summary.PP3: Print summary information about a PP3 object.

Description

Print summary statistics about a PP3 object.

Usage

# S3 method for PP3
summary(object, …)

Arguments

object

PP3 object

…

Other arguments (which aren't used)

Value

Nothing explicit is returned

Details

This applies the usual summary default function (which calculates summary statistics on a vector of values) to two vectors. The first is to the vector of maximised projection indices; the intention is so one can see what kinds of values the large ones take. The second application is to the pseudo projection indices, those computed on random directions without optimisation. Essentially, real projection indices that are larger than the maximum pseudo indices might be interesting and worth looking at with, e.g. the plot.PP3 function.

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.

Examples

Run this code

# NOT RUN {
#
# The flea beetle data
#
data(beetle)
#
# Run projection pursuit with 10 random starts (usually MUCH more than this,
# but this example will be run on installation and testing and hence I
# want to minimize computational load. A more reasonable value is 1000)
#
beetle.PP3 <- PP3many(t(beetle), nrandstarts=10)
#
# Output from summary
#
summary(beetle.PP3)
#Summary statistics of projection index
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  13.84   15.36   17.50   17.30   19.08   20.39 
#Summary statistics of pseudo p-values
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  11.14   11.78   12.77   13.59   15.31   17.63 
# }

Run the code above in your browser using DataCamp Workspace