def test_compute_esci(self): """Test function compute_esci. Note that since Pingouin v0.3.5, CIs around a Cohen d are calculated using a T (and not Z) distribution. This is the same behavior as the cohen.d function of the effsize R package. However, note that the cohen.d function does not use the Cohen d-avg for paired samples, and therefore we cannot directly compare the CIs for paired samples. Similarly, R uses a slightly different formula to estimate the SE of one-sample cohen D. """ # Pearson correlation r = 0.5543563 ci = compute_esci(stat=r, nx=6, eftype='r') assert np.allclose(ci, [-0.47, 0.94]) # Cohen d # .. One sample and paired # Cannot compare to R because cohen.d uses different formulas for # Cohen d and SE. d = compute_effsize(np.r_[x, y], y=0) assert round(d, 6) == 2.086694 # Same as cohen.d ci = compute_esci(d, nx + ny, 1, decimals=6) d = compute_effsize(x, y, paired=True) ci = compute_esci(d, nx, ny, paired=True, decimals=6) # .. Independent (compare with cohen.d function) d = compute_effsize(x, y) ci = compute_esci(d, nx, ny, decimals=6) np.testing.assert_equal(ci, [-1.067645, 0.226762]) # Same but with different n d = compute_effsize(x, y[:-5]) ci = compute_esci(d, nx, len(y[:-5]), decimals=8) np.testing.assert_equal(ci, [-1.33603010, 0.08662825])
def test_compute_esci(self): """Test function compute_esci. Note that since Pingouin v0.3.5, CIs around a Cohen d are calculated using a T (and not Z) distribution. This is the same behavior as the cohen.d function of the effsize R package. However, note that the cohen.d function does not use the Cohen d-avg for paired samples, and therefore we cannot directly compare the CIs for paired samples. Similarly, R uses a slightly different formula to estimate the SE of one-sample cohen D. """ # Pearson correlation # https://github.com/SurajGupta/r-source/blob/master/src/library/stats/R/cor.test.R ci = compute_esci(stat=0.5543563, nx=6, eftype='r', decimals=6) assert np.allclose(ci, [-0.4675554, 0.9420809]) # Alternative == "greater" ci = compute_esci(stat=0.8, nx=20, eftype='r', alternative="greater", decimals=6) assert np.allclose(ci, [0.6041625, 1]) ci = compute_esci(stat=-0.2, nx=30, eftype='r', alternative="greater", decimals=6) assert np.allclose(ci, [-0.4771478, 1]) # Alternative == "less" ci = compute_esci(stat=-0.8, nx=20, eftype='r', alternative="less", decimals=6) assert np.allclose(ci, [-1, -0.6041625]) ci = compute_esci(stat=0.2, nx=30, eftype='r', alternative="less", decimals=6) assert np.allclose(ci, [-1, 0.4771478]) # Cohen d # .. One sample and paired # Cannot compare to R because cohen.d uses different formulas for # Cohen d and SE. d = compute_effsize(np.r_[x, y], y=0) assert round(d, 6) == 2.086694 # Same as cohen.d ci = compute_esci(d, nx + ny, 1, decimals=6) d = compute_effsize(x, y, paired=True) ci = compute_esci(d, nx, ny, paired=True, decimals=6) # .. Independent (compare with cohen.d function) d = compute_effsize(x, y) ci = compute_esci(d, nx, ny, decimals=6) np.testing.assert_equal(ci, [-1.067645, 0.226762]) # Same but with different n d = compute_effsize(x, y[:-5]) ci = compute_esci(d, nx, len(y[:-5]), decimals=8) np.testing.assert_equal(ci, [-1.33603010, 0.08662825])
def feature_scores(df: pd.DataFrame, features: list, endpoint: str, effect_size: str = "cles"): """ Given the covid admissions dataframe generated earlier, iterate over the given features and determine the p-value when comparing patients for a given endpoint e.g. death or death and ICU admission. All p-values are corrected for multiple comparisons using bonferroni method and alpha = 0.05 """ pos = df[df[endpoint] == 1] neg = df[df[endpoint] == 0] results = {"feature": [], "p-values": [], "effect size": []} for f in features: x, y = pos[f].dropna().values, neg[f].dropna().values results["feature"].append(f) p = stat_test(x, y) results["p-values"].append(p) es = compute_effsize(x, y, paired=False, eftype=effect_size) results["effect size"].append(es) results = pd.DataFrame(results) results["p-values"] = multipletests(results["p-values"].values, method="bonferroni", alpha=0.05)[1] return results
