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pcfdot.inhom

Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type)


Description

Estimates the inhomogeneous multitype pair correlation function (from type i to any type) for a multitype point pattern.

Usage

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)

Arguments

X

The observed point pattern, from which an estimate of the inhomogeneous multitype pair correlation function g[i.](r) will be computed. It must be a multitype point pattern (a marked point pattern whose marks are a factor).

i

The type (mark value) of the points in 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).

lambdaI

Optional. Values of the estimated intensity function of the points of type 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.

lambdadot

Optional. Values of the estimated intensity function of the point pattern X. A numeric vector, pixel image or function(x,y).

r

Vector of values for the argument r at which g[i.](r) should be evaluated. There is a sensible default.

breaks

This argument is for internal use only.

kernel

Choice of smoothing kernel, passed to density.default.

bw

Bandwidth for smoothing kernel, passed to density.default.

...

Other arguments passed to the kernel density estimation function density.default.

stoyan

Bandwidth coefficient; see Details.

correction

Choice of edge correction.

sigma,varcov

Optional arguments passed to density.ppp to control the smoothing bandwidth, when lambdaI or lambdadot is estimated by kernel smoothing.

Details

The inhomogeneous multitype (type i to any type) pair correlation function g[i.](r) is a summary of the dependence between different types of points in a multitype spatial point process that does not have a uniform density of points.

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) dx dy

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.

Value

A function value table (object of class "fv"). Essentially a data frame containing the variables

r

the vector of values of the argument r at which the inhomogeneous multitype pair correlation function g[i.](r) has been estimated

theo

vector of values equal to 1, the theoretical value of g[i.](r) for the Poisson process

trans

vector of values of g[i.](r) estimated by translation correction

iso

vector of values of g[i.](r) estimated by Ripley isotropic correction

as required.

Author(s)

and Rolf Turner r.turner@auckland.ac.nz

See Also

Examples

data(amacrine)
  plot(pcfdot.inhom(amacrine, "on", stoyan=0.1), legendpos="bottom")

spatstat.core

Core Functionality of the 'spatstat' Family

v2.1-2
GPL (>= 2)
Authors
Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut], Kasper Klitgaard Berthelsen [ctb], Achmad Choiruddin [ctb], Jean-Francois Coeurjolly [ctb], Ottmar Cronie [ctb], Tilman Davies [ctb], Julian Gilbey [ctb], Yongtao Guan [ctb], Ute Hahn [ctb], Kassel Hingee [ctb], Abdollah Jalilian [ctb], Marie-Colette van Lieshout [ctb], Greg McSwiggan [ctb], Tuomas Rajala [ctb], Suman Rakshit [ctb], Dominic Schuhmacher [ctb], Rasmus Plenge Waagepetersen [ctb], Hangsheng Wang [ctb]
Initial release
2021-04-17

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