pingouin.pairwise_ttests

pingouin.pairwise_ttests(data=None, dv=None, between=None, within=None, subject=None, parametric=True, alpha=0.05, tail='two-sided', padjust='none', effsize='hedges', nan_policy='listwise', return_desc=False, interaction=True, export_filename=None)[source]

Pairwise T-tests.

Parameters
datapandas DataFrame

DataFrame. Note that this function can also directly be used as a Pandas method, in which case this argument is no longer needed.

dvstring

Name of column containing the dependant variable.

betweenstring or list with 2 elements

Name of column(s) containing the between factor(s).

withinstring or list with 2 elements

Name of column(s) containing the within factor(s).

subjectstring

Name of column containing the subject identifier. Compulsory for contrast including a within-subject factor.

parametricboolean

If True (default), use the parametric ttest() function. If False, use pingouin.wilcoxon() or pingouin.mwu() for paired or unpaired samples, respectively.

alphafloat

Significance level

tailstring

Specify whether the alternative hypothesis is ‘two-sided’ or ‘one-sided’. Can also be ‘greater’ or ‘less’ to specify the direction of the test. ‘greater’ tests the alternative that x has a larger mean than y. If tail is ‘one-sided’, Pingouin will automatically infer the one-sided alternative hypothesis of the test based on the test statistic.

padjuststring

Method used for testing and adjustment of pvalues. Available methods are

'none' : no correction
'bonf' : one-step Bonferroni correction
'sidak' : one-step Sidak correction
'holm' : step-down method using Bonferroni adjustments
'fdr_bh' : Benjamini/Hochberg FDR correction
'fdr_by' : Benjamini/Yekutieli FDR correction
effsizestring or None

Effect size type. Available methods are

'none' : no effect size
'cohen' : Unbiased Cohen d
'hedges' : Hedges g
'glass': Glass delta
'r' : Pearson correlation coefficient
'eta-square' : Eta-square
'odds-ratio' : Odds ratio
'AUC' : Area Under the Curve
'CLES' : Common Language Effect Size
nan_policystring

Can be ‘listwise’ for listwise deletion of missing values in repeated measures design (= complete-case analysis) or ‘pairwise’ for the more liberal pairwise deletion (= available-case analysis).

New in version 0.2.9.

return_descboolean

If True, append group means and std to the output dataframe

interactionboolean

If there are multiple factors and interaction is True (default), Pingouin will also calculate T-tests for the interaction term (see Notes).

New in version 0.2.9.

export_filenamestring

Filename (without extension) for the output file. If None, do not export the table. By default, the file will be created in the current python console directory. To change that, specify the filename with full path.

Returns
statsDataFrame

Stats summary

'A' : Name of first measurement
'B' : Name of second measurement
'Paired' : indicates whether the two measurements are paired or not
'Parametric' : indicates if (non)-parametric tests were used
'Tail' : indicate whether the p-values are one-sided or two-sided
'T' : T statistic (only if parametric=True)
'U-val' : Mann-Whitney U stat (if parametric=False and unpaired data)
'W-val' : Wilcoxon W stat (if parametric=False and paired data)
'dof' : degrees of freedom (only if parametric=True)
'p-unc' : Uncorrected p-values
'p-corr' : Corrected p-values
'p-adjust' : p-values correction method
'BF10' : Bayes Factor
'hedges' : effect size (or any effect size defined in ``effsize``)

Notes

Data are expected to be in long-format. If your data is in wide-format, you can use the pandas.melt() function to convert from wide to long format.

If between or within is a list (e.g. [‘col1’, ‘col2’]), the function returns 1) the pairwise T-tests between each values of the first column, 2) the pairwise T-tests between each values of the second column and 3) the interaction between col1 and col2. The interaction is dependent of the order of the list, so [‘col1’, ‘col2’] will not yield the same results as [‘col2’, ‘col1’], and will only be calculated if interaction=True.

In other words, if between is a list with two elements, the output model is between1 + between2 + between1 * between2.

Similarly, if within` is a list with two elements, the output model is within1 + within2 + within1 * within2.

If both between and within are specified, the function return within + between + within * between.

Missing values in repeated measurements are automatically removed using a listwise (default) or pairwise deletion strategy. However, you should be very careful since it can result in undesired values removal (especially for the interaction effect). We strongly recommend that you preprocess your data and remove the missing values before using this function.

This function has been tested against the pairwise.t.test R function.

Examples

  1. One between-factor

>>> from pingouin import pairwise_ttests, read_dataset
>>> df = read_dataset('mixed_anova.csv')
>>> post_hocs = pairwise_ttests(dv='Scores', between='Group', data=df)
  1. One within-factor

>>> post_hocs = pairwise_ttests(dv='Scores', within='Time',
...                             subject='Subject', data=df)
>>> print(post_hocs)  # doctest: +SKIP
  1. Non-parametric pairwise paired test (wilcoxon)

>>> pairwise_ttests(dv='Scores', within='Time', subject='Subject',
...                 data=df, parametric=False)  # doctest: +SKIP
  1. Within + Between + Within * Between with corrected p-values

>>> posthocs = pairwise_ttests(dv='Scores', within='Time',
...                            subject='Subject', between='Group',
...                            padjust='bonf', data=df)
  1. Between1 + Between2 + Between1 * Between2

>>> posthocs = pairwise_ttests(dv='Scores', between=['Group', 'Time'],
...                            data=df)
  1. Between1 + Between2, no interaction

>>> posthocs = df.pairwise_ttests(dv='Scores', between=['Group', 'Time'],
...                               interaction=False)