Pairwise T-tests.
| Parameters: |
- dv : string
Name of column containing the dependant variable.
- between : string or list with 2 elements
Name of column(s) containing the between factor(s).
- within : string or list with 2 elements
Name of column(s) containing the within factor(s).
- subject : string
Name of column containing the subject identifier. Compulsory for
contrast including a within-subject factor.
- data : pandas DataFrame
DataFrame
- parametric : boolean
If True (default), use the parametric ttest() function.
If False, use pingouin.wilcoxon() or pingouin.mwu()
for paired or unpaired samples, respectively.
- alpha : float
Significance level
- tail : string
Indicates whether to return the ‘two-sided’ or ‘one-sided’ p-values
- padjust : string
Method used for testing and adjustment of pvalues.
Available methods are
'none' : no correction
'bonferroni' : one-step Bonferroni correction
'holm' : step-down method using Bonferroni adjustments
'fdr_bh' : Benjamini/Hochberg FDR correction
'fdr_by' : Benjamini/Yekutieli FDR correction
- effsize : string or None
Effect size type. Available methods are
'none' : no effect size
'cohen' : Unbiased Cohen d
'hedges' : Hedges g
'glass': Glass delta
'eta-square' : Eta-square
'odds-ratio' : Odds ratio
'AUC' : Area Under the Curve
- return_desc : boolean
If True, append group means and std to the output dataframe
- export_filename : string
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.
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| Returns: |
- stats : DataFrame
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-values (only if parametric=True)
'U' : Mann-Whitney U value (only if parametric=False and unpaired data)
'W' : Wilcoxon W value (only if parametric=False and paired data)
'p-unc' : Uncorrected p-values
'p-corr' : Corrected p-values
'p-adjust' : p-values correction method
'BF10' : Bayes Factor
'hedges' : Hedges effect size
'CLES' : Common language effect size
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See also
ttest- T-test.
wilcoxon- Non-parametric test for paired samples.
mwu- Non-parametric test for independent samples.
Notes
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’].
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.
Examples
- 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)
- One within-factor
>>> post_hocs = pairwise_ttests(dv='Scores', within='Time',
... subject='Subject', data=df)
>>> print(post_hocs)
- Non-parametric pairwise paired test (wilcoxon)
>>> pairwise_ttests(dv='Scores', within='Time', subject='Subject',
... data=df, parametric=False)
- Within + Between + Within * Between with corrected p-values
>>> pairwise_ttests(dv='Scores', within='Time', subject='Subject',
... between='Group', padjust='bonf', data=df)
- Between1 + Between2 + Between1 * Between2
>>> pairwise_ttests(dv='Scores', between=['Group', 'Time'], data=df)
