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, ..., correction=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
- 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 ofmarks(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
- 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"
.
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 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 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"
(seefv.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 $J_{i\bullet}(r)$ han the Hanisch-style 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
andFest
.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 ofGdot
andFest
for the point pattern.
Warnings
The argument i
is interpreted as
a level of the factor X$marks
. It is converted to a character
string if it is not already a character string.
The value i=1
does not
refer to the first level of the factor.
References
Van Lieshout, M.N.M. and Baddeley, A.J. (1996) A nonparametric measure of spatial interaction in point patterns. Statistica Neerlandica 50, 344--361.
Van Lieshout, M.N.M. and Baddeley, A.J. (1999) Indices of dependence between types in multivariate point patterns. Scandinavian Journal of Statistics 26, 511--532.
See Also
Examples
# Lansing woods data: 6 types of trees
data(lansing)
<testonly>lansing <- lansing[seq(1,lansing$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, legendpos="bottom")
# synthetic example with two marks "a" and "b"
pp <- runifpoint(30) %mark% factor(sample(c("a","b"), 30, replace=TRUE))
J <- Jdot(pp, "a")