| Jmulti.inhom | R Documentation | 
For a marked point pattern, 
estimate the inhomogeneous version of the multitype J function.
Jmulti.inhom(X, I, J,
             lambda = NULL, lambdaI = NULL, lambdaJ = NULL,
             lambdamin = NULL,
             ...,
             r = NULL,
             ReferenceMeasureMarkSetI = NULL,
             ratio = FALSE)
X | 
 The observed point pattern, 
from which an estimate of the inhomogeneous multitype   | 
I | 
 Subset index specifying the points of   | 
J | 
 Subset index specifying the points in   | 
lambda | 
 Optional.
Values of the estimated intensity function.
Either a vector giving the intensity values
at the points of the pattern   | 
lambdaI | 
 Optional.
Values of the estimated intensity of the sub-process   | 
lambdaJ | 
 Optional.
Values of the estimated intensity of the sub-process   | 
lambdamin | 
 Optional. The minimum possible value of the intensity over the spatial domain. A positive numerical value.  | 
... | 
 Extra arguments passed to   | 
r | 
 vector of values for the argument   | 
ReferenceMeasureMarkSetI | 
 Optional. The total measure of the mark set. A positive number.  | 
ratio | 
 Logical value indicating whether to save ratio information.  | 
This function is the counterpart of Jmulti
for inhomogeneous patterns. It is computed by evaluating the
inhomogeneous G function GmultiInhom 
and the inhomogeneous F function FmultiInhom
and computing the ratio J = (1-G)/(1-F).
Object of class "fv" containing the estimate of the
inhomogeneous multitype J function.
Jonatan \Gonzalez and \adrian.
Cronie, O. and Van Lieshout, M.N.M. (2015) Summary statistics for inhomogeneous marked point processes. Annals of the Institute of Statistical Mathematics DOI: 10.1007/s10463-015-0515-z
Jcross.inhom, Jdot.inhom
for special cases.
GmultiInhom, FmultiInhom,
Jmulti.
  X <- rescale(amacrine)
  I <- (marks(X) == "on")
  J <- (marks(X) == "off")
  if(interactive() && require(spatstat.model)) {
    ## how to do it normally
    mod <- ppm(X ~ marks * x)
    lam <- fitted(mod, dataonly=TRUE)
    lmin <- min(predict(mod)[["off"]]) * 0.9
    dd <- NULL
  } else {
    ## for package testing
    lam <- intensity(X)[as.integer(marks(X))]
    lmin <- intensity(X)[2] * 0.9
    dd <- 32
  }
  JM <- Jmulti.inhom(X, I, J, lambda=lam, lambdamin=lmin, dimyx=dd)
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