| Jdot {spatstat} | R Documentation |
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=1, eps=NULL, r=NULL, breaks=NULL, ...)
X |
The observed point pattern, from which an estimate of the multitype J function Ji.(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.
|
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 Ji.(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 |
An alternative to the argument r.
Not normally invoked by the user. See the Details section.
|
... |
Ignored. |
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 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
Ji.(r) = (1 - Gi.(r))/(1-F.(r))
where Gi.(r) is the distribution function of the distance from a type i point to the nearest other point of the pattern, and F.(r) is the distribution function of the distance from a fixed point in space to the nearest point of the pattern.
An estimate of Ji.(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
Ji.(r) = 1.
If the subprocess of type i points is independent
of the subprocess of points of all types not equal to i,
then Ji.(r) equals
Jii(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
Ji.-Jii
may suggest dependence between types.
This algorithm estimates Ji.(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$window)
may have arbitrary shape.
Biases due to edge effects are
treated in the same manner as in Jest,
using the Kaplan-Meier and border corrections.
The main work is done by Gmulti and Fest.
The argument r is the vector of values for the
distance r at which Ji.(r) should be evaluated.
The values of r must be increasing nonnegative numbers
and the maximum r value must exceed the radius of the
largest disc contained in the window.
An object of class "fv" (see fv.object).
Essentially a data frame containing six numeric columns
J |
the recommended estimator of Ji.(r), currently the Kaplan-Meier estimator. |
r |
the values of the argument r at which the function Ji.(r) has been estimated |
km |
the Kaplan-Meier estimator of Ji.(r) |
rs |
the ``reduced sample'' or ``border correction'' estimator of Ji.(r) |
un |
the ``uncorrected''
estimator of Ji.(r)
formed by taking the ratio of uncorrected empirical estimators
of 1 - Gi.(r)
and 1 - F.(r), see
Gdot and Fest.
|
theo |
the theoretical value of Ji.(r) for a marked Poisson process, namely 1. |
The result also has two attributes "G" and "F"
which are respectively the outputs of Gdot
and Fest for the point pattern.
The argument i is interpreted as
a level of the factor X$marks. Beware of the usual
trap with factors: numerical values are not
interpreted in the same way as character values. See the first example.
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf
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.
# Lansing woods data: 6 types of trees
data(lansing)
Jh. <- Jdot(lansing, "hickory")
plot(Jh.)
# diagnostic plot for independence between hickories and other trees
Jhh <- Jest(lansing[lansing$marks == "hickory", ])
plot(Jhh, add=TRUE)
# synthetic example with two marks "a" and "b"
pp <- runifpoispp(50)
pp <- pp %mark% sample(c("a","b"), pp$n, replace=TRUE)
J <- Jdot(pp, "a")