F3est(X, ..., rmax = NULL, nrval = 128, vside = NULL, correction = c("rs", "km", "cs"))
"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 empty space function of $\Phi$ can then be estimated using
techniques described in the References. The box containing the point
pattern is discretised into cubic voxels of side length vside
.
The distance function $d(u,\Phi)$ is computed for
every voxel centre point
$u$ using a three-dimensional version of the distance transform
algorithm (Borgefors, 1986). The empirical cumulative distribution
function of these values, with appropriate edge corrections, is the
estimate of $F_3(r)$.
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.
Borgefors, G. (1986) Distance transformations in digital images. Computer Vision, Graphics and Image Processing 34, 344--371.
Chiu, S.N. and Stoyan, D. (1998) Estimators of distance distributions for spatial patterns. Statistica Neerlandica 52, 239--246.
G3est
,
K3est
.X <- rpoispp3(42)
Z <- F3est(X)
if(interactive()) plot(Z)
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