This module contains several ordination methods, including Principal Coordinate Analysis, Correspondence Analysis, Redundancy Analysis and Canonical Correspondence Analysis.
| PCoA(distance_matrix) | Perform Principal Coordinate Analysis. |
| CA(X, row_ids, column_ids) | Compute correspondence analysis, a multivariate statistical technique for ordination. |
| RDA(Y, X, site_ids, species_ids[, scale_Y]) | Compute redundancy analysis, a type of canonical analysis. |
| CCA(Y, X, site_ids, species_ids) | Compute constrained (also known as canonical) correspondence analysis. |
| OrdinationResults(eigvals[, species, site, ...]) | Store ordination results |
This is an artificial dataset (table 11.3 in [R164]) that represents fish abundance in different sites (Y, the response variables) and environmental variables (X, the explanatory variables).
>>> import numpy as np
>>> X = np.array([[1.0, 0.0, 1.0, 0.0],
... [2.0, 0.0, 1.0, 0.0],
... [3.0, 0.0, 1.0, 0.0],
... [4.0, 0.0, 0.0, 1.0],
... [5.0, 1.0, 0.0, 0.0],
... [6.0, 0.0, 0.0, 1.0],
... [7.0, 1.0, 0.0, 0.0],
... [8.0, 0.0, 0.0, 1.0],
... [9.0, 1.0, 0.0, 0.0],
... [10.0, 0.0, 0.0, 1.0]])
>>> Y = np.array([[1, 0, 0, 0, 0, 0, 2, 4, 4],
... [0, 0, 0, 0, 0, 0, 5, 6, 1],
... [0, 1, 0, 0, 0, 0, 0, 2, 3],
... [11, 4, 0, 0, 8, 1, 6, 2, 0],
... [11, 5, 17, 7, 0, 0, 6, 6, 2],
... [9, 6, 0, 0, 6, 2, 10, 1, 4],
... [9, 7, 13, 10, 0, 0, 4, 5, 4],
... [7, 8, 0, 0, 4, 3, 6, 6, 4],
... [7, 9, 10, 13, 0, 0, 6, 2, 0],
... [5, 10, 0, 0, 2, 4, 0, 1, 3]])
We can now create a CCA object to perform canonical correspondence analysis. Matrix X contains a continuous variable (depth) and a categorical one (substrate type) encoded using a one-hot encoding. We explicitly need to avoid perfect collinearity, so we’ll drop one of the substrate types (the last column of X). We also expect to increase pandas integration to ease analyses.
>>> from skbio.math.stats.ordination import CCA
>>> ordination_result = CCA(Y, X[:, :-1],
... ['Site0', 'Site1', 'Site2', 'Site3', 'Site4',
... 'Site5', 'Site6', 'Site7', 'Site8', 'Site9'],
... ['Species0', 'Species1', 'Species2', 'Species3',
... 'Species4', 'Species5', 'Species6', 'Species7',
... 'Species8'])
Exploring the results we see that the first three axes explain about 80% of all the variance.
>>> sc_2 = ordination_result.scores(scaling=2)
>>> print sc_2.proportion_explained
[ 0.46691091 0.23832652 0.10054837 0.10493671 0.04480535 0.02974698
0.01263112 0.00156168 0.00053235]