skbio.stats.gradient.GradientANOVA

class skbio.stats.gradient.GradientANOVA(coords, prop_expl, metadata_map, trajectory_categories=None, sort_category=None, axes=3, weighted=False)

Base class for the Trajectory algorithms.

Parameters:

coordspandas.DataFrame

The coordinates for each sample id

prop_explarray like

The numpy 1-D array with the proportion explained by each axis in coords

metadata_mappandas.DataFrame

The metadata map, indexed by sample ids and columns are metadata categories

trajectory_categorieslist of str, optional

A list of metadata categories to use to create the trajectories. If None is passed, the trajectories for all metadata categories are computed. Default: None, compute all of them

sort_categorystr, optional

The metadata category to use to sort the trajectories. Default: None

axesint, optional

The number of axes to account while doing the trajectory specific calculations. Pass 0 to compute all of them. Default: 3

weightedbool, optional

If true, the output is weighted by the space between samples in the sort_category column

Raises:

ValueError

If any category of trajectory_categories is not present in metadata_map If sort_category is not present in metadata_map If axes is not between 0 and the maximum number of axes available If weighted is True and no sort_category is provided If weighted is True and the values under sort_category are not numerical If coords and metadata_map does not have samples in common

Methods

get_trajectories()

Compute the trajectories for each group and category and run ANOVA.

Special methods (inherited)

__eq__(value, /)	Return self==value.
__ge__(value, /)	Return self>=value.
__getstate__(/)	Helper for pickle.
__gt__(value, /)	Return self>value.
__hash__(/)	Return hash(self).
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__str__(/)	Return str(self).

Details