scikit_posthocs.test_durbin

scikit_posthocs.test_durbin(data: List | ndarray | DataFrame, y_col: str | int = None, block_col: str | int = None, group_col: str | int = None, melted: bool = False, sort: bool = True) → Tuple[float, float, int]

Durbin’s test whether k groups (or treatments) in a two-way balanced incomplete block design (BIBD) have identical effects. See references for additional information [1], [2].

Parameters:

  • data (Union[List, np.ndarray, DataFrame]) –

    An array, any object exposing the array interface or a pandas DataFrame with data values.

    If melted argument is set to False (default), a is a typical matrix of block design, i.e. rows are blocks, and columns are groups. In this case you do not need to specify col arguments.

    If a is an array and melted is set to True, y_col, block_col and group_col must specify the indices of columns containing elements of correspondary type.

    If a is a Pandas DataFrame and melted is set to True, y_col, block_col and group_col must specify columns names (string).

  • y_col (Union[str, int] = None) – Must be specified if a is a pandas DataFrame object. Name of the column that contains y data.

  • block_col (Union[str, int] = None) – Must be specified if a is a pandas DataFrame object. Name of the column that contains block names.

  • group_col (Union[str, int] = None) – Must be specified if a is a pandas DataFrame object. Name of the column that contains group names.

  • melted (bool = False) – Specifies if data are given as melted columns “y”, “blocks”, and “groups”.

  • sort (bool = False) – If True, sort data by block and group columns.

Returns:

P value, statistic, and number of degrees of freedom.

Return type:

Tuple[float, float, int]

References

Examples

>>> x = np.array([[31,27,24],[31,28,31],[45,29,46],[21,18,48],[42,36,46],[32,17,40]])
>>> sp.test_durbin(x)