# Kest

0th

Percentile

##### K-function

Estimates the reduced second moment function $K(r)$ from a point pattern in a window of arbitrary shape.

Keywords
spatial
##### Usage
Kest(X)
Kest(X, r)
Kest(X, r, correction=c("border", "isotropic", "Ripley", "translate"))
Kest(X, breaks=breaks)
##### Arguments
X
The observed point pattern, from which an estimate of $K(r)$ will be computed. An object of class "ppp", or data in any format acceptable to as.ppp().
r
vector of values for the argument $r$ at which $K(r)$ should be evaluated. There is a sensible default.
breaks
An alternative to the argument r. Not normally invoked by the user. See Details.
correction
A character vector containing any selection of the options "border", "bord.modif", "isotropic", "Ripley" or "translate". It specifies the edge correction(s) to be applied.
##### Details

The $K$ function (variously called Ripley's K-function'' and the reduced second moment function'') of a stationary point process $X$ is defined so that $\lambda K(r)$ equals the expected number of additional random points within a distance $r$ of a typical random point of $X$. Here $\lambda$ is the intensity of the process, i.e. the expected number of points of $X$ per unit area. The $K$ function is determined by the second order moment properties of $X$. An estimate of $K$ derived from a spatial point pattern dataset can be used in exploratory data analysis and formal inference about the pattern (Cressie, 1991; Diggle, 1983; Ripley, 1988). In exploratory analyses, the estimate of $K$ is a useful statistic summarising aspects of inter-point dependence'' and clustering''. For inferential purposes, the estimate of $K$ is usually compared to the true value of $K$ for a completely random (Poisson) point process, which is $K(r) = \pi r^2$. Deviations between the empirical and theoretical $K$ curves may suggest spatial clustering or spatial regularity. This routine 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] Note that the estimator assumes the process is stationary (spatially homogeneous). For inhomogeneous point patterns, see Kinhom.

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.

##### Value

• An object of class "fv", see fv.object, which can be plotted directly using plot.fv.

Essentially a data frame containing columns

• rthe vector of values of the argument $r$ at which the function $K$ has been estimated
• theothe theoretical value $K(r) = \pi r^2$ for a stationary Poisson process
• together with columns named "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.

##### synopsis

Kest(X, r=NULL, breaks=NULL, slow, correction=c("border", "isotropic", "Ripley", "translate"), ...)

##### Warnings

The estimator of $K(r)$ is approximately unbiased for each fixed $r$. Bias increases with $r$ and depends on the window geometry. For a rectangular window it is prudent to restrict the $r$ values to a maximum of $1/4$ of the smaller side length of the rectangle. Bias may become appreciable for point patterns consisting of fewer than 15 points. While $K(r)$ is always a non-decreasing function, the estimator of $K$ is not guaranteed to be non-decreasing. This is rarely a problem in practice.

##### References

Baddeley, A.J. Spatial sampling and censoring. In O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. van Lieshout (eds) Stochastic Geometry: Likelihood and Computation. Chapman and Hall, 1998. Chapter 2, pages 37--78. Cressie, N.A.C. Statistics for spatial data. John Wiley and Sons, 1991.

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. 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.

Fest, Gest, Jest, pcf, reduced.sample, Kcross, Kdot, Kinhom, Kmulti

• Kest
##### Examples
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")
Documentation reproduced from package spatstat, version 1.9-0, License: GPL version 2 or newer

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