pcfcross.inhom(X, i, j, lambdaI = NULL, lambdaJ = 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.X to which distances are measured.i.
Either a vector giving the intensity values
at the points of type i,
a pixel image (object of class "im") giving the
ij.
A numeric vector, pixel image or function(x,y).r.
Not normally invoked by the user.density.default.density.default.density.default.density.ppp
to control the smoothing bandwidth, when lambdaI or
lambdaJ is estimated by kernel smoothing."fv").
Essentially a data frame containing the variablesThe best intuitive interpretation is the following: the probability $p(r)$ of finding two points, of types $i$ and $j$ respectively, at locations $x$ and $y$ separated by a distance $r$ is equal to $$p(r) = \lambda_i(x) lambda_j(y) g(r) \,{\rm d}x \, {\rm d}y$$ where $\lambda_i$ is the intensity function of the process of points of type $i$. For a multitype Poisson point process, this probability is $p(r) = \lambda_i(x) \lambda_j(y)$ so $g_{ij}(r) = 1$.
The command pcfcross.inhom estimates the inhomogeneous
pair correlation using a modified version of
the algorithm in pcf.ppp.
If the arguments lambdaI and lambdaJ are missing or
null, they are estimated from X by kernel smoothing using a
leave-one-out estimator.
pcf.ppp,
pcfinhom,
pcfcross,
pcfdot.inhomdata(amacrine)
plot(pcfcross.inhom(amacrine, "on", "off", stoyan=0.1))Run the code above in your browser using DataLab