sharp3dB: Identify Cluster Centres for 3-dimensional Data via Data...
In CHsharp: Choi and Hall Style Data Sharpening

Description Usage Arguments Details Value Author(s) References See Also

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.

1	sharp3dB(x, y, z, hspace = 1, htime = 1, v = 1)

`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

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().

Returns a (number of data points x 3) data frame containing the sharpened points x.sharp, y.sharp and z.sharp, respectively.

Douglas G. Woolford, W. John Braun

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.

sharp3d

CHsharp documentation built on May 1, 2019, 6:48 p.m.