For a multitype point pattern,
estimate the multitype
Jcross(X, i, j, eps=NULL, r=NULL, breaks=NULL, ..., correction=NULL)
An object of class "fv"
(see fv.object
).
Essentially a data frame containing six numeric columns
the recommended
estimator of
the values of the argument
the Kaplan-Meier
estimator of
the ``reduced sample'' or ``border correction''
estimator of
the Hanisch-style
estimator of
the ``uncorrected''
estimator of Gdot
and Fest
.
the theoretical value of
The result also has two attributes "G"
and "F"
which are respectively the outputs of Gcross
and Fest
for the point pattern.
The observed point pattern,
from which an estimate of the multitype
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)
.
The type (mark value)
of the points in X
to which distances are measured.
A character string (or something that will be
converted to a character string).
Defaults to the second level of marks(X)
.
A positive number. The resolution of the discrete approximation to Euclidean distance (see below). There is a sensible default.
Optional. Numeric vector. The values of the argument
This argument is for internal use only.
Ignored.
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.
The arguments i
and j
are always interpreted as
levels of the factor X$marks
. They are converted to character
strings if they are not already character strings.
The value i=1
does not
refer to the first level of the factor.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
This function Jcross
and its companions
Jdot
and Jmulti
are generalisations of the function Jest
to multitype point patterns.
A multitype point pattern is a spatial pattern of points classified into a finite number of possible ``colours'' or ``types''. In the spatstat package, a multitype pattern is represented as a single point pattern object in which the points carry marks, and the mark value attached to each point determines the type of that point.
The argument X
must be a point pattern (object of class
"ppp"
) or any data that are acceptable to as.ppp
.
It must be a marked point pattern, and the mark vector
X$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
An estimate of
This algorithm estimates 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
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.
Jdot
,
Jest
,
Jmulti
# Lansing woods data: 6 types of trees
woods <- lansing
# \testonly{
woods <- woods[seq(1,npoints(woods), by=30)]
# }
Jhm <- Jcross(woods, "hickory", "maple")
# diagnostic plot for independence between hickories and maples
plot(Jhm)
# synthetic example with two types "a" and "b"
pp <- runifpoint(30) %mark% factor(sample(c("a","b"), 30, replace=TRUE))
J <- Jcross(pp)
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