Gcross.inhom | R Documentation |
For a multitype point pattern,
estimate the inhomogeneous version of the cross G
function,
which is the distribution of the distance
from a point of type i
to the nearest point of type j
,
adjusted for spatially varying intensity.
Gcross.inhom(X, i, j,
lambda = NULL, lambdaI = NULL, lambdaJ = NULL,
lambdamin = NULL,
...,
r = NULL,
ReferenceMeasureMarkSetI = NULL,
ratio = FALSE)
X |
The observed point pattern,
from which an estimate of the inhomogeneous cross type |
i |
The type (mark value)
of the points in |
j |
The type (mark value)
of the points in |
lambda |
Optional.
Values of the estimated intensity of the point process.
Either a pixel image (object of class |
lambdaI |
Optional.
Values of the estimated intensity of the sub-process of
points of type |
lambdaJ |
Optional.
Values of the the estimated intensity of the sub-process of
points of type |
lambdamin |
Optional. The minimum possible value of the intensity over the spatial domain. A positive numerical value. |
... |
Extra arguments passed to |
r |
vector of values for the argument |
ReferenceMeasureMarkSetI |
Optional. The total measure of the mark set. A positive number. |
ratio |
Logical value indicating whether to save ratio information. |
This is a generalisation of the function Gcross
to include an adjustment for spatially inhomogeneous intensity,
in a manner similar to the function Ginhom
.
The argument lambdaI
supplies the values
of the intensity of the sub-process of points of type i
.
It may be either
(object of class "im"
) which
gives the values of the type i
intensity
at all locations in the window containing X
;
containing the values of the
type i
intensity evaluated only
at the data points of type i
. The length of this vector
must equal the number of type i
points in X
.
of the form function(x,y)
which can be evaluated to give values of the intensity at
any locations.
(object of class "ppm"
, "kppm"
or "dppm"
)
whose fitted trend can be used as the fitted intensity.
(If update=TRUE
the model will first be refitted to the
data X
before the trend is computed.)
if lambdaI
is omitted then it will be estimated
using a leave-one-out kernel smoother.
If lambdaI
is omitted, then it will be estimated using
a ‘leave-one-out’ kernel smoother.
Similarly the argument lambdaJ
should contain
estimated values of the intensity of the points of type j
.
It may be either a pixel image, a numeric vector of length equal
to the number of points in X
, a function, or omitted.
The argument r
is the vector of values for the
distance r
at which G_{ij}(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.
An object of class "fv"
(see fv.object
)
containing estimates of the inhomogeneous cross type G
function.
The argument i
is interpreted as
a level of the factor X$marks
. It is converted to a character
string if it is not already a character string.
The value i=1
does not
refer to the first level of the factor.
.
Cronie, O. and Van Lieshout, M.N.M. (2015) Summary statistics for inhomogeneous marked point processes. Annals of the Institute of Statistical Mathematics DOI: 10.1007/s10463-015-0515-z
Gcross
,
Ginhom
,
Gcross.inhom
,
Gmulti.inhom
.
X <- rescale(amacrine)
if(interactive() && require(spatstat.model)) {
## how to do it normally
mod <- ppm(X ~ marks * x)
lam <- fitted(mod, dataonly=TRUE)
lmin <- min(predict(mod)[["off"]]) * 0.9
} else {
## for package testing
lam <- intensity(X)[as.integer(marks(X))]
lmin <- intensity(X)[2] * 0.9
}
GC <- Gcross.inhom(X, "on", "off", lambda=lam, lambdamin=lmin)
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