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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