linearKdot.inhom {spatstat.linnet} | R Documentation |
Inhomogeneous multitype K Function (Dot-type) for Linear Point Pattern
Description
For a multitype point pattern on a linear network,
estimate the inhomogeneous multitype K
function
which counts the expected number of points (of any type)
within a given distance of a point of type i
.
Usage
linearKdot.inhom(X, i, lambdaI=NULL, lambdadot=NULL, r=NULL, ...,
correction="Ang", normalise=TRUE, sigma=NULL)
Arguments
X |
The observed point pattern,
from which an estimate of the dot type |
i |
Number or character string identifying the type (mark value)
of the points in |
lambdaI |
Intensity values for the points of type |
lambdadot |
Intensity values for all points of |
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 Kdot.inhom
for a point pattern on a linear network (object of class "lpp"
).
The argument i
will be interpreted as
levels of the factor marks(X)
.
If i
is missing, it defaults to the first
level of the marks factor.
The argument r
is the vector of values for the
distance r
at which K_{i\bullet}(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 lambdadot
is missing, it will be estimated
by kernel smoothing using density.lpp
.
If lambdaI
or lambdadot
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 argument i
is interpreted as a
level 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)) }
lam. <- function(x,y,const=sum(lam)){ rep(const, length(x)) }
K <- linearKdot.inhom(chicago, "assault", lamI, lam.)
# using fitted models for the intensity
# fit <- lppm(chicago ~marks + x)
# linearKdot.inhom(chicago, "assault", fit, fit)