G3est(X, ..., rmax = NULL, nrval = 128, correction = c("rs", "km", "Hanisch"))
"pp3"
).nrval
is required to avoid discretisation effects."fv"
) that can be
plotted, printed or coerced to a data frame containing the function values.nrval
is required in order to avoid
discretisation effects (due to the use of histograms in the
calculation).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: [object Object],[object Object],[object Object]
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.
F3est
,
K3est
X <- rpoispp3(42)
Z <- G3est(X)
if(interactive()) plot(Z)
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