# Kdot

##### Multitype K Function (i-to-any)

For a multitype point pattern, estimate the multitype $K$ function which counts the expected number of other points of the process within a given distance of a point of type $i$.

- Keywords
- spatial, nonparametric

##### Usage

`Kdot(X, i, r=NULL, breaks=NULL, correction, ...)`

##### Arguments

- X
- The observed point pattern, from which an estimate of the multitype $K$ function $K_{i\bullet}(r)$ will be computed. It must be a multitype point pattern (a marked point pattern whose marks are a factor). See under Details.
- i
- Number or character string identifying the type (mark value)
of the points in
`X`

from which distances are measured. Defaults to the first level of`marks(X)`

. - r
- numeric vector. The values of the argument $r$ at which the distribution function $K_{i\bullet}(r)$ should be evaluated. There is a sensible default. First-time users are strongly advised not to specify this argument. See below for imp
- breaks
- An alternative to the argument
`r`

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

,`"bord.modif"`

,`"isotropic"`

,`"Ripley"`

,`"translate"`

,`"none"`

or`"best"`

. It specifie - ...
- Ignored.

##### Details

This function `Kdot`

and its companions
`Kcross`

and `Kmulti`

are generalisations of the function `Kest`

to multitype point patterns.

A multitype point pattern is a spatial pattern of
points classified into a finite number of possible
``colours'' or ``types''. In the `X`

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

) or any data that are acceptable to `as.ppp`

.
It must be a marked point pattern, and the mark vector
`X$marks`

must be a factor.

The argument `i`

will be interpreted as a
level of the factor `X$marks`

.
If `i`

is missing, it defaults to the first
level of the marks factor, `i = levels(X$marks)[1]`

.
The ``type $i$ to any type'' multitype $K$ function
of a stationary multitype point process $X$ is defined so that
$\lambda K_{i\bullet}(r)$
equals the expected number of
additional random points within a distance $r$ of a
typical point of type $i$ in the process $X$.
Here $\lambda$
is the intensity of the process,
i.e. the expected number of points of $X$ per unit area.
The function $K_{i\bullet}$ is determined by the
second order moment properties of $X$.

An estimate of $K_{i\bullet}(r)$ is a useful summary statistic in exploratory data analysis of a multitype point pattern. If the subprocess of type $i$ points were independent of the subprocess of points of all types not equal to $i$, then $K_{i\bullet}(r)$ would equal $\pi r^2$. Deviations between the empirical $K_{i\bullet}$ curve and the theoretical curve $\pi r^2$ may suggest dependence between types.

This algorithm estimates the distribution function $K_{i\bullet}(r)$
from the point pattern `X`

. It assumes that `X`

can be treated
as a realisation of a stationary (spatially homogeneous)
random spatial point process in the plane, observed through
a bounded window.
The window (which is specified in `X`

as `X$window`

)
may have arbitrary shape.
Biases due to edge effects are
treated in the same manner as in `Kest`

,
using the border correction.

The argument `r`

is the vector of values for the
distance $r$ at which $K_{i\bullet}(r)$ should be evaluated.
The values of $r$ must be increasing nonnegative numbers
and the maximum $r$ value must exceed the radius of the
largest disc contained in the window.

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

; see `pcf`

.

##### Value

- An object of class
`"fv"`

(see`fv.object`

).Essentially a data frame containing numeric columns

r the values of the argument $r$ at which the function $K_{i\bullet}(r)$ has been estimated theo the theoretical value of $K_{i\bullet}(r)$ for a marked Poisson process, namely $\pi r^2$ - together with a column or columns named
`"border"`

,`"bord.modif"`

,`"iso"`

and/or`"trans"`

, according to the selected edge corrections. These columns contain estimates of the function $K_{i\bullet}(r)$ obtained by the edge corrections named.

##### Warnings

The argument `i`

is interpreted as
a level of the factor `X$marks`

. Beware of the usual
trap with factors: numerical values are not
interpreted in the same way as character values. See the first example.

The reduced sample estimator of $K_{i\bullet}$ is pointwise approximately unbiased, but need not be a valid distribution function; it may not be a nondecreasing function of $r$. Its range is always within $[0,1]$.

##### References

Cressie, N.A.C. *Statistics for spatial data*.
John Wiley and Sons, 1991.

Diggle, P.J. *Statistical analysis of spatial point patterns*.
Academic Press, 1983.

Harkness, R.D and Isham, V. (1983)
A bivariate spatial point pattern of ants' nests.
*Applied Statistics* **32**, 293--303
Lotwick, H. W. and Silverman, B. W. (1982).
Methods for analysing spatial processes of several types of points.
*J. Royal Statist. Soc. Ser. B* **44**, 406--413.

Ripley, B.D. *Statistical inference for spatial processes*.
Cambridge University Press, 1988.

Stoyan, D, Kendall, W.S. and Mecke, J.
*Stochastic geometry and its applications*.
2nd edition. Springer Verlag, 1995.

##### See Also

##### Examples

```
# Lansing woods data: 6 types of trees
data(lansing)
Kh. <- Kdot(lansing, "hickory")
<testonly>sub <- lansing[seq(1,lansing$n, by=80), ]
Kh. <- Kdot(sub, "hickory")</testonly>
# diagnostic plot for independence between hickories and other trees
plot(Kh.)
# synthetic example with two marks "a" and "b"
pp <- runifpoispp(50)
pp <- pp %mark% factor(sample(c("a","b"), pp$n, replace=TRUE))
K <- Kdot(pp, "a")
```

*Documentation reproduced from package spatstat, version 1.19-1, License: GPL (>= 2)*