def func(x, y): return compute_effsize(x, y, paired=paired, eftype=func_str)
def pairwise_ttests(dv=None, between=None, within=None, subject=None, data=None, alpha=.05, tail='two-sided', padjust='none', effsize='hedges', return_desc=False, export_filename=None): '''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 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. 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 'Tail' : indicate whether the p-values are one-sided or two-sided 'T' : T-values 'p-unc' : Uncorrected p-values 'p-corr' : Corrected p-values 'p-adjust' : p-values correction method 'BF10' : Bayes Factor 'efsize' : effect sizes 'eftype' : type of effect size 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 -------- 1. One between-factor >>> from pingouin import pairwise_ttests >>> from pingouin.datasets import read_dataset >>> df = read_dataset('mixed_anova.csv') >>> post_hocs = pairwise_ttests(dv='Scores', between='Group', data=df) >>> print(post_hocs) 2. One within-factor >>> post_hocs = pairwise_ttests(dv='Scores', within='Time', >>> subject='Subject', data=df) >>> print(post_hocs) 3. Within + Between + Within * Between with corrected p-values >>> post_hocs = pairwise_ttests(dv='Scores', within='Time', >>> subject='Subject', between='Group', >>> padjust='bonf', data=df) >>> print(post_hocs) 3. Between1 + Between2 + Between1 * Between2 >>> pairwise_ttests(dv='Scores', between=['Group', 'Time'], data=df) ''' from pingouin.parametric import ttest # Safety checks _check_dataframe(dv=dv, between=between, within=within, subject=subject, effects='all', data=data) if tail not in ['one-sided', 'two-sided']: raise ValueError('Tail not recognized') if not isinstance(alpha, float): raise ValueError('Alpha must be float') # Check if we have multiple between or within factors multiple_between = False multiple_within = False contrast = None if isinstance(between, list): if len(between) > 1: multiple_between = True contrast = 'multiple_between' assert all([b in data.keys() for b in between]) else: between = between[0] if isinstance(within, list): if len(within) > 1: multiple_within = True contrast = 'multiple_within' assert all([w in data.keys() for w in within]) else: within = within[0] if all([multiple_within, multiple_between]): raise ValueError("Multiple between and within factors are", "currently not supported. Please select only one.") # Check the other cases if isinstance(between, str) and within is None: contrast = 'simple_between' assert between in data.keys() if isinstance(within, str) and between is None: contrast = 'simple_within' assert within in data.keys() if isinstance(between, str) and isinstance(within, str): contrast = 'within_between' assert all([between in data.keys(), within in data.keys()]) # Initialize empty variables stats = pd.DataFrame([]) ddic = {} if contrast in ['simple_within', 'simple_between']: # OPTION A: SIMPLE MAIN EFFECTS, WITHIN OR BETWEEN paired = True if contrast == 'simple_within' else False col = within if contrast == 'simple_within' else between # Remove NAN in repeated measurements if contrast == 'simple_within' and data[dv].isnull().values.any(): data = _remove_rm_na(dv=dv, within=within, subject=subject, data=data) # Extract effects labels = data[col].unique().tolist() for l in labels: ddic[l] = data.loc[data[col] == l, dv].values # Number and labels of possible comparisons if len(labels) >= 2: combs = list(combinations(labels, 2)) else: raise ValueError('Columns must have at least two unique values.') # Initialize vectors for comb in combs: col1, col2 = comb x = ddic.get(col1) y = ddic.get(col2) df_ttest = ttest(x, y, paired=paired, tail=tail) ef = compute_effsize(x=x, y=y, eftype=effsize, paired=paired) stats = _append_stats_dataframe(stats, x, y, col1, col2, alpha, paired, df_ttest, ef, effsize) stats['Contrast'] = col # Multiple comparisons padjust = None if stats['p-unc'].size <= 1 else padjust if padjust is not None: if padjust.lower() != 'none': _, stats['p-corr'] = multicomp(stats['p-unc'].values, alpha=alpha, method=padjust) stats['p-adjust'] = padjust else: stats['p-corr'] = None stats['p-adjust'] = None else: # B1: BETWEEN1 + BETWEEN2 + BETWEEN1 * BETWEEN2 # B2: WITHIN1 + WITHIN2 + WITHIN1 * WITHIN2 # B3: WITHIN + BETWEEN + WITHIN * BETWEEN if contrast == 'multiple_between': # B1 factors = between fbt = factors fwt = [None, None] # eft = ['between', 'between'] paired = False elif contrast == 'multiple_within': # B2 factors = within fbt = [None, None] fwt = factors # eft = ['within', 'within'] paired = True else: # B3 factors = [within, between] fbt = [None, between] fwt = [within, None] # eft = ['within', 'between'] paired = False for i, f in enumerate(factors): stats = stats.append(pairwise_ttests(dv=dv, between=fbt[i], within=fwt[i], subject=subject, data=data, alpha=alpha, tail=tail, padjust=padjust, effsize=effsize, return_desc=return_desc), ignore_index=True, sort=False) # Then compute the interaction between the factors labels_fac1 = data[factors[0]].unique().tolist() labels_fac2 = data[factors[1]].unique().tolist() comb_fac1 = list(combinations(labels_fac1, 2)) comb_fac2 = list(combinations(labels_fac2, 2)) lc_fac1 = len(comb_fac1) lc_fac2 = len(comb_fac2) for lw in labels_fac1: for l in labels_fac2: tmp = data.loc[data[factors[0]] == lw] ddic[lw, l] = tmp.loc[tmp[factors[1]] == l, dv].values # Pairwise comparisons combs = list(product(labels_fac1, comb_fac2)) for comb in combs: fac1, (col1, col2) = comb x = ddic.get((fac1, col1)) y = ddic.get((fac1, col2)) df_ttest = ttest(x, y, paired=paired, tail=tail) ef = compute_effsize(x=x, y=y, eftype=effsize, paired=paired) stats = _append_stats_dataframe(stats, x, y, col1, col2, alpha, paired, df_ttest, ef, effsize, fac1) # Update the Contrast columns txt_inter = factors[0] + ' * ' + factors[1] idxitr = np.arange(lc_fac1 + lc_fac2, stats.shape[0]).tolist() stats.loc[idxitr, 'Contrast'] = txt_inter # Multi-comparison columns if padjust is not None and padjust.lower() != 'none': _, pcor = multicomp(stats.loc[idxitr, 'p-unc'].values, alpha=alpha, method=padjust) stats.loc[idxitr, 'p-corr'] = pcor stats.loc[idxitr, 'p-adjust'] = padjust # --------------------------------------------------------------------- stats['Paired'] = stats['Paired'].astype(bool) # Reorganize column order col_order = [ 'Contrast', 'Time', 'A', 'B', 'mean(A)', 'std(A)', 'mean(B)', 'std(B)', 'Paired', 'T', 'tail', 'p-unc', 'p-corr', 'p-adjust', 'BF10', 'efsize', 'eftype' ] if return_desc is False: stats.drop(columns=['mean(A)', 'mean(B)', 'std(A)', 'std(B)'], inplace=True) stats = stats.reindex(columns=col_order) stats.dropna(how='all', axis=1, inplace=True) # Rename Time columns if contrast in ['multiple_within', 'multiple_between', 'within_between']: stats['Time'].fillna('-', inplace=True) stats.rename(columns={'Time': factors[0]}, inplace=True) if export_filename is not None: _export_table(stats, export_filename) return stats
def test_compute_effsize(self): """Test function compute_effsize""" compute_effsize(x=x, y=y, eftype='cohen', paired=False) compute_effsize(x=x, y=y, eftype='AUC', paired=True) compute_effsize(x=x, y=y, eftype='r', paired=False) compute_effsize(x=x, y=y, eftype='odds-ratio', paired=False) compute_effsize(x=x, y=y, eftype='eta-square', paired=False) compute_effsize(x=x, y=y, eftype='cles', paired=False) compute_effsize(x=x, y=y, eftype='none', paired=False) # Unequal variances z = np.random.normal(2.5, 3, 30) compute_effsize(x=x, y=z, eftype='cohen') # Wrong effect size type with pytest.raises(ValueError): compute_effsize(x=x, y=y, eftype='wrong') # Unequal sample size with paired == True z = np.random.normal(2.5, 3, 25) compute_effsize(x=x, y=z, paired=True) # Compare with the effsize R package a = [3.2, 6.4, 1.8, 2.4, 5.8, 6.5] b = [2.4, 3.2, 3.2, 1.4, 2.8, 3.5] d = compute_effsize(x=a, y=b, eftype='cohen', paired=False) assert np.isclose(d, 1.002549) # Note that ci are different than from R because we use a normal and # not a T distribution to estimate the CI. # Also, for paired samples, effsize does not return the Cohen d-avg. # ci = compute_esci(ef=d, nx=na, ny=nb) # assert ci[0] == -.2 # assert ci[1] == 2.2 # With Hedges correction g = compute_effsize(x=a, y=b, eftype='hedges', paired=False) assert np.isclose(g, 0.9254296) # CLES # Compare to # https://janhove.github.io/reporting/2016/11/16/common-language-effect-sizes x2 = [20, 22, 19, 20, 22, 18, 24, 20, 19, 24, 26, 13] y2 = [38, 37, 33, 29, 14, 12, 20, 22, 17, 25, 26, 16] cl = compute_effsize(x=x2, y=y2, eftype='cles') assert np.isclose(cl, 0.3958333) assert np.isclose((1 - cl), compute_effsize(x=y2, y=x2, eftype='cles'))
def test_compute_effsize(self): """Test function compute_effsize""" compute_effsize(x=x, y=y, eftype='cohen', paired=False) compute_effsize(x=x, y=y, eftype='AUC', paired=True) compute_effsize(x=x, y=y, eftype='r', paired=False) compute_effsize(x=x, y=y, eftype='glass', paired=False) compute_effsize(x=x, y=y, eftype='odds-ratio', paired=False) compute_effsize(x=x, y=y, eftype='eta-square', paired=False) compute_effsize(x=x, y=y, eftype='none', paired=False) # Unequal variances z = np.random.normal(2.5, 3, 30) compute_effsize(x=x, y=z, eftype='cohen') # Wrong effect size type with pytest.raises(ValueError): compute_effsize(x=x, y=y, eftype='wrong') # Unequal sample size with paired == True z = np.random.normal(2.5, 3, 20) compute_effsize(x=x, y=z, paired=True) # Compare with the effsize R package a = [3.2, 6.4, 1.8, 2.4, 5.8, 6.5] b = [2.4, 3.2, 3.2, 1.4, 2.8, 3.5] # na = len(a) # nb = len(b) d = compute_effsize(x=a, y=b, eftype='cohen', paired=False) assert np.isclose(d, 1.002549) # Note that ci are different than from R because we use a normal and # not a T distribution to estimate the CI # ci = compute_esci(ef=d, nx=na, ny=nb) # assert ci[0] == -.2 # assert ci[1] == 2.2 # With Hedges correction g = compute_effsize(x=a, y=b, eftype='hedges', paired=False) assert np.isclose(g, 0.9254296)
def pairwise_ttests(dv=None, between=None, within=None, subject=None, data=None, parametric=True, alpha=.05, tail='two-sided', padjust='none', effsize='hedges', return_desc=False, export_filename=None): '''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. Note that this function can also directly be used as a Pandas method, in which case this argument is no longer needed. parametric : boolean If True (default), use the parametric :py:func:`ttest` function. If False, use :py:func:`pingouin.wilcoxon` or :py:func:`pingouin.mwu` for paired or unpaired samples, respectively. alpha : float Significance level tail : string 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. 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. 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) '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' : Hedges effect size 'CLES' : Common language effect size See also -------- ttest, mwu, wilcoxon, compute_effsize, multicomp Notes ----- Data are expected to be in long-format. If your data is in wide-format, you can use the :py:func:`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']. 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 the :py:func:`pingouin.remove_rm_na` function. 