Review principal component outliers using matplotlib graphics

Matplotlib was designed to provide a Matlab like environment within python, when using the matplotlib plots it is convenient to have the rest of matplotlib available.

from matplotlib.pylab import *

One incredibly important difference between the previous functions and the following functions are that these are 1 based. The first principal component is 1, the second is 2, etc.

Both PCAGinsu constructors take a similar list of parameters, they are in order, the dataset to operate on, the number of high and low outliers, the significance threshold, and what is the highest numbered principal component to analyze.

The following function call creates a PCAGinsu object using the cho dataset, still with the 10 outliers, but instead of the default .05 significance, it is using a .01 cutoff, and it will analyze components 1 through 17. (17 happens to the number of principal components that would be naturally found).

pcaginsu = pcaGinsu.pcaGinsuVisualizeMatplotlib(cho, 10, .01, 17)

Brandon King 2005-07-29