sharp3dB: Identify Cluster Centres for 3-dimensional Data via Data Sharpening
Description
Identifies the centres of clusters for 3-dimensional data using a
convergent form of Choi and Hall's (1999) data sharpening method. For
use when the data is such that the z coordinates are in increasing
order.Usage
sharp3dB(x, y, z, hspace = 1, htime = 1, v = 1)
Arguments
x
the x coordinates of the data
y
the y coordinates of the data
z
the z coordinates of the data, in increasing order
hspace
the bandwidth for sharpening in the direction of the x-y
plane
htime
the bandwidth for sharpening in the z direction
v
a positive integer representing the number of iterations to
perform
Value
Returns a (number of data points x 3) data frame containing the sharpened
points x.sharp, y.sharp and z.sharp, respectively.
Details
Identifies the centres of clusters based on a convergent form of Choi
and Hall's data sharpening method. This function was originally built for
identifying clusters in space-time where space is the x-y plane and time
is the z-axis. Provided the z-data is in increasing order, this function
is significantly faster than sharp3d().References
Woolford, D. G. and Braun, W. J. (2004) Exploring lightning
and fire ignition data as point processes. 2004 Proceeding of the
American Statistical Association, Statistics and the Environment Section
[CD-ROM], Alexandria, VA: American Statistical Association.Choi, E. and Hall, P. (1999) Data sharpening as a prelude to density
estimation. Biometrika 86, 941-947.