Gradient analyses (skbio.stats.gradient)#

This module provides functionality for performing gradient analyses. The algorithms included in this module mainly allows performing analysis of volatility on time series data, but they can be applied to any data that contains a gradient.

Classes#

GradientANOVA

Base class for the Trajectory algorithms.

AverageGradientANOVA

Perform trajectory analysis using the RMS average algorithm.

TrajectoryGradientANOVA

Perform trajectory analysis using the RMS trajectory algorithm.

FirstDifferenceGradientANOVA

Perform trajectory analysis using the first difference algorithm.

WindowDifferenceGradientANOVA

Perform trajectory analysis using the modified first difference algorithm.

GroupResults

Store the trajectory results of a group of a metadata category.

CategoryResults

Store the trajectory results of a metadata category.

GradientANOVAResults

Store the trajectory results.

Examples#

Assume we have the following coordinates:

>>> import numpy as np
>>> import pandas as pd
>>> from skbio.stats.gradient import AverageGradientANOVA
>>> coord_data = {'PC.354': np.array([0.2761, -0.0341, 0.0633, 0.1004]),
...               'PC.355': np.array([0.2364, 0.2186, -0.0301, -0.0225]),
...               'PC.356': np.array([0.2208, 0.0874, -0.3519, -0.0031]),
...               'PC.607': np.array([-0.1055, -0.4140, -0.15, -0.116]),
...               'PC.634': np.array([-0.3716, 0.1154, 0.0721, 0.0898])}
>>> coords = pd.DataFrame.from_dict(coord_data, orient='index')

the following metadata map:

>>> metadata_map = {'PC.354': {'Treatment': 'Control', 'Weight': '60'},
...            'PC.355': {'Treatment': 'Control', 'Weight': '55'},
...            'PC.356': {'Treatment': 'Control', 'Weight': '50'},
...            'PC.607': {'Treatment': 'Fast', 'Weight': '65'},
...            'PC.634': {'Treatment': 'Fast', 'Weight': '68'}}
>>> metadata_map = pd.DataFrame.from_dict(metadata_map, orient='index')

and the following array with the proportion explained of each coord:

>>> prop_expl = np.array([25.6216, 15.7715, 14.1215, 11.6913, 9.8304])

Then to compute the average trajectory of this data:

>>> av = AverageGradientANOVA(coords, prop_expl, metadata_map,
...                     trajectory_categories=['Treatment'],
...                     sort_category='Weight')
>>> trajectory_results = av.get_trajectories()

Check the algorithm used to compute the trajectory_results:

>>> print(trajectory_results.algorithm)
avg

Check if we weighted the data or not:

>>> print(trajectory_results.weighted)
False

Check the results of one of the categories:

>>> print(trajectory_results.categories[0].category)
Treatment
>>> print(trajectory_results.categories[0].probability)
0.0118478282382

Check the results of one group of one of the categories:

>>> print(trajectory_results.categories[0].groups[0].name)
Control
>>> print(trajectory_results.categories[0].groups[0].trajectory)
[ 3.52199973  2.29597001  3.20309816]
>>> print(trajectory_results.categories[0].groups[0].info)
{'avg': 3.007022633956606}
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