skbio.stats.gradient.WindowDifferenceGradientANOVA

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

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
  • coords (pandas.DataFrame) – The coordinates for each sample id

  • prop_expl (array like) – The numpy 1-D array with the proportion explained by each axis in coords

  • metadata_map (pandas.DataFrame) – The metadata map, indexed by sample ids and columns are metadata categories

  • window_size (int 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 in each category and run