the
`correction`

argument. The additional columns are named`border`

,`trans`

and`iso`

and give the estimated values of $K_{\rm scaled}(r)$ using the border correction, translation correction, and Ripley isotropic correction, respectively.

##### References

Hahn, U. (2007)
*Global and Local Scaling in the
Statistics of Spatial Point Processes*. Habilitationsschrift,
Universitaet Augsburg.
Hahn, U., Jensen, E.BV., van Lieshout, M.N.M. and Nielsen, L.S. (2003)
Inhomogeneous spatial point processes by location-dependent scaling.
*Advances in Applied Probability* **35**, 319--336.

Prokesova, M., Hahn, U. and Vedel Jensen, E.B. (2006)
Statistics for locally scaled point patterns.
In A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan (eds.)
*Case Studies in Spatial Point Pattern Modelling*.
Lecture Notes in Statistics 185. New York: Springer Verlag.
Pages 99--123.

##### See Also

##### Examples

```
data(bronzefilter)
X <- unmark(bronzefilter)
K <- Kscaled(X)
fit <- ppm(X, ~x)
lam <- predict(fit)
K <- Kscaled(X, lam)
```

*Documentation reproduced from package spatstat, version 1.29-0, License: GPL (>= 2)*