For a multitype point pattern on a linear network,
estimate the inhomogeneous multitype pair correlation function
from points of type
linearpcfcross.inhom(X, i, j, lambdaI, lambdaJ, r=NULL, …,
correction="Ang", normalise=TRUE)
The observed point pattern,
from which an estimate of the "lpp"
which
must be a multitype point pattern (a marked point pattern
whose marks are a factor).
Number or character string identifying the type (mark value)
of the points in X
from which distances are measured.
Defaults to the first level of marks(X)
.
Number or character string identifying the type (mark value)
of the points in X
to which distances are measured.
Defaults to the second level of marks(X)
.
Intensity values for the points of type i
. Either a numeric vector,
a function
, a pixel image
(object of class "im"
or "linim"
) or
a fitted point process model (object of class "ppm"
or "lppm"
).
Intensity values for the points of type j
. Either a numeric vector,
a function
, a pixel image
(object of class "im"
or "linim"
) or
a fitted point process model (object of class "ppm"
or "lppm"
).
numeric vector. The values of the argument
Geometry correction.
Either "none"
or "Ang"
. See Details.
Arguments passed to density.default
to control the kernel smoothing.
Logical. If TRUE
(the default), the denominator of the estimator is
data-dependent (equal to the sum of the reciprocal intensities at
the points of type i
), which reduces the sampling variability.
If FALSE
, the denominator is the length of the network.
An object of class "fv"
(see fv.object
).
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.
This is a counterpart of the function pcfcross.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
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
.
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.
# NOT RUN {
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)) }
g <- linearpcfcross.inhom(chicago, "assault", "robbery", lamI, lamJ)
# }
# NOT RUN {
fit <- lppm(chicago, ~marks + x)
linearpcfcross.inhom(chicago, "assault", "robbery", fit, fit)
# }
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