Gcross(X, i, j, r=NULL, breaks=NULL, ..., correction=c("rs", "km", "han"))X from which distances are measured.
Defaults to the first level of marks(X).X to which distances are measured.
Defaults to the second level of marks(X).r.
Not normally invoked by the user. See the Details section."none", "rs", "km",
"hanisch" and "best"."fv" (see fv.object).
Essentially a data frame containing six numeric columnsi and j are interpreted as
levels 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.The function $G_{ij}$ does not necessarily have a density.
The reduced sample estimator of $G_{ij}$ is pointwise approximately unbiased, but need not be a valid distribution function; it may not be a nondecreasing function of $r$. Its range is always within $[0,1]$.
The spatial Kaplan-Meier estimator of $G_{ij}$ is always nondecreasing but its maximum value may be less than $1$.
Gcross and its companions
Gdot and Gmulti
are generalisations of the function Gest
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$marks must be a factor.
The arguments i and j will be interpreted as
levels of the factor X$marks. (Warning: this means that
an integer value i=3 will be interpreted as the 3rd smallest level,
not the number 3).
The ``cross-type'' (type $i$ to type $j$)
nearest neighbour distance distribution function
of a multitype point process
is the cumulative distribution function $G_{ij}(r)$
of the distance from a typical random point of the process with type $i$
the nearest point of type $j$.
An estimate of $G_{ij}(r)$ is a useful summary statistic in exploratory data analysis of a multitype point pattern. If the process of type $i$ points were independent of the process of type $j$ points, then $G_{ij}(r)$ would equal $F_j(r)$, the empty space function of the type $j$ points. For a multitype Poisson point process where the type $i$ points have intensity $\lambda_i$, we have $$G_{ij}(r) = 1 - e^{ - \lambda_j \pi r^2}$$ Deviations between the empirical and theoretical $G_{ij}$ curves may suggest dependence between the points of types $i$ and $j$.
This algorithm estimates the distribution function $G_{ij}(r)$
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 Gest.
The argument r is the vector of values for the
distance $r$ at which $G_{ij}(r)$ 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.
The algorithm also returns an estimate of the hazard rate function, $\lambda(r)$, of $G_{ij}(r)$. This estimate should be used with caution as $G_{ij}(r)$ is not necessarily differentiable.
The naive empirical distribution of distances from each point of
the pattern X to the nearest other point of the pattern,
is a biased estimate of $G_{ij}$.
However this is also returned by the algorithm, as it is sometimes
useful in other contexts. Care should be taken not to use the uncorrected
empirical $G_{ij}$ as if it were an unbiased estimator of
$G_{ij}$.
Diggle, P.J. Statistical analysis of spatial point patterns. Academic Press, 1983.
Diggle, P. J. (1986). Displaced amacrine cells in the retina of a rabbit : analysis of a bivariate spatial point pattern. J. Neurosci. Meth. 18, 115--125. Harkness, R.D and Isham, V. (1983) A bivariate spatial point pattern of ants' nests. Applied Statistics 32, 293--303 Lotwick, H. W. and Silverman, B. W. (1982). Methods for analysing spatial processes of several types of points. J. Royal Statist. Soc. Ser. B 44, 406--413.
Ripley, B.D. Statistical inference for spatial processes. Cambridge University Press, 1988.
Stoyan, D, Kendall, W.S. and Mecke, J. Stochastic geometry and its applications. 2nd edition. Springer Verlag, 1995.
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.
Gdot,
Gest,
Gmultidata(betacells)
# cat retina data
G01 <- Gcross(betacells)
# equivalent to:
G01 <- Gcross(betacells, "off", "on")
plot(G01)
# empty space function of `on' points
F1 <- Fest(split(betacells)$on, r = G01$r, eps=10.0)
lines(F1$r, F1$km, lty=3)
# synthetic example
pp <- runifpoispp(30)
pp <- pp %mark% factor(sample(0:1, pp$n, replace=TRUE))
G <- Gcross(pp, "0", "1") # note: "0" not 0Run the code above in your browser using DataLab