scikit_posthocs.posthoc_quade

scikit_posthocs.posthoc_quade(a: Union[list, numpy.ndarray, pandas.core.frame.DataFrame], y_col: Optional[str] = None, block_col: Optional[str] = None, group_col: Optional[str] = None, dist: str = 't', p_adjust: Optional[str] = None, melted: bool = False, sort: bool = False) pandas.core.frame.DataFrame

Calculate pairwise comparisons using Quade’s post hoc test for unreplicated blocked data. This test is usually conducted if significant results were obtained by the omnibus test 1, 2, 3.

Parameters
  • a (array_like or pandas DataFrame object) –

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

    If melted 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 (str or int, optional) – Must be specified if a is a pandas DataFrame object. Name of the column that contains y data.

  • block_col (str or int) – Must be specified if a is a pandas DataFrame object. Name of the column that contains blocking factor values.

  • group_col (str or int) – Must be specified if a is a pandas DataFrame object. Name of the column that contains treatment (group) factor values.

  • dist (str, optional) – Method for determining p values. The default distribution is “t”, else “normal”.

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

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

  • p_adjust (str, optional) – Method for adjusting p values. See statsmodels.sandbox.stats.multicomp for details. Available methods are: ‘bonferroni’ : one-step correction ‘sidak’ : one-step correction ‘holm-sidak’ : step-down method using Sidak adjustments ‘holm’ : step-down method using Bonferroni adjustments ‘simes-hochberg’ : step-up method (independent) ‘hommel’ : closed method based on Simes tests (non-negative) ‘fdr_bh’ : Benjamini/Hochberg (non-negative) ‘fdr_by’ : Benjamini/Yekutieli (negative) ‘fdr_tsbh’ : two stage fdr correction (non-negative) ‘fdr_tsbky’ : two stage fdr correction (non-negative)

Returns

result – P values.

Return type

pandas.DataFrame

References

1

W. J. Conover (1999), Practical nonparametric Statistics, 3rd. Edition, Wiley.

2

N. A. Heckert and J. J. Filliben (2003). NIST Handbook 148: Dataplot Reference Manual, Volume 2: Let Subcommands and Library Functions. National Institute of Standards and Technology Handbook Series, June 2003.

3

D. Quade (1979), Using weighted rankings in the analysis of complete blocks with additive block effects. Journal of the American Statistical Association, 74, 680-683.

Examples

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