PP3 (version 1.2)

print.PP3: Print information about a PP3 object.

Description

Print information about a PP3 object.

Usage

# S3 method for PP3
print(x, …)

Arguments

x

PP3 object to print

…

Other arguments (not used)

Value

No specific value

Details

Prints information about a PP3 object. Primarily, the names of the components, the number of random starts that were used, the biggest (maximised) projection index that was found, and how many random starts achieved the max, and the index numbers of those. Then summary.PP3 is applied.

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, summary.PP3

Examples

# 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
#
print(beetle.PP3)
#Class 'PP3' : Three-dimensional Projection Pursuit Object:
#       ~~~  : List with 5 components with names
#              ix3 info pdata.list pseudp.vals origvarnames 
#
#Number of random start(s):  10 
#Maximum projection index is  20.39497  achieved by  1  random start(s).
#(Partial) list of those starts achieving max are:  9
#
#summary(.):
#----------
#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 
# }