spatstat (version 1.47-0)

GmultiInhom: Inhomogeneous Marked G-Function

Description

For a marked point pattern, estimate the inhomogeneous version of the multitype $G$ function, effectively the cumulative distribution function of the distance from a point in subset $I$ to the nearest point in subset $J$, adjusted for spatially varying intensity.

Usage

GmultiInhom(X, I, J, lambda = NULL, lambdaI = NULL, lambdaJ = NULL, lambdamin = NULL, ..., r = NULL, ReferenceMeasureMarkSetI = NULL, ratio = FALSE)

Arguments

X

A spatial point pattern (object of class "ppp".

I

A subset index specifying the subset of points from which distances are measured. Any kind of subset index acceptable to [.ppp.

J

A subset index specifying the subset of points to which distances are measured. Any kind of subset index acceptable to [.ppp.

lambda

Intensity estimates for each point of X. A numeric vector of length equal to npoints(X). Incompatible with lambdaI,lambdaJ.

lambdaI

Intensity estimates for each point of X[I]. A numeric vector of length equal to npoints(X[I]). Incompatible with lambda.

lambdaJ

Intensity estimates for each point of X[J]. A numeric vector of length equal to npoints(X[J]). Incompatible with lambda.

lambdamin

A lower bound for the intensity, or at least a lower bound for the values in lambdaJ or lambda[J].

...

Ignored.

r

Vector of distance values at which the inhomogeneous $G$ function should be estimated. There is a sensible default.

ReferenceMeasureMarkSetI

Optional. The total measure of the mark set. A positive number.

ratio

Logical value indicating whether to save ratio information.

Value

Object of class "fv" containing the estimate of the inhomogeneous multitype $G$ function.

Details

See Cronie and Van Lieshout (2015).

References

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

See Also

Ginhom, Gmulti

Examples

  X <- amacrine
  I <- (marks(X) == "on")
  J <- (marks(X) == "off")
  mod <- ppm(X ~ marks * x)
  lam <- fitted(mod, dataonly=TRUE)
  lmin <- min(predict(mod)[["off"]]) * 0.9
  plot(GmultiInhom(X, I, J, lambda=lam, lambdamin=lmin))

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