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) 2. One within-factor >>> post_hocs = pairwise_ttests(dv='Scores', within='Time', ... subject='Subject', data=df) >>> print(post_hocs) # doctest: +SKIP 3. Non-parametric pairwise paired test (wilcoxon) >>> pairwise_ttests(dv='Scores', within='Time', subject='Subject', ... data=df, parametric=False) # doctest: +SKIP 4. Within + Between + Within * Between with corrected p-values >>> posthocs = pairwise_ttests(dv='Scores', within='Time', ... subject='Subject', between='Group', ... padjust='bonf', data=df) 5. Between1 + Between2 + Between1 * Between2 >>> posthocs = pairwise_ttests(dv='Scores', between=['Group', 'Time'], ... data=df) ''' from .parametric import ttest from .nonparametric import wilcoxon, mwu # Safety checks _check_dataframe(dv=dv, between=between, within=within, subject=subject, effects='all', data=data) if tail not in ['one-sided', 'two-sided', 'greater', 'less']: raise ValueError('Tail not recognized') if not isinstance(alpha, float): raise ValueError('Alpha must be float') # Check if we have multiple between or within factors multiple_between = False multiple_within = False contrast = None if isinstance(between, list): if len(between) > 1: multiple_between = True contrast = 'multiple_between' assert all([b in data.keys() for b in between]) else: between = between[0] if isinstance(within, list): if len(within) > 1: multiple_within = True contrast = 'multiple_within' assert all([w in data.keys() for w in within]) else: within = within[0] if all([multiple_within, multiple_between]): raise ValueError("Multiple between and within factors are", "currently not supported. Please select only one.") # Check the other cases if isinstance(between, str) and within is None: contrast = 'simple_between' assert between in data.keys() if isinstance(within, str) and between is None: contrast = 'simple_within' assert within in data.keys() if isinstance(between, str) and isinstance(within, str): contrast = 'within_between' assert all([between in data.keys(), within in data.keys()]) # Initialize empty variables stats = pd.DataFrame([]) ddic = {} if contrast in ['simple_within', 'simple_between']: # OPTION A: SIMPLE MAIN EFFECTS, WITHIN OR BETWEEN paired = True if contrast == 'simple_within' else False col = within if contrast == 'simple_within' else between # Remove NAN in repeated measurements if contrast == 'simple_within' and data[dv].isnull().values.any(): data = remove_rm_na(dv=dv, within=within, subject=subject, data=data) # Extract effects labels = data[col].unique().tolist() for l in labels: ddic[l] = data.loc[data[col] == l, dv].values # Number and labels of possible comparisons if len(labels) >= 2: combs = list(combinations(labels, 2)) else: raise ValueError('Columns must have at least two unique values.') # Initialize vectors for comb in combs: col1, col2 = comb x = ddic.get(col1) y = ddic.get(col2) if parametric: df_ttest = ttest(x, y, paired=paired, tail=tail) # Compute exact CLES df_ttest['CLES'] = compute_effsize(x, y, paired=paired, eftype='CLES') else: if paired: df_ttest = wilcoxon(x, y, tail=tail) else: df_ttest = mwu(x, y, tail=tail) # Compute Hedges / Cohen ef = compute_effsize(x=x, y=y, eftype=effsize, paired=paired) stats = _append_stats_dataframe(stats, x, y, col1, col2, alpha, paired, tail, df_ttest, ef, effsize) stats['Contrast'] = col # Multiple comparisons padjust = None if stats['p-unc'].size <= 1 else padjust if padjust is not None: if padjust.lower() != 'none': _, stats['p-corr'] = multicomp(stats['p-unc'].values, alpha=alpha, method=padjust) stats['p-adjust'] = padjust else: stats['p-corr'] = None stats['p-adjust'] = None else: # B1: BETWEEN1 + BETWEEN2 + BETWEEN1 * BETWEEN2 # B2: WITHIN1 + WITHIN2 + WITHIN1 * WITHIN2 # B3: WITHIN + BETWEEN + WITHIN * BETWEEN if contrast == 'multiple_between': # B1 factors = between fbt = factors fwt = [None, None] # eft = ['between', 'between'] paired = False elif contrast == 'multiple_within': # B2 factors = within fbt = [None, None] fwt = factors # eft = ['within', 'within'] paired = True else: # B3 factors = [within, between] fbt = [None, between] fwt = [within, None] # eft = ['within', 'between'] paired = False for i, f in enumerate(factors): stats = stats.append(pairwise_ttests(dv=dv, between=fbt[i], within=fwt[i], subject=subject, data=data, parametric=parametric, alpha=alpha, tail=tail, padjust=padjust, effsize=effsize, return_desc=return_desc), ignore_index=True, sort=False) # Rename effect size to generic name