Inhomogeneous multitype K Function (Cross-type) for Linear Point Pattern
For a multitype point pattern on a linear network, estimate the inhomogeneous multitype K function which counts the expected number of points of type j within a given distance of a point of type i.
linearKcross.inhom(X, i, j, lambdaI, lambdaJ,
r=NULL, ..., correction="Ang", normalise=TRUE)X |
The observed point pattern,
from which an estimate of the cross type K function
Kij(r) will be computed.
An object of class |
i |
Number or character string identifying the type (mark value)
of the points in |
j |
Number or character string identifying the type (mark value)
of the points in |
lambdaI |
Intensity values for the points of type |
lambdaJ |
Intensity values for the points of type |
r |
numeric vector. The values of the argument r at which the K-function Kij(r) should be evaluated. There is a sensible default. First-time users are strongly advised not to specify this argument. See below for important conditions on r. |
correction |
Geometry correction.
Either |
... |
Arguments passed to |
normalise |
Logical. If |
This is a counterpart of the function Kcross.inhom
for a point pattern on a linear network (object of class "lpp").
The arguments i and j will be interpreted as
levels of the factor marks(X).
If i and j are missing, they default to the first
and second level of the marks factor, respectively.
The argument r is the vector of values for the
distance r at which Kij(r) should be evaluated.
The values of r must be increasing nonnegative numbers
and the maximum r value must not exceed the radius of the
largest disc contained in the window.
If lambdaI or lambdaJ is a fitted point process model,
the default behaviour is to update the model by re-fitting it to
the data, before computing the fitted intensity.
This can be disabled by setting update=FALSE.
An object of class "fv" (see fv.object).
The arguments i and j are interpreted as
levels of the factor marks(X). Beware of the usual
trap with factors: numerical values are not
interpreted in the same way as character values.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
Baddeley, A, Jammalamadaka, A. and Nair, G. (to appear) Multitype point process analysis of spines on the dendrite network of a neuron. Applied Statistics (Journal of the Royal Statistical Society, Series C), 63, 673–694.
lam <- table(marks(chicago))/(summary(chicago)$totlength)
lamI <- function(x,y,const=lam[["assault"]]){ rep(const, length(x)) }
lamJ <- function(x,y,const=lam[["robbery"]]){ rep(const, length(x)) }
K <- linearKcross.inhom(chicago, "assault", "robbery", lamI, lamJ)
# using fitted models for the intensity
# fit <- lppm(chicago ~marks + x)
# K <- linearKcross.inhom(chicago, "assault", "robbery", fit, fit)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.