Gdot.inhom | R Documentation |
For a multitype point pattern,
estimate the inhomogeneous version of the dot G
function,
which is the distribution of the distance
from a point of type i
to the nearest other point of any type,
adjusted for spatially varying intensity.
Gdot.inhom(X, i,
lambdaI = NULL, lambdadot = NULL, lambdamin = NULL,
...,
r = NULL, ReferenceMeasureMarkSetI = NULL, ratio = FALSE)
X |
The observed point pattern,
from which an estimate of the inhomogeneous dot type |
i |
The type (mark value)
of the points in |
lambdaI |
Optional.
Values of the estimated intensity of the sub-process of
points of type |
lambdadot |
Optional.
Values of the estimated intensity of the entire point process,
Either a pixel image (object of class |
lambdamin |
Optional. The minimum possible value of the intensity over the spatial domain. A positive numerical value. |
... |
Ignored. |
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 Gdot
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 lambdadot
should contain
estimated values of the intensity of the entire point process.
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_{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.
An object of class "fv"
(see fv.object
)
containing estimates of the inhomogeneous dot 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
Gdot
,
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
}
lamI <- lam[marks(X) == "on"]
GD <- Gdot.inhom(X, "on", lambdaI=lamI, lambdadot=lam, lambdamin=lmin)
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