stats.rename(columns={effsize: 'efsize'}, inplace=True) # Then compute the interaction between the factors labels_fac1 = data[factors[0]].unique().tolist() labels_fac2 = data[factors[1]].unique().tolist() comb_fac1 = list(combinations(labels_fac1, 2)) comb_fac2 = list(combinations(labels_fac2, 2)) lc_fac1 = len(comb_fac1) lc_fac2 = len(comb_fac2) for lw in labels_fac1: for l in labels_fac2: tmp = data.loc[data[factors[0]] == lw] ddic[lw, l] = tmp.loc[tmp[factors[1]] == l, dv].values # Pairwise comparisons combs = list(product(labels_fac1, comb_fac2)) for comb in combs: fac1, (col1, col2) = comb x = ddic.get((fac1, col1)) y = ddic.get((fac1, col2)) if parametric: df_ttest = ttest(x, y, paired=paired, tail=tail) # Compute exact CLES df_ttest['CLES'] = compute_effsize(x, y, paired=paired, eftype='CLES') else: if paired: df_ttest = wilcoxon(x, y, tail=tail) else: df_ttest = mwu(x, y, tail=tail) ef = compute_effsize(x=x, y=y, eftype=effsize, paired=paired) stats = _append_stats_dataframe(stats, x, y, col1, col2, alpha, paired, tail, df_ttest, ef, effsize, fac1) # Update the Contrast columns txt_inter = factors[0] + ' * ' + factors[1] idxitr = np.arange(lc_fac1 + lc_fac2, stats.shape[0]).tolist() stats.loc[idxitr, 'Contrast'] = txt_inter # Multi-comparison columns if padjust is not None and padjust.lower() != 'none': _, pcor = multicomp(stats.loc[idxitr, 'p-unc'].values, alpha=alpha, method=padjust) stats.loc[idxitr, 'p-corr'] = pcor stats.loc[idxitr, 'p-adjust'] = padjust # --------------------------------------------------------------------- stats['Paired'] = stats['Paired'].astype(bool) stats['Parametric'] = parametric # Round effect size and CLES stats[['efsize', 'CLES']] = stats[['efsize', 'CLES']].round(3) # Reorganize column order col_order = [ 'Contrast', 'Time', 'A', 'B', 'mean(A)', 'std(A)', 'mean(B)', 'std(B)', 'Paired', 'Parametric', 'T', 'U', 'W', 'dof', 'tail', 'p-unc', 'p-corr', 'p-adjust', 'BF10', 'CLES', 'efsize' ] if return_desc is False: stats.drop(columns=['mean(A)', 'mean(B)', 'std(A)', 'std(B)'], inplace=True) stats = stats.reindex(columns=col_order) stats.dropna(how='all', axis=1, inplace=True) # Rename effect size column stats.rename(columns={'efsize': effsize}, inplace=True) # Rename Time columns if contrast in ['multiple_within', 'multiple_between', 'within_between']: stats['Time'].fillna('-', inplace=True) stats.rename(columns={'Time': factors[0]}, inplace=True) if export_filename is not None: _export_table(stats, export_filename) return stats
def pairwise_ttests(data=None, dv=None, between=None, within=None, subject=None, parametric=True, alpha=.05, tail='two-sided', padjust='none', effsize='hedges', nan_policy='listwise', return_desc=False, interaction=True, export_filename=None): '''Pairwise T-tests. Parameters ---------- data : pandas DataFrame DataFrame. Note that this function can also directly be used as a Pandas method, in which case this argument is no longer needed. 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. parametric : boolean If True (default), use the parametric :py:func:`ttest` function. If False, use :py:func:`pingouin.wilcoxon` or :py:func:`pingouin.mwu` for paired or unpaired samples, respectively. alpha : float Significance level tail : string 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. padjust : string 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 effsize : string 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_policy : string 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). .. versionadded:: 0.2.9 return_desc : boolean If True, append group means and std to the output dataframe interaction : boolean If there are multiple factors and ``interaction`` is True (default), Pingouin will also calculate T-tests for the interaction term (see Notes). .. versionadded:: 0.2.9 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. 