There were four samples of bone, and ten regions were mapped in each bone, yielding 40 spatial point patterns. The data can be regarded as replicated observations of a three-dimensional point process, nested within bone samples.
data(osteo)
hyperframe
with the following columns:
id |
character string identifier of bone sample |
shortid |
last numeral in id |
brick |
serial number (1 to 10) of sampling volume within this bone sample |
pts |
three dimensional point pattern (class pp3 ) |
Each point pattern dataset gives the $(x,y,z)$ coordinates (in microns) of all points visible in a three-dimensional rectangular box (``brick'') of dimensions $81 * 100 * d$ microns, where $d$ varies. The $z$ coordinate is depth into the bone (depth of the focal plane of the confocal microscope); the $(x,y)$ plane is parallel to the exterior surface of the bone; the relative orientation of the $x$ and $y$ axes is not important. The bone samples were three intact skulls and one skull cap, all originally identified as belonging to the macaque monkey Macaca fascicularis, from the collection of the Department of Anatomy, University of London. Later analysis (Baddeley et al, 1993) suggested that the skull cap, given here as the first animal, was a different subspecies, and this was confirmed by anatomical inspection.
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 (1993) 641--668. Howard, C.V. and Reid, S. and Baddeley, A.J. and Boyde, A. (1985) Unbiased estimation of particle density in the tandem-scanning reflected light microscope. Journal of Microscopy 138 203--212.
data(osteo)
osteo
if(interactive()) {
plot(osteo$pts[[1]], main="animal 1, brick 1")
ape1 <- osteo[osteo$shortid==4, ]
plot(ape1, tick.marks=FALSE)
with(osteo, intensity(pts))
plot(with(ape1, K3est(pts)))
}
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