A quantile tessellation is a division of space into
pieces which contain equal amounts of stuff. The function quantess
computes a quantile tessellation and
returns the tessellation itself.
The function quantess is generic, with methods for
windows (class "owin"), point patterns ("ppp")
and pixel images ("im").
The first argument M (for mass) specifies the spatial
distribution of stuff that is to be divided. If M is a window,
the area of the window is to be divided into n equal pieces.
If M is a point pattern, the number of points in the
pattern is to be divided into n equal parts, as far as
possible. If M is a pixel image, the pixel values are
interpreted as weights, and the total weight is to be divided
into n equal parts.
The second argument
Z is a spatial covariate. The range of values of Z
will be divided into n bands, each containing
the same total weight. That is, we determine the quantiles of Z
with weights given by M.
For convenience, additional arguments ... can be given,
to further subdivide the tiles of the tessellation.
The result of quantess is a tessellation of as.owin(M)
determined by the quantiles of Z.