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# 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:
    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`

  Methods (inherited)

  `get_trajectories` | Compute the trajectories for each group and category and run ANOVA.

  Special methods (inherited)

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

  Details