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r"""Ordination methods (:mod:`skbio.stats.ordination`)
==================================================
.. currentmodule:: skbio.stats.ordination
This module provides functions for ordination -- a category of methods that aim at
arranging data so that similar data points are proximate to each other. Ordination can
preserve and represent the structure of high-dimensional data within a low-dimensional
space, thereby facilitating visual exploration and statistical analysis.
Mathematically, ordination shares similarities with, and is in multiple respects
equivalent to, embedding and dimensionality reduction. While all three aim to represent
high-dimensional data in a lower-dimensional space, the term "ordination" is mainly
used in the field of ecology to reveal patterns such as groups or gradients underlying
community data. However, the ordination methods implemented in scikit-bio are
versatile, serving not only ecological studies but also broader applications in
scientific computing.
Multidimensional scaling
------------------------
.. autosummary::
:toctree:
pcoa
pcoa_biplot
Correspondence analysis
-----------------------
.. autosummary::
:toctree:
ca
Canonical analysis
------------------
.. autosummary::
:toctree:
cca
rda
Ordination results
------------------
.. autosummary::
:toctree:
OrdinationResults
Utility functions
-----------------
.. autosummary::
:toctree:
mean_and_std
corr
scale
svd_rank
e_matrix
f_matrix
Examples
--------
This is an artificial dataset (table 11.3 in [1]_) that represents fish
abundance in different sites (`Y`, the response variables) and
environmental variables (`X`, the explanatory variables).
>>> import numpy as np
>>> import pandas as pd
First we need to construct our explanatory variable dataset `X`.
>>> 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]])
>>> transects = ['depth', 'substrate_coral', 'substrate_sand',
... 'substrate_other']
>>> sites = ['site1', 'site2', 'site3', 'site4', 'site5', 'site6', 'site7',
... 'site8', 'site9', 'site10']
>>> X = pd.DataFrame(X, sites, transects)
Then we need to create a dataframe with the information about the species
observed at different sites.
>>> species = ['specie1', 'specie2', 'specie3', 'specie4', 'specie5',
... 'specie6', 'specie7', 'specie8', 'specie9']
>>> 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]])
>>> Y = pd.DataFrame(Y, sites, species)
We can now perform canonical correspondence analysis. Matrix `X` contains a
continuous variable (depth) and a categorical one (substrate type) encoded
using a one-hot encoding.
>>> from skbio.stats.ordination import cca
We explicitly need to avoid perfect collinearity, so we'll drop one of the
substrate types (the last column of `X`).
>>> del X['substrate_other']
>>> ordination_result = cca(Y, X, scaling=2)
Exploring the results we see that the first three axes explain about
80% of all the variance.
>>> ordination_result.proportion_explained
CCA1 0.466911
CCA2 0.238327
CCA3 0.100548
CCA4 0.104937
CCA5 0.044805
CCA6 0.029747
CCA7 0.012631
CCA8 0.001562
CCA9 0.000532
dtype: float64
References
----------
.. [1] Legendre P. and Legendre L. 1998. Numerical Ecology. Elsevier,
Amsterdam.
""" # noqa: D205, D415
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE.txt, distributed with this software.
# ----------------------------------------------------------------------------
from ._redundancy_analysis import rda
from ._correspondence_analysis import ca
from ._canonical_correspondence_analysis import cca
from ._principal_coordinate_analysis import pcoa, pcoa_biplot
from ._ordination_results import OrdinationResults
from ._utils import (
mean_and_std,
scale,
svd_rank,
corr,
e_matrix,
f_matrix,
center_distance_matrix,
)
__all__ = [
"ca",
"rda",
"cca",
"pcoa",
"pcoa_biplot",
"OrdinationResults",
"mean_and_std",
"scale",
"svd_rank",
"corr",
"e_matrix",
"f_matrix",
"center_distance_matrix",
]
|
