i to points of any type)
for a multitype point pattern.
pcfdot(X, i, ..., r = NULL, kernel = "epanechnikov", bw = NULL, stoyan = 0.15, correction = c("isotropic", "Ripley", "translate"), divisor = c("r", "d"))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).
density.default.
density.default.
"r" (the default) or "d". See Details.
"fv", see fv.object,
which can be plotted directly using plot.fv.Essentially a data frame containing columnstogether with columns named
"border", "bord.modif",
"iso" and/or "trans",
according to the selected edge corrections. These columns contain
estimates of the function $g[i,j]$
obtained by the edge corrections named.
pcf
to multitype point patterns. For two locations $x$ and $y$ separated by a nonzero
distance $r$,
the probability $p(r)$ of finding a point of type $i$ at location
$x$ and a point of any type at location $y$ is
$$
p(r) = \lambda_i \lambda g_{i\bullet}(r) \,{\rm d}x \, {\rm d}y
$$
where $lambda$ is the intensity of all points,
and $lambda[i]$ is the intensity of the points
of type $i$.
For a completely random Poisson marked point process,
$p(r) = lambda[i] * lambda$
so $gdot[i](r) = 1$.
For a stationary multitype point process, the
type-i-to-any-type pair correlation
function between marks $i$ and $j$ is formally defined as
$$
g_{i\bullet}(r) = \frac{K_{i\bullet}^\prime(r)}{2\pi r}
$$
where $Kdot[i]'(r)$ is the derivative of
the type-i-to-any-type $K$ function
$Kdot[i](r)$.
of the point process. See Kdot for information
about $Kdot[i](r)$.
The command pcfdot computes a kernel estimate of
the multitype pair correlation function from points of type $i$
to points of any type.
divisor="r" (the default), then the multitype
counterpart of the standard
kernel estimator (Stoyan and Stoyan, 1994, pages 284--285)
is used. By default, the recommendations of Stoyan and Stoyan (1994)
are followed exactly.
divisor="d" then a modified estimator is used:
the contribution from
an interpoint distance $d[ij]$ to the
estimate of $g(r)$ is divided by $d[ij]$
instead of dividing by $r$. This usually improves the
bias of the estimator when $r$ is close to zero.
There is also a choice of spatial edge corrections
(which are needed to avoid bias due to edge effects
associated with the boundary of the spatial window):
correction="translate" is the Ohser-Stoyan translation
correction, and correction="isotropic" or "Ripley"
is Ripley's isotropic correction.
The choice of smoothing kernel is controlled by the
argument kernel which is passed to density.
The default is the Epanechnikov kernel.
The bandwidth of the smoothing kernel can be controlled by the
argument bw. Its precise interpretation
is explained in the documentation for density.default.
For the Epanechnikov kernel with support $[-h,h]$,
the argument bw is equivalent to $h/sqrt(5)$.
If bw is not specified, the default bandwidth
is determined by Stoyan's rule of thumb (Stoyan and Stoyan, 1994, page
285). That is,
$h = c/sqrt(lambda)$,
where $lambda$ is the (estimated) intensity of the
unmarked point process,
and $c$ is a constant in the range from 0.1 to 0.2.
The argument stoyan determines the value of $c$.
The companion function pcfcross computes the
corresponding analogue of Kcross.
markconnect. Multitype pair correlation pcfcross, pcfmulti.
Pair correlation pcf,pcf.ppp.
Kdot
data(amacrine)
p <- pcfdot(amacrine, "on")
p <- pcfdot(amacrine, "on", stoyan=0.1)
plot(p)
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