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 function
which counts the expected number of points (of any type)
within a given distance of a point of type
.
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 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 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)