Kinhomhat {dbmss} | R Documentation |
Estimates the Kinhom function
Kinhomhat(X, r = NULL, ReferenceType = "", lambda = NULL, CheckArguments = TRUE)
X |
A weighted, marked, planar point pattern ( |
r |
A vector of distances. If |
ReferenceType |
One of the point types. Default is all point types. |
lambda |
An estimation of the point pattern density, obtained by the |
CheckArguments |
Logical; if |
Kinhom is a cumulative, topographic measure of an inhomogenous point pattern structure.
By default, density estimation is performed at points by density.ppp
using the optimal bandwith (bw.diggle
). It can be calculated separately (see example), including at pixels if the point pattern is too large for the default estimation to succeed, and provided as the argument lambda
:
Arbia et al. (2012) for example use another point pattern as a reference to estimate density.
Bivariate Kinhom is not currently supported.
An object of class fv
, see fv.object
, which can be plotted directly using plot.fv
.
The computation of Kinhomhat
relies on spatstat functions Kinhom
, density.ppp
and bw.diggle
.
Baddeley, A. J., J. Moller, et al. (2000). Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica 54(3): 329-350.
Arbia, G., G. Espa, et al. (2012). Clusters of firms in an inhomogeneous space: The high-tech industries in Milan. Economic Modelling 29(1): 3-11.
data(paracou16)
# Density of all trees
lambda <- density.ppp(paracou16, bw.diggle(paracou16))
plot(lambda)
# Reduce the point pattern to one type of trees
V.americana <- paracou16[paracou16$marks$PointType=="V. Americana"]
plot(V.americana, add=TRUE)
# Calculate Kinhom according to the density of all trees
r <- 0:30
autoplot(Kinhomhat(paracou16, r, "V. Americana", lambda), ./(pi*r^2) ~ r)