# Jdot

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

For a multitype point pattern, estimate the multitype $J$ function summarising the interpoint dependence between the type $i$ points and the points of any type.

- Keywords
- spatial, nonparametric

##### Usage

`Jdot(X, i, eps=NULL, r=NULL, breaks=NULL, ...)`

##### Arguments

- X
- The observed point pattern, from which an estimate of the multitype $J$ function $J_{i\bullet}(r)$ 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)`

. - eps
- A positive number. The resolution of the discrete approximation to Euclidean distance (see below). There is a sensible default.
- r
- numeric vector. The values of the argument $r$ at which the function $J_{i\bullet}(r)$ should be evaluated. There is a sensible default. First-time users are strongly advised not to specify this argument. See below for important condit
- breaks
- An alternative to the argument
`r`

. Not normally invoked by the user. See the**Details**section. - ...
- Ignored.

##### Details

This function `Jdot`

and its companions
`Jcross`

and `Jmulti`

are generalisations of the function `Jest`

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`

. (Warning: this means that
an integer value `i=3`

will be interpreted as the 3rd smallest level,
not the number 3).
The ``type $i$ to any type'' multitype $J$ function
of a stationary multitype point process $X$
was introduced by Van lieshout and Baddeley (1999). It is defined by
$$J_{i\bullet}(r) = \frac{1 - G_{i\bullet}(r)}{1 -
F_{\bullet}(r)}$$
where $G_{i\bullet}(r)$ is the distribution function of
the distance from a type $i$ point to the nearest other point
of the pattern, and $F_{\bullet}(r)$ is the distribution
function of the distance from a fixed point in space to the nearest
point of the pattern.

An estimate of $J_{i\bullet}(r)$
is a useful summary statistic in exploratory data analysis
of a multitype point pattern. If the pattern is
a marked Poisson point process, then
$J_{i\bullet}(r) \equiv 1$.
If the subprocess of type $i$ points is independent
of the subprocess of points of all types not equal to $i$,
then $J_{i\bullet}(r)$ equals
$J_{ii}(r)$, the ordinary $J$ function
(see `Jest`

and Van Lieshout and Baddeley (1996))
of the points of type $i$.
Hence deviations from zero of the empirical estimate of
$J_{i\bullet} - J_{ii}$
may suggest dependence between types.

This algorithm estimates $J_{i\bullet}(r)$
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 `Jest`

,
using the Kaplan-Meier and border corrections.
The main work is done by `Gmulti`

and `Fest`

.

The argument `r`

is the vector of values for the
distance $r$ at which $J_{i\bullet}(r)$ 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.

##### Value

- An object of class
`"fv"`

(see`fv.object`

).Essentially a data frame containing six numeric columns

J the recommended estimator of $J_{i\bullet}(r)$, currently the Kaplan-Meier estimator. r the values of the argument $r$ at which the function $J_{i\bullet}(r)$ has been estimated km the Kaplan-Meier estimator of $J_{i\bullet}(r)$ rs the ``reduced sample'' or ``border correction'' estimator of $J_{i\bullet}(r)$ un the ``uncorrected'' estimator of $J_{i\bullet}(r)$ formed by taking the ratio of uncorrected empirical estimators of $1 - G_{i\bullet}(r)$ and $1 - F_{\bullet}(r)$, see `Gdot`

and`Fest`

.theo the theoretical value of $J_{i\bullet}(r)$ for a marked Poisson process, namely 1. - The result also has two attributes
`"G"`

and`"F"`

which are respectively the outputs of`Gdot`

and`Fest`

for the point pattern.

##### 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.

##### References

Van Lieshout, M.N.M. and Baddeley, A.J. (1996)
A nonparametric measure of spatial interaction in point patterns.
*Statistica Neerlandica* **50**, 344--361.

Van Lieshout, M.N.M. and Baddeley, A.J. (1999)
Indices of dependence between types in multivariate point patterns.
*Scandinavian Journal of Statistics* **26**, 511--532.

##### See Also

##### Examples

```
# Lansing woods data: 6 types of trees
data(lansing)
<testonly>lansing <- lansing[seq(1,lansing$n, by=30), ]</testonly>
Jh. <- Jdot(lansing, "hickory")
plot(Jh.)
# diagnostic plot for independence between hickories and other trees
Jhh <- Jest(lansing[lansing$marks == "hickory", ])
plot(Jhh, add=TRUE)
# synthetic example with two marks "a" and "b"
pp <- runifpoint(50) %mark% factor(sample(c("a","b"), 50, replace=TRUE))
J <- Jdot(pp, "a")
```

*Documentation reproduced from package spatstat, version 1.15-2, License: GPL (>= 2)*