scikit_posthocs.posthoc_siegel_friedman

scikit_posthocs.posthoc_siegel_friedman(a, y_col=None, block_col=None, group_col=None, melted=False, sort=False, p_adjust=None)

Siegel and Castellan’s All-Pairs Comparisons Test for Unreplicated Blocked Data. See authors’ paper for additional information [1].

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 (strings).

  • y_col (str or int) – 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.
  • 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

Notes

For all-pairs comparisons in a two factorial unreplicated complete block design with non-normally distributed residuals, Siegel and Castellan’s test can be performed on Friedman-type ranked data.

References

[1]S. Siegel, N. J. Castellan Jr. (1988), Nonparametric Statistics for the Behavioral Sciences. 2nd ed. New York: McGraw-Hill.

Examples

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