spatstat (version 1.64-1)

G3est: Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern

Description

Estimates the nearest-neighbour distance distribution function \(G_3(r)\) from a three-dimensional point pattern.

Usage

G3est(X, ..., rmax = NULL, nrval = 128, correction = c("rs", "km", "Hanisch"))

Arguments

X

Three-dimensional point pattern (object of class "pp3").

…

Ignored.

rmax

Optional. Maximum value of argument \(r\) for which \(G_3(r)\) will be estimated.

nrval

Optional. Number of values of \(r\) for which \(G_3(r)\) will be estimated. A large value of nrval is required to avoid discretisation effects.

correction

Optional. Character vector specifying the edge correction(s) to be applied. See Details.

Value

A function value table (object of class "fv") that can be plotted, printed or coerced to a data frame containing the function values.

Warnings

A large value of nrval is required in order to avoid discretisation effects (due to the use of histograms in the calculation).

Details

For a stationary point process \(\Phi\) in three-dimensional space, the nearest-neighbour function is $$ G_3(r) = P(d^\ast(x,\Phi) \le r \mid x \in \Phi) $$ the cumulative distribution function of the distance \(d^\ast(x,\Phi)\) from a typical point \(x\) in \(\Phi\) to its nearest neighbour, i.e. to the nearest other point of \(\Phi\).

The three-dimensional point pattern X is assumed to be a partial realisation of a stationary point process \(\Phi\). The nearest neighbour function of \(\Phi\) can then be estimated using techniques described in the References. For each data point, the distance to the nearest neighbour is computed. The empirical cumulative distribution function of these values, with appropriate edge corrections, is the estimate of \(G_3(r)\).

The available edge corrections are:

"rs":

the reduced sample (aka minus sampling, border correction) estimator (Baddeley et al, 1993)

"km":

the three-dimensional version of the Kaplan-Meier estimator (Baddeley and Gill, 1997)

"Hanisch":

the three-dimensional generalisation of the Hanisch estimator (Hanisch, 1984).

Alternatively correction="all" selects all options.

References

Baddeley, A.J, Moyeed, R.A., Howard, C.V. and Boyde, A. (1993) Analysis of a three-dimensional point pattern with replication. Applied Statistics 42, 641--668.

Baddeley, A.J. and Gill, R.D. (1997) Kaplan-Meier estimators of interpoint distance distributions for spatial point processes. Annals of Statistics 25, 263--292.

Hanisch, K.-H. (1984) Some remarks on estimators of the distribution function of nearest neighbour distance in stationary spatial point patterns. Mathematische Operationsforschung und Statistik, series Statistics 15, 409--412.

See Also

pp3 to create a three-dimensional point pattern (object of class "pp3").

F3est, K3est, pcf3est for other summary functions of a three-dimensional point pattern.

Gest to estimate the empty space function of point patterns in two dimensions.

Examples

# NOT RUN {
  X <- rpoispp3(42)
  Z <- G3est(X)
  if(interactive()) plot(Z)
# }