redwood.
Strauss (1975) divided the sampling region into two subregions I and II
demarcated by a diagonal line. The spatial pattern
appears to be slightly regular in region I and strongly clustered in
region II. Strauss (1975) writes:
The dataset redwoodfull contains the full point pattern
of 195 trees.
The window has been rescaled to the unit square.
Its physical size is approximately 130 feet across.
The auxiliary information about the subregions is contained in
redwoodfull.extra, which is a list with entries
rdiag The coordinates of the diagonal boundary
between regions I and II
regionI Region I as a window object
regionII Region II as a window object
regionR Ripley's subrectangle (approximate)
plotit Function to plot the full data and auxiliary markings
}
Ripley (1977) extracted a subset of these data, containing 62 points,
lying within a square subregion which overlaps regions I and II.
He rescaled that subset to the unit square.
This subset has been re-analysed many times,
and is the dataset usually known as
``the redwood data'' in the spatial statistics literature.
The exact dataset used by Ripley is supplied in the redwood.
The approximate position of the square chosen by Ripley
within the redwoodfull pattern
is indicated by the window redwoodfull.extra$regionR.
There are some minor inconsistencies with
redwood since it originates from a different digitisation.
data(redwoodfull)redwoodfull is an object of class "ppp"
representing the point pattern of tree locations.
See ppp.object for details of the format of a
point pattern object.
The window has been rescaled to the unit square.
Its physical size is approximately 128 feet across. The dataset redwoodfull.extra is a list with entries
rdiag coordinates of endpoints of a line,
in format list(x=numeric(2),y=numeric(2))
regionI a window object
regionII a window object
regionR a window object
plotit Function with no arguments
}
Ripley, B.D. (1977) Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society, Series B 39, 172--212.
Strauss, D.J. (1975) A model for clustering. Biometrika 63, 467--475.
redwooddata(redwoodfull)
plot(redwoodfull)
redwoodfull.extra$plotit()
# extract the pattern in region II
redwoodII <- redwoodfull[, redwoodfull.extra$regionII]Run the code above in your browser using DataLab