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.

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

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

The type (mark value)
of the points in `X`

from which distances are measured.
A character string (or something that will be converted to a
character string).
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 conditions on \(r\).

breaks

This argument is for internal use only.

…

Ignored.

correction

Optional. Character string specifying the edge correction(s)
to be used. Options are `"none"`

, `"rs"`

, `"km"`

,
`"Hanisch"`

and `"best"`

.
Alternatively `correction="all"`

selects all options.

An object of class `"fv"`

(see `fv.object`

).

Essentially a data frame containing six numeric columns

the recommended estimator of \(J_{i\bullet}(r)\), currently the Kaplan-Meier estimator.

the values of the argument \(r\) at which the function \(J_{i\bullet}(r)\) has been estimated

the Kaplan-Meier estimator of \(J_{i\bullet}(r)\)

the ``reduced sample'' or ``border correction'' estimator of \(J_{i\bullet}(r)\)

the Hanisch-style estimator of \(J_{i\bullet}(r)\)

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`

.

the theoretical value of \(J_{i\bullet}(r)\) for a marked Poisson process, namely 1.

The argument `i`

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

. It is converted to a character
string if it is not already a character string.
The value `i=1`

does **not**
refer to the first level of the factor.

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 spatstat package, a multitype pattern is represented as a single point pattern object in which the points carry marks, and the mark value attached to each point determines the type of that point.

The argument `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 number 3,
**not** the 3rd smallest level.)

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 `Window(X)`

)
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 not exceed the radius of the
largest disc contained in the window.

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.

# NOT RUN { # Lansing woods data: 6 types of trees woods <- lansing # } # NOT RUN { Jh. <- Jdot(woods, "hickory") plot(Jh.) # diagnostic plot for independence between hickories and other trees Jhh <- Jest(split(woods)$hickory) plot(Jhh, add=TRUE, legendpos="bottom") # } # NOT RUN { # synthetic example with two marks "a" and "b" pp <- runifpoint(30) %mark% factor(sample(c("a","b"), 30, replace=TRUE)) J <- Jdot(pp, "a") # }