pcfdot.inhom(X, i, lambdaI = NULL, lambdadot = NULL, ..., r = NULL, breaks = NULL, kernel="epanechnikov", bw=NULL, stoyan=0.15, correction = c("isotropic", "Ripley", "translate"), sigma = NULL, varcov = NULL)
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)
.
i
.
Either a vector giving the intensity values
at the points of type i
,
a pixel image (object of class "im"
) giving the
intensity values at all locations, or a function(x,y)
which
can be evaluated to give the intensity value at any location.
X
.
A numeric vector, pixel image or function(x,y)
.
density.default
.
density.default
.
density.default
.
density.ppp
to control the smoothing bandwidth, when lambdaI
or
lambdadot
is estimated by kernel smoothing.
"fv"
).
Essentially a data frame containing the variablesas required.
The best intuitive interpretation is the following: the probability $p(r)$ of finding a point of type $i$ at location $x$ and another point of any type at location $y$, where $x$ and $y$ are separated by a distance $r$, is equal to $$ p(r) = \lambda_i(x) lambda(y) g(r) \,{\rm d}x \, {\rm d}y $$ where $lambda[i]$ is the intensity function of the process of points of type $i$, and where $lambda$ is the intensity function of the points of all types. For a multitype Poisson point process, this probability is $p(r) = lambda[i](x) * lambda(y)$ so $g[i.](r) = 1$.
The command pcfdot.inhom
estimates the inhomogeneous
multitype pair correlation using a modified version of
the algorithm in pcf.ppp
.
If the arguments lambdaI
and lambdadot
are missing or
null, they are estimated from X
by kernel smoothing using a
leave-one-out estimator.
pcf.ppp
,
pcfinhom
,
pcfdot
,
pcfcross.inhom
data(amacrine)
plot(pcfdot.inhom(amacrine, "on", stoyan=0.1), legendpos="bottom")
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