scikit_posthocs.posthoc_quade

scikit_posthocs.posthoc_quade(a: list | ndarray | DataFrame, y_col: str | None = None, group_col: str | None = None, block_col: str | None = None, block_id_col: str | None = None, dist: str = 't', p_adjust: str | None = None, melted: bool = False, sort: bool = False) → 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)