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# 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:
    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

    trajectory_categories list 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_category str, optional

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

    axes int, optional

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

    weighted bool, 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

  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.