Description Usage Arguments Details Value Author(s) References See Also Examples
Print summary statistics about a PP3 object.
1 2 |
object |
PP3 object |
... |
Other arguments (which aren't used) |
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.
Nothing explicit is returned
G. P. Nason
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | #
# 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
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