skbio.stats.gradient.WindowDifferenceGradientANOVA

class skbio.stats.gradient.WindowDifferenceGradientANOVA(coords, prop_expl, metadata_map, window_size, **kwargs)

Perform trajectory analysis using the modified first difference algorithm.

It calculates the norm for all the time-points and subtracts the mean of the next number of elements specified in window_size and the current element.

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

window_sizeint or long

The window size to use while computing the differences

Raises:

ValueError

If the window_size is not a positive integer

See also

GradientANOVA

Built-ins

__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).

Methods

get_trajectories()

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