PP3: Three-Dimensional Exploratory Projection Pursuit
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>.
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