PP3-package: Three-Dimensional Exploratory Projection Pursuit

In PP3: Three-Dimensional Exploratory Projection Pursuit

Description

Exploratory projection pursuit is a method to discovers structure in multivariate data. At heart this package uses a projection index to evaluate how interesting a specific three-dimensional projection of multivariate data (with more than three dimensions) is. Typically, the main structure finding algorithm starts at a random projection and then iteratively changes the projection direction to move to a more interesting one. In other words, the projection index is maximised over the projection direction to find the most interesting projection. This maximum is, though, a local maximum. So, this code has the ability to restart the algorithm from many different starting positions automatically. Routines exist to plot a density estimate of projection indices over the runs, this enables the user to obtain an idea of the distribution of the projection indices, and, hence, which ones might be interesting. Individual projection solutions, including those identified as interesting, can be extracted and plotted individually. The package can make use of the mclapply() function to execute multiple runs in parallel to speed up index discovery. Projection pursuit is similar to independent component analysis. This package uses a projection index that maximises an entropy measure to look for projections that exhibit non-normality, and operates on sphered data. Hence, information from this package is different from that obtained from principal components analysis, but the rationale behind both methods is similar. Nason, G. P. (1995) <doi:10.2307/2986135>.

Details

The DESCRIPTION file: This package was not yet installed at build time.

Index: This package was not yet installed at build time.
The main routine is PP3many. This package carries out three-dimensional projection pursuit on multivariate data set. It can be thought of as an alternative to principal components analysis, where interesting views of the data are presented in a three-dimensional projected space. This package can directly produce a true three-dimensional solution and, not, a combination of, e.g. three one-dimensional views. This permits the elucidation of more complex structures. The three-dimensional solution can be used to produce colour pixel values enabling interesting contrast display of colour images from multispectal ones.

Author(s)

Guy Nason [aut, cre], Robin Sibson [ctb, ths]

Maintainer: Guy Nason <[e-mail]>

References

Friedman, J.H. and Tukey, J.W. (1974) A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput., 23, 881-890.

Jones, M.C. and Sibson, R. (1987) What is projection pursuit? (with discussion) J. R. Statist. Soc. A, 150, 1-36.

Nason, G. P. (1995) Three-dimensional projection pursuit. J. R. Statist. Soc. C, 44, 411-430.

Nason, G. P. (2001) Robust projection indices. J. R. Statist. Soc. B, 63, 551-567.

See Also

PP3many

Examples

#
# See extended example in PP3many
#

PP3 documentation built on May 2, 2019, 8:57 a.m.