| Kcross {spatstat} | R Documentation |
For a multitype point pattern, estimate the multitype K function which counts the expected number of points of type j within a given distance of a point of type i.
Kcross(X, i=1, j=2, r=NULL, breaks=NULL, correction, ...)
X |
The observed point pattern, from which an estimate of the cross type K function Kij(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.
|
j |
Number or character string identifying the type (mark value)
of the points in X to which distances are measured.
|
r |
numeric vector. The values of the argument r at which the distribution function Kij(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.
|
correction |
A character vector containing any selection of the
options "border", "bord.modif",
"isotropic", "Ripley" or "translate".
It specifies the edge correction(s) to be applied.
|
... |
Ignored. |
This function Kcross and its companions
Kdot and Kmulti
are generalisations of the function Kest
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$marks must be a factor.
The arguments i and j will be interpreted as
levels of the factor X$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) K function of a stationary multitype point process X is defined so that lambda[j] Kij(r) equals the expected number of additional random points of type j within a distance r of a typical point of type i in the process X. Here lambda[j] is the intensity of the type j points, i.e. the expected number of points of type j per unit area. The function Kij is determined by the second order moment properties of X.
An estimate of Kij(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 Kij(r) would equal pi * r^2. Deviations between the empirical Kij curve and the theoretical curve pi * r^2 may suggest dependence between the points of types i and j.
This algorithm estimates the distribution function Kij(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$window)
may have arbitrary shape.
Biases due to edge effects are
treated in the same manner as in Kest,
using the border correction.
The argument r is the vector of values for the
distance r at which Kij(r) should be evaluated.
The values of r must be increasing nonnegative numbers
and the maximum r value must exceed the radius of the
largest disc contained in the window.
The pair correlation function can also be applied to the
result of Kcross; see pcf.
An object of class "fv" (see fv.object).
Essentially a data frame containing numeric columns
r |
the values of the argument r at which the function Kij(r) has been estimated |
theo |
the theoretical value of Kij(r) for a marked Poisson process, namely pi * r^2 |
together with a column or columns named
"border", "bord.modif",
"iso" and/or "trans",
according to the selected edge corrections. These columns contain
estimates of the function Kij(r)
obtained by the edge corrections named.
The arguments i and j are interpreted as
levels of the factor X$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 reduced sample estimator of Kij 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].
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
Cressie, N.A.C. Statistics for spatial data. John Wiley and Sons, 1991.
Diggle, P.J. Statistical analysis of spatial point patterns. Academic Press, 1983.
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.
data(betacells)
# cat retina data
K01 <- Kcross(betacells, "off", "on")
plot(K01)
K10 <- Kcross(betacells, "on", "off")
# synthetic example
pp <- runifpoispp(50)
pp <- pp %mark% factor(sample(0:1, pp$n, replace=TRUE))
K <- Kcross(pp, "0", "1") # note: "0" not 0