Jmulti: Marked J Function

In spatstat.explore: Exploratory Data Analysis for the 'spatstat' Family

Marked J Function

Description

For a marked point pattern, estimate the multitype J function summarising dependence between the points in subset I and those in subset J.

Usage

  Jmulti(X, I, J, eps=NULL, r=NULL, breaks=NULL, ..., disjoint=NULL,
         correction=NULL)

Arguments

X	The observed point pattern, from which an estimate of the multitype distance distribution function J_{IJ}(r) will be computed. It must be a marked point pattern. See under Details.
I	Subset of points of X from which distances are measured. See Details.
J	Subset of points in X to which distances are measured. See Details.
eps	A positive number. The pixel resolution of the discrete approximation to Euclidean distance (see Jest). There is a sensible default.
r	numeric vector. The values of the argument r at which the distribution function J_{IJ}(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.
disjoint	Optional flag indicating whether the subsets I and J are disjoint. If missing, this value will be computed by inspecting the vectors I and J.
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.

Details

The function Jmulti generalises Jest (for unmarked point patterns) and Jdot and Jcross (for multitype point patterns) to arbitrary marked point patterns.

Suppose X_I, X_J are subsets, possibly overlapping, of a marked point process. Define

J_{IJ}(r) = \frac{1 - G_{IJ}(r)}{1 - F_J(r)}

where F_J(r) is the cumulative distribution function of the distance from a fixed location to the nearest point of X_J, and G_{IJ}(r) is the distribution function of the distance from a typical point of X_I to the nearest distinct point of X_J.

The argument X must be a point pattern (object of class "ppp") or any data that are acceptable to as.ppp.

The arguments I and J specify two subsets of the point pattern. They may be any type of subset indices, for example, logical vectors of length equal to npoints(X), or integer vectors with entries in the range 1 to npoints(X), or negative integer vectors.

Alternatively, I and J may be functions that will be applied to the point pattern X to obtain index vectors. If I is a function, then evaluating I(X) should yield a valid subset index. This option is useful when generating simulation envelopes using envelope.

It is assumed 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.

The argument r is the vector of values for the distance r at which J_{IJ}(r) should be evaluated. It is also used to determine the breakpoints (in the sense of hist) for the computation of histograms of distances. The reduced-sample and Kaplan-Meier estimators are computed from histogram counts. In the case of the Kaplan-Meier estimator this introduces a discretisation error which is controlled by the fineness of the breakpoints.

First-time users would be strongly advised not to specify r. However, if it is specified, r must satisfy r[1] = 0, and max(r) must be larger than the radius of the largest disc contained in the window. Furthermore, the successive entries of r must be finely spaced.

Value

An object of class "fv" (see fv.object).

Essentially a data frame containing six numeric columns

r	the values of the argument r at which the function J_{IJ}(r) has been estimated
rs	the “reduced sample” or “border correction” estimator of J_{IJ}(r)
km	the spatial Kaplan-Meier estimator of J_{IJ}(r)
han	the Hanisch-style estimator of J_{IJ}(r)
un	the uncorrected estimate of J_{IJ}(r), formed by taking the ratio of uncorrected empirical estimators of 1 - G_{IJ}(r) and 1 - F_{J}(r), see Gdot and Fest.
theo	the theoretical value of J_{IJ}(r) for a marked Poisson process with the same estimated intensity, namely 1.

Author(s)

\spatstatAuthors

.

References

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

Jcross, Jdot, Jest

Examples

    trees <- longleaf
     # Longleaf Pine data: marks represent diameter
    
    Jm <- Jmulti(trees, marks(trees) <= 15, marks(trees) >= 25)
    plot(Jm)

spatstat.explore documentation built on Oct. 23, 2023, 1:07 a.m.