Kinhom
Inhomogeneous K-function
Estimates the inhomogeneous $K$ function of a non-stationary point pattern.
Usage
Kinhom(X, lambda=NULL, ..., r = NULL, breaks = NULL,
correction=c("border", "bord.modif", "isotropic", "translate"),
renormalise=TRUE,
normpower=1,
update=TRUE,
leaveoneout=TRUE,
nlarge = 1000,
lambda2=NULL, reciplambda=NULL, reciplambda2=NULL,
sigma=NULL, varcov=NULL)
Arguments
- X
- The observed data point pattern,
from which an estimate of the inhomogeneous $K$ function
will be computed.
An object of class
"ppp"
or in a format recognised byas.ppp()
- lambda
- Optional.
Values of the estimated intensity function.
Either a vector giving the intensity values
at the points of the pattern
X
, a pixel image (object of class"im"
) giving the intensity values at all locatio - ...
- Extra arguments. Ignored if
lambda
is present. Passed todensity.ppp
iflambda
is omitted. - r
- vector of values for the argument $r$ at which the inhomogeneous $K$ function should be evaluated. Not normally given by the user; there is a sensible default.
- breaks
- This argument is for internal use only.
- correction
- A character vector containing any selection of the
options
"border"
,"bord.modif"
,"isotropic"
,"Ripley"
,"translate"
,"translation"
,"none"
or - renormalise
- Logical. Whether to renormalise the estimate. See Details.
- normpower
- Integer (usually either 1 or 2). Normalisation power. See Details.
- update
- Logical. If
lambda
is a fitted model (class"ppm"
or"kppm"
) andupdate=TRUE
(the default), the model will first be refitted to the dataX
(using - leaveoneout
- Logical value (passed to
density.ppp
orfitted.ppm
) specifying whether to use a leave-one-out rule when calculating the intensity. - nlarge
- Optional. Efficiency threshold.
If the number of points exceeds
nlarge
, then only the border correction will be computed, using a fast algorithm. - lambda2
- Advanced use only. Matrix containing estimates of the products $\lambda(x_i)\lambda(x_j)$ of the intensities at each pair of data points $x_i$ and $x_j$.
- reciplambda
- Alternative to
lambda
. Values of the estimated reciprocal $1/\lambda$ of the intensity function. Either a vector giving the reciprocal intensity values at the points of the patternX
, a pixel image (o - reciplambda2
- Advanced use only. Alternative to
lambda2
. A matrix giving values of the estimated reciprocal products $1/\lambda(x_i)\lambda(x_j)$ of the intensities at each pair of data points $x_i$ and $x_j$. - sigma,varcov
- Optional arguments passed to
density.ppp
to control the smoothing bandwidth, whenlambda
is estimated by kernel smoothing.
Details
This computes a generalisation of the $K$ function
for inhomogeneous point patterns, proposed by
Baddeley, Kest
. It is defined only
for stationary point processes.
The inhomogeneous $K$ function
$K_{\rm inhom}(r)$
is a direct generalisation to nonstationary point processes.
Suppose $x$ is a point process with non-constant intensity
$\lambda(u)$ at each location $u$.
Define $K_{\rm inhom}(r)$ to be the expected
value, given that $u$ is a point of $x$,
of the sum of all terms
$1/\lambda(x_j)$
over all points $x_j$
in the process separated from $u$ by a distance less than $r$.
This reduces to the ordinary $K$ function if
$\lambda()$ is constant.
If $x$ is an inhomogeneous Poisson process with intensity
function $\lambda(u)$, then
$K_{\rm inhom}(r) = \pi r^2$.
Given a point pattern dataset, the
inhomogeneous $K$ function can be estimated
essentially by summing the values
$1/(\lambda(x_i)\lambda(x_j))$
for all pairs of points $x_i, x_j$
separated by a distance less than $r$.
This allows us to inspect a point pattern for evidence of interpoint interactions after allowing for spatial inhomogeneity of the pattern. Values $K_{\rm inhom}(r) > \pi r^2$ are suggestive of clustering.
The argument lambda
should supply the
(estimated) values of the intensity function $\lambda$.
It may be either
[object Object],[object Object],[object Object],[object Object],[object Object]
If lambda
is a numeric vector, then its length should
be equal to the number of points in the pattern X
.
The value lambda[i]
is assumed to be the
the (estimated) value of the intensity
$\lambda(x_i)$ for
the point $x_i$ of the pattern $X$.
Each value must be a positive number; NA
's are not allowed.
If lambda
is a pixel image, the domain of the image should
cover the entire window of the point pattern. If it does not (which
may occur near the boundary because of discretisation error),
then the missing pixel values
will be obtained by applying a Gaussian blur to lambda
using
blur
, then looking up the values of this blurred image
for the missing locations.
(A warning will be issued in this case.)
