pydiodon.coa

pydiodon.coa(X, k=- 1, meth='svd', transpose=False, withloops=False)

what it does

Correspondance Analysis of an array X

Parameters

Xa 2D numpy array, n x p

array to be analysed

kan integer

number of axis to compute

metha string

method for numerical computing

Returns

La 1D numpy array

vector of eigenvalues

Y_ra 2D numpy array,`n x k`

coordinates of row points

Y_ca 2D numpy array, p x k

coordinates of column points

Notes

If \(k=-1\), all axis are computed. If \(k > 0\), only k first axis and components are computed.

methods for PCA core

evd	EVD
svd	SVD with numpy.linalg.svd()
grp	SVD with gaussian random projection

Example

>>> import pydiodon as dio
>>> import numpy as np
>>> dataset = "../data4tests/coa4tests.txt"
>>> A, headers, rownames = dio.load(dataset)
>>> L, Y_r, Y_c = dio.coa(A)

note on the example

The dataset is in text format with tabs as delimiters. It contains headers and row names. These are default parameters for fucntion dio.load()

References:

Nenadic & Greenacre, Journal of Statistical Software, 20(3): 2-13, 2007

Lebart, Morineau & Fénelon, 1982, pp. 305-320

af, revised 21.02.21, 22.11.05