WELCOME TO THE PRINCIPAL COMPONENTS ANALYSIS DEMO

PRINCIPAL COMPONENTS ANALYSIS attempts to describe
relationships and associations among the variables (the
columns) of multivariate data.  When dealing with a large
number of variables standard analysis methods are
excessively time-consuming.  In such circumstances, a
reduction in the number of variables is required.  Simply
choosing a subset of the original variables may result in
the loss of essential information.  An alternative procedure
is to construct a number of new variables from the original
variables.

PRINCIPAL COMPONENTS ANALYSIS is a method of data
reduction which aims to create a small number of derived
variables that can be used in place of the large number of
original variables.  The derived variables are uncorrelated
(containing unique data) and are used in the subsequent
analyses with virtually no loss of accuracy.

An example of the call to the function PCOMP is shown below.

newVariables = PCOMP(array, $
     COEFFICIENTS = eigenvectors, $
     EIGENVALUES = eigenvalues, $
     VARIANCES = variances, $
     NVARIABLES = nvariables, $
     /COVARIANCE, /STANDARDIZE)

The reference for the data used in the demo is shown below.
 
Statistical Methods For Medical Investigations
Brian S. Everitt
ISBN: 0-340-61431-5']], $


   MENU OPTIONS
   ------------
   File Menu:
      Select Quit to exit the Demo and return 
      to the IDL Demo main screen.

   About Menu:
      Select "About Principal Components Analysis" for information 
      about the Principal Components Analysi demo.


   FEATURES
   --------

   <<Show Eigenvalues>> button
      Displays a plot of the eigenvalues for the analysis.

   <<Show Variances>> button
      Displays the variances of the first through fifth derived variables.