linearKcross.inhom {spatstat.linnet} | R Documentation |
Inhomogeneous multitype K Function (Cross-type) for Linear Point Pattern
Description
For a multitype point pattern on a linear network,
estimate the inhomogeneous multitype function
which counts the expected number of points of type
within a given distance of a point of type
.
Usage
linearKcross.inhom(X, i, j, lambdaI=NULL, lambdaJ=NULL,
r=NULL, ..., correction="Ang", normalise=TRUE,
sigma=NULL)
Arguments
X |
The observed point pattern,
from which an estimate of the cross type |
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 |
correction |
Geometry correction.
Either |
... |
Arguments passed to |
normalise |
Logical. If |
sigma |
Smoothing bandwidth passed to |
Details
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 at which
should be evaluated.
The values of
must be increasing nonnegative numbers
and the maximum
value must not exceed the radius of the
largest disc contained in the window.
If lambdaI
or lambdaJ
is missing or NULL
, it will
be estimated by kernel smoothing using density.lpp
.
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
.
Value
An object of class "fv"
(see fv.object
).
Warnings
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.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
References
Baddeley, A, Jammalamadaka, A. and Nair, G. (2014) 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.
See Also
Examples
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)