Description Usage Arguments Details Value Author(s) References See Also Examples
Estimate the summary functions E(r) and V(r) for a marked point pattern, proposed by Schlather et al (2004) as diagnostics for dependence between the points and the marks.
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X 
The observed point pattern.
An object of class 
r 
Optional. Numeric vector. The values of the argument r at which the function E(r) or V(r) should be evaluated. There is a sensible default. 
correction 
A character vector containing any selection of the
options 
method 
A character vector indicating the user's choice of
density estimation technique to be used. Options are

... 
Arguments passed to the density estimation routine
( 
normalise 
If 
For a marked point process, Schlather et al (2004) defined the functions E(r) and V(r) to be the conditional mean and conditional variance of the mark attached to a typical random point, given that there exists another random point at a distance r away from it.
More formally,
E(r) = E[0u] M(0)
and
V(r) = E[0u]((M(0)E(u))^2)
where E[0u] denotes the conditional expectation given that there are points of the process at the locations 0 and u separated by a distance r, and where M(0) denotes the mark attached to the point 0.
These functions may serve as diagnostics for dependence between the points and the marks. If the points and marks are independent, then E(r) and V(r) should be constant (not depending on r). See Schlather et al (2004).
The argument X
must be a point pattern (object of class
"ppp"
) or any data that are acceptable to as.ppp
.
It must be a marked point pattern with numeric marks.
The argument r
is the vector of values for the
distance r at which k[f](r) is estimated.
This algorithm assumes that X
can be treated
as a realisation of a stationary (spatially homogeneous)
random spatial point process in the plane, observed through
a bounded window.
The window (which is specified in X
as Window(X)
)
may have arbitrary shape.
Biases due to edge effects are
treated in the same manner as in Kest
.
The edge corrections implemented here are
Ripley's isotropic correction (see Ripley, 1988; Ohser, 1983). This is implemented only for rectangular and polygonal windows (not for binary masks).
Translation correction (Ohser, 1983). Implemented for all window geometries, but slow for complex windows.
Note that the estimator assumes the process is stationary (spatially homogeneous).
The numerator and denominator of the mark correlation function (in the expression above) are estimated using density estimation techniques. The user can choose between
"density"
which uses the standard kernel
density estimation routine density
, and
works only for evenlyspaced r
values;
"loess"
which uses the function loess
in the
package modreg;
"sm"
which uses the function sm.density
in the
package sm and is extremely slow;
"smrep"
which uses the function sm.density
in the
package sm and is relatively fast, but may require manual
control of the smoothing parameter hmult
.
If marks(X)
is a numeric vector, the result is
an object of class "fv"
(see fv.object
).
If marks(X)
is a data frame, the result is
a list of objects of class "fv"
, one for each column of marks.
An object of class "fv"
is essentially
a data frame containing numeric columns
r 
the values of the argument r at which the function E(r) or V(r) has been estimated 
theo 
the theoretical, constant value of E(r) or V(r) when the marks attached to different points are independent 
together with a column or columns named
"iso"
and/or "trans"
,
according to the selected edge corrections. These columns contain
estimates of the function E(r) or V(r)
obtained by the edge corrections named.
and \rolf
Schlather, M. and Ribeiro, P. and Diggle, P. (2004) Detecting dependence between marks and locations of marked point processes. Journal of the Royal Statistical Society, series B 66 (2004) 7983.
Mark correlation markcorr
,
mark variogram markvario
for numeric marks.
Mark connection function markconnect
and
multitype Kfunctions Kcross
, Kdot
for factorvalued marks.
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