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 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``) See also -------- ttest, mwu, wilcoxon, compute_effsize, multicomp Notes ----- Data are expected to be in long-format. If your data is in wide-format, you can use the :py:func:`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) 2. One within-factor >>> post_hocs = pairwise_ttests(dv='Scores', within='Time', ... subject='Subject', data=df) >>> print(post_hocs) # doctest: +SKIP 3. Non-parametric pairwise paired test (wilcoxon) >>> pairwise_ttests(dv='Scores', within='Time', subject='Subject', ... data=df, parametric=False) # doctest: +SKIP 4. Within + Between + Within * Between with corrected p-values >>> posthocs = pairwise_ttests(dv='Scores', within='Time', ... subject='Subject', between='Group', ... padjust='bonf', data=df) 5. Between1 + Between2 + Between1 * Between2 >>> posthocs = pairwise_ttests(dv='Scores', between=['Group', 'Time'], ... data=df) 6. Between1 + Between2, no interaction >>> posthocs = df.pairwise_ttests(dv='Scores', between=['Group', 'Time'], ... interaction=False) ''' from .parametric import ttest from .nonparametric import wilcoxon, mwu # Safety checks _check_dataframe(dv=dv, between=between, within=within, subject=subject, effects='all', data=data) assert tail in ['one-sided', 'two-sided', 'greater', 'less'] assert isinstance(alpha, float), 'alpha must be float.' assert nan_policy in ['listwise', 'pairwise'] # Check if we have multiple between or within factors multiple_between = False multiple_within = False contrast = None if isinstance(between, list): if len(between) > 1: multiple_between = True contrast = 'multiple_between' assert all([b in data.keys() for b in between]) else: between = between[0] if isinstance(within, list): if len(within) > 1: multiple_within = True contrast = 'multiple_within' assert all([w in data.keys() for w in within]) else: within = within[0] if all([multiple_within, multiple_between]): raise ValueError("Multiple between and within factors are", "currently not supported. Please select only one.") # Check the other cases if isinstance(between, str) and within is None: contrast = 'simple_between' assert between in data.keys() if isinstance(within, str) and between is None: contrast = 'simple_within' assert within in data.keys() if isinstance(between, str) and isinstance(within, str): contrast = 'within_between' assert all([between in data.keys(), within in data.keys()]) # Reorganize column order col_order = [ 'Contrast', 'Time', 'A', 'B', 'mean(A)', 'std(A)', 'mean(B)', 'std(B)', 'Paired', 'Parametric', 'T', 'U-val', 'W-val', 'dof', 'Tail', 'p-unc', 'p-corr', 'p-adjust', 'BF10', effsize ] if contrast in ['simple_within', 'simple_between']: # OPTION A: SIMPLE MAIN EFFECTS, WITHIN OR BETWEEN paired = True if contrast == 'simple_within' else False col = within if contrast == 'simple_within' else between # Remove NAN in repeated measurements if contrast == 'simple_within' and data[dv].isnull().values.any(): # Only if nan_policy == 'listwise'. For pairwise deletion, # missing values will be removed directly in the lower-level # functions (e.g. pg.ttest) if nan_policy == 'listwise': data = remove_rm_na(dv=dv, within=within, subject=subject, data=data) else: # The `remove_rm_na` also aggregate other repeated measures # factor using the mean. Here, we ensure this behavior too. data = data.groupby([subject, within])[dv].mean().reset_index() # Now we check that subjects are present in all conditions # For example, if we have four subjects and 3 conditions, # and if subject 2 have missing data at the third condition, # we still need a row with missing values for this subject. if data.groupby(within)[subject].count().nunique() != 1: raise ValueError("Repeated measures dataframe is not balanced." " `Subjects` must have the same number of " "elements in all conditions, " "even when missing values are present.") # Extract effects grp_col = data.groupby(col, sort=False)[dv] labels = grp_col.groups.keys() # Number and labels of possible comparisons