spatstat (version 1.39-0)

linearpcfinhom: Inhomogeneous Linear Pair Correlation Function

Description

Computes an estimate of the inhomogeneous linear pair correlation function for a point pattern on a linear network.

Usage

linearpcfinhom(X, lambda=NULL, r=NULL, ..., correction="Ang", normalise=TRUE)

Arguments

X

Point pattern on linear network (object of class "lpp").

lambda

Intensity values for the point pattern. Either a numeric vector, a function, a pixel image (object of class "im") or a fitted point process model (object of class "ppm" or "lppm").

r

Optional. Numeric vector of values of the function argument $r$. There is a sensible default.

...

Arguments passed to density.default to control the smoothing.

correction

Geometry correction. Either "none" or "Ang". See Details.

normalise

Logical. If TRUE (the default), the denominator of the estimator is data-dependent (equal to the sum of the reciprocal intensities at the data points), which reduces the sampling variability. If FALSE, the denominato

Value

• Function value table (object of class "fv").

Details

This command computes the inhomogeneous version of the linear pair correlation function from point pattern data on a linear network.

If lambda = NULL the result is equivalent to the homogeneous pair correlation function linearpcf. If lambda is given, then it is expected to provide estimated values of the intensity of the point process at each point of X. The argument lambda may be a numeric vector (of length equal to the number of points in X), or a function(x,y) that will be evaluated at the points of X to yield numeric values, or a pixel image (object of class "im") or a fitted point process model (object of class "ppm" or "lppm").

If correction="none", the calculations do not include any correction for the geometry of the linear network. If correction="Ang", the pair counts are weighted using Ang's correction (Ang, 2010).

References

Ang, Q.W. (2010) Statistical methodology for spatial point patterns on a linear network. MSc thesis, University of Western Australia.

Ang, Q.W., Baddeley, A. and Nair, G. (2012) Geometrically corrected second-order analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics 39, 591--617.

Okabe, A. and Yamada, I. (2001) The K-function method on a network and its computational implementation. Geographical Analysis 33, 271-290.

See Also

linearpcf, linearKinhom, lpp

Examples

data(simplenet)
  X <- rpoislpp(5, simplenet)
  fit <- lppm(X, ~x)
  K <- linearpcfinhom(X, lambda=fit)
  plot(K)