If lambda
is a function, then it will be evaluated in the
form lambda(x,y)
where x
and y
are vectors
of coordinates of the points of X
. It should return a numeric
vector with length equal to the number of points in X
.
If lambda
is omitted, then it will be estimated using
a `leave-one-out' kernel smoother,
as described in Baddeley, lambda[i]
for the
point X[i]
is computed by removing X[i]
from the
point pattern, applying kernel smoothing to the remaining points using
density.ppp
, and evaluating the smoothed intensity
at the point X[i]
. The smoothing kernel bandwidth is controlled
by the arguments sigma
and varcov
, which are passed to
density.ppp
along with any extra arguments.
Edge corrections are used to correct bias in the estimation
of $K_{\rm inhom}$.
Each edge-corrected estimate of $K_{\rm inhom}(r)$ is
of the form
$$\widehat K_{\rm inhom}(r) = \sum_i \sum_j \frac{1{d_{ij} \le
r} e(x_i,x_j,r)}{\lambda(x_i)\lambda(x_j)}$$
where $d_{ij}$ is the distance between points
$x_i$ and $x_j$, and
$e(x_i,x_j,r)$ is
an edge correction factor. For the `border' correction,
$$e(x_i,x_j,r) =
\frac{1(b_i > r)}{\sum_j 1(b_j > r)/\lambda(x_j)}$$
where $b_i$ is the distance from $x_i$
to the boundary of the window. For the `modified border'
correction,
$$e(x_i,x_j,r) =
\frac{1(b_i > r)}{\mbox{area}(W \ominus r)}$$
where $W \ominus r$ is the eroded window obtained
by trimming a margin of width $r$ from the border of the original
window.
For the `translation' correction,
$$e(x_i,x_j,r) =
\frac 1 {\mbox{area}(W \cap (W + (x_j - x_i)))}$$
and for the `isotropic' correction,
$$e(x_i,x_j,r) =
\frac 1 {\mbox{area}(W) g(x_i,x_j)}$$
where $g(x_i,x_j)$ is the fraction of the
circumference of the circle with centre $x_i$ and radius
$||x_i - x_j||$ which lies inside the window.
If renormalise=TRUE
(the default), then the estimates
are multiplied by $c^{\mbox{normpower}}$ where
$c = \mbox{area}(W)/\sum (1/\lambda(x_i)).$
This rescaling reduces the variability and bias of the estimate
in small samples and in cases of very strong inhomogeneity.
The default value of normpower
is 1 (for consistency with
previous versions of lambda
values so that
$\sum (1/\lambda(x_i)) = \mbox{area}(W).$
If the point pattern X
contains more than about 1000 points,
the isotropic and translation edge corrections can be computationally
prohibitive. The computations for the border method are much faster,
and are statistically efficient when there are large numbers of
points. Accordingly, if the number of points in X
exceeds
the threshold nlarge
, then only the border correction will be
computed. Setting nlarge=Inf
or correction="best"
will prevent this from happening.
Setting nlarge=0
is equivalent to selecting only the border
correction with correction="border"
.
The pair correlation function can also be applied to the
result of Kinhom
; see pcf
.
}
Kest
,
pcf
# (1) intensity function estimated by model-fitting # Fit spatial trend: polynomial in x and y coordinates fit <- ppm(X, ~ polynom(x,y,2), Poisson()) # (a) predict intensity values at points themselves, # obtaining a vector of lambda values lambda <- predict(fit, locations=X, type="trend") # inhomogeneous K function Ki <- Kinhom(X, lambda) plot(Ki) # (b) predict intensity at all locations, # obtaining a pixel image lambda <- predict(fit, type="trend") Ki <- Kinhom(X, lambda) plot(Ki)
# (2) intensity function estimated by heavy smoothing Ki <- Kinhom(X, sigma=0.1) plot(Ki)
# (3) simulated data: known intensity function lamfun <- function(x,y) { 50 + 100 * x } # inhomogeneous Poisson process Y <- rpoispp(lamfun, 150, owin()) # inhomogeneous K function Ki <- Kinhom(Y, lamfun) plot(Ki)
# How to make simulation envelopes:
# Example shows method (2)
smo <- density.ppp(X, sigma=0.1)
Ken <- envelope(X, Kinhom, nsim=99,
simulate=expression(rpoispp(smo)),
sigma=0.1, correction="trans")
plot(Ken)
[object Object],[object Object],[object Object]
Value
- An object of class
"fv"
(seefv.object
). Essentially a data frame containing at least the following columns, r the vector of values of the argument $r$ at which $K_{\rm inhom}(r)$ has been estimated theo vector of values of $\pi r^2$, the theoretical value of $K_{\rm inhom}(r)$ for an inhomogeneous Poisson process - and containing additional columns
according to the choice specified in the
correction
argument. The additional columns are namedborder
,trans
andiso
and give the estimated values of $K_{\rm inhom}(r)$ using the border correction, translation correction, and Ripley isotropic correction, respectively.