Gfox | R Documentation |
Given a point pattern X
and a spatial object Y
,
compute estimates of Foxall's G
and J
functions.
Gfox(X, Y, r=NULL, breaks=NULL, correction=c("km", "rs", "han"), W, ...)
Jfox(X, Y, r=NULL, breaks=NULL, correction=c("km", "rs", "han"), W, ...,
warn.trim=TRUE)
X |
A point pattern (object of class |
Y |
An object of class |
r |
Optional. Numeric vector. The values of the argument |
breaks |
This argument is for internal use only. |
correction |
Optional.
The edge correction(s) to be used to estimate
|
W |
Optional. A window (object of class |
... |
Extra arguments affecting the discretisation of distances.
These arguments are ignored by |
warn.trim |
Logical value indicating whether a warning should be issued
by |
Given a point pattern X
and another spatial object Y
,
these functions compute two nonparametric measures of association
between X
and Y
, introduced by Foxall
(Foxall and Baddeley, 2002).
Let the random variable R
be the distance from a typical point
of X
to the object Y
.
Foxall's G
-function is the cumulative distribution function
of R
:
G(r) = P(R \le r)
Let the random variable S
be the distance from a fixed point
in space to the object Y
. The cumulative distribution function
of S
is the (unconditional) spherical contact distribution
function
H(r) = P(S \le r)
which is computed by Hest
.
Foxall's J
-function is the ratio
J(r) = \frac{1-G(r)}{1-H(r)}
For further interpretation, see Foxall and Baddeley (2002).
Accuracy of Jfox
depends on the pixel resolution,
which is controlled by the
arguments eps
, dimyx
and xy
passed to
as.mask
. For example, use eps=0.1
to specify
square pixels of side 0.1 units, and dimyx=256
to specify a
256 by 256 grid of pixels.
A function value table (object of class "fv"
)
which can be printed, plotted, or converted to a data frame of values.
Rob Foxall and \adrian
Foxall, R. and Baddeley, A. (2002) Nonparametric measures of association between a spatial point process and a random set, with geological applications. Applied Statistics 51, 165–182.
Gest
,
Hest
,
Jest
,
Fest
X <- copper$SouthPoints
Y <- copper$SouthLines
G <- Gfox(X,Y)
J <- Jfox(X,Y, correction="km")
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