CHsharp (version 0.4)

sharp3d: 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.

Usage

sharp3d(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

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.

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.

See Also

sharp3dB

Examples

x <- 1:200
y <- c(rnorm(50,-1,1),rnorm(50,2,2), rnorm(100,0,.5))
z <- c(sample(1:50,50), sample(26:75,50), sample(51:150,100))
data.sharp5 <- sharp3d(x,y,z,5,10,5)
data.sharp10 <- sharp3d(x,y,z,5,10,10)
# original data:
dataPlot <- scatterplot3d(x,y,z)
# sharpened data after 5 iterations:
dataPlot$points3d(data.sharp5$x.sharp, data.sharp5$y.sharp, 
data.sharp5$z.sharp, col=2,pch=19)
# sharpened data after 10 iterations:
dataPlot$points3d(data.sharp10$x.sharp, data.sharp10$y.sharp, 
data.sharp10$z.sharp, col=4, pch=19)