scikit_posthocs.posthoc_conover_friedman

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

Calculate pairwise comparisons using Conover post hoc test for unreplicated blocked data. This test is usually conducted post hoc after significant results of the Friedman test. The statistics refer to the Student t distribution [1], [2].

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) ‘single-step’ : uses Tukey distribution for multiple comparisons
Returns:

result – P values.

Return type:

pandas DataFrame

Notes

A one-way ANOVA with repeated measures that is also referred to as ANOVA with unreplicated block design can also be conducted via the friedman.test. The consequent post hoc pairwise multiple comparison test according to Conover is conducted with this function.

If y is a matrix, than the columns refer to the treatment and the rows indicate the block.

References

[1]W. J. Conover and R. L. Iman (1979), On multiple-comparisons procedures, Tech. Rep. LA-7677-MS, Los Alamos Scientific Laboratory.
[2]W. J. Conover (1999), Practical nonparametric Statistics, 3rd. Edition, Wiley.

Examples

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