Kest(X, ..., r=NULL, breaks=NULL,
correction=c("border", "isotropic", "Ripley", "translate"),
nlarge=3000, domain=NULL, var.approx=FALSE)"ppp", or data
in any format acceptable to as.ppp().r.
Not normally invoked by the user. See the Details section."none", "border", "bord.modif",
"isotropic", "Ripley", "translate",
"none" or nlarge, then only the
border correction will be computed, using a fast algorithm.TRUE, the approximate
variance of $\hat K(r)$ under CSR
will also be computed."fv", see fv.object,
which can be plotted directly using plot.fv.Essentially a data frame containing columns
"border", "bord.modif",
"iso" and/or "trans",
according to the selected edge corrections. These columns contain
estimates of the function $K(r)$ obtained by the edge corrections
named. If var.approx=TRUE then the return value
also has columns rip and ls containing approximations
to the variance of $\hat K(r)$ under CSR.
envelope.
To compute a confidence interval for the true $K$-function,
use varblock.Kest estimates the $K$ function
of a stationary point process, given observation of the process
inside a known, bounded window.
The argument X is interpreted as a point pattern object
(of class "ppp", see ppp.object) and can
be supplied in any of the formats recognised by
as.ppp(). The estimation of $K$ is hampered by edge effects arising from
the unobservability of points of the random pattern outside the window.
An edge correction is needed to reduce bias (Baddeley, 1998; Ripley, 1988).
The corrections implemented here are
[object Object],[object Object],[object Object]
For instructional purposes, you can set
correction="none" to compute an estimate of the $K$
function without edge correction. This estimate is biased and should
not be used for data analysis.
You can also set correction="best" to select the best
correction that is available for the geometry of the window. Currently
this is Ripley's isotropic correction for a rectangular
or polygonal windows, and the translation correction for masks.
The estimates of $K(r)$ are of the form
$$\hat K(r) = \frac a {(n-1) \pi} \sum_i \sum_j I(d_{ij}\le r) e_{ij}$$
where $a$ is the area of the window, $n$ is the number of
data points, and the sum is taken over all ordered pairs of points
$i$ and $j$ in X.
Here $d_{ij}$ is the distance between the two points,
and $I(d_{ij} \le r)$ is the indicator
that equals 1 if the distance is less than or equal to $r$.
The term $e_{ij}$ is the edge correction weight (which
depends on the choice of edge correction listed above).
Note that this estimator assumes the process is stationary (spatially
homogeneous). For inhomogeneous point patterns, see
Kinhom.
If the point pattern X contains more than about 3000 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".
Approximations to the variance of $\hat K(r)$
are available, for the case of the isotropic edge correction estimator,
assuming complete spatial randomness
(Ripley, 1988; Lotwick and Silverman, 1982; Diggle, 2003, pp 51-53).
If var.approx=TRUE, then the result of
Kest also has a column named rip
values of Ripley's (1988) approximation to
$\mbox{var}(\hat K(r))$,
and (if the window is a rectangle) a column named ls giving
values of Lotwick and Silverman's (1982) approximation.
If the argument domain is given, the calculations will
be restricted to a subset of the data. In the formula for $K(r)$ above,
the first point $i$ will be restricted to lie inside
domain. The result is an approximately unbiased estimate
of $K(r)$ based on pairs of points in which the first point lies
inside domain and the second point is unrestricted.
This is useful in bootstrap techniques. The argument domain
should be a window (object of class "owin") or something acceptable to
as.owin. It must be a subset of the
window of the point pattern X.
The estimator Kest ignores marks.
Its counterparts for multitype point patterns
are Kcross, Kdot,
and for general marked point patterns
see Kmulti.
Some writers, particularly Stoyan (1994, 1995) advocate the use of
the ``pair correlation function''
$$g(r) = \frac{K'(r)}{2\pi r}$$
where $K'(r)$ is the derivative of $K(r)$.
See pcf on how to estimate this function.
Diggle, P.J. Statistical analysis of spatial point patterns. Academic Press, 1983.
Ohser, J. (1983) On estimators for the reduced second moment measure of point processes. Mathematische Operationsforschung und Statistik, series Statistics, 14, 63 -- 71. Ripley, B.D. (1977) Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society, Series B, 39, 172 -- 212.
Ripley, B.D. Statistical inference for spatial processes. Cambridge University Press, 1988.
Stoyan, D, Kendall, W.S. and Mecke, J. (1995) Stochastic geometry and its applications. 2nd edition. Springer Verlag.
Stoyan, D. and Stoyan, H. (1994) Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.
localK to extract individual summands in the $K$
function. pcf for the pair correlation.
Fest,
Gest,
Jest
for alternative summary functions.
Kcross,
Kdot,
Kinhom,
Kmulti for counterparts of the $K$ function
for multitype point patterns.
reduced.sample for the calculation of reduced sample
estimators.
pp <- runifpoint(50)
K <- Kest(pp)
data(cells)
K <- Kest(cells, correction="isotropic")
plot(K)
plot(K, main="K function for cells")
# plot the L function
plot(K, sqrt(iso/pi) ~ r)
plot(K, sqrt(./pi) ~ r, ylab="L(r)", main="L function for cells")Run the code above in your browser using DataLab