linearpcfinhom {spatstat} | R Documentation |
Computes an estimate of the inhomogeneous linear pair correlation function for a point pattern on a linear network.
linearpcfinhom(X, lambda=NULL, r=NULL, ..., correction="Ang", normalise=TRUE)
X |
Point pattern on linear network (object of class |
lambda |
Intensity values for the point pattern. Either a numeric vector,
a |
r |
Optional. Numeric vector of values of the function argument r. There is a sensible default. |
... |
Arguments passed to |
correction |
Geometry correction.
Either |
normalise |
Logical. If |
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).
Function value table (object of class "fv"
).
Ang Qi Wei aqw07398@hotmail.com and Adrian Baddeley Adrian.Baddeley@csiro.au http://www.maths.uwa.edu.au/~adrian/
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. To appear in Scandinavian Journal of Statistics.
Okabe, A. and Yamada, I. (2001) The K-function method on a network and its computational implementation. Geographical Analysis 33, 271-290.
data(simplenet) X <- rpoislpp(5, simplenet) fit <- lppm(X, ~x) K <- linearpcfinhom(X, lambda=fit) plot(K)