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
Built-ins
__eq__
(value, /)Return self==value.
__ge__
(value, /)Return self>=value.
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
Compute the trajectories for each group in each category and run