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

__hash__

Return hash(self).

Methods

get_trajectories()

Compute the trajectories for each group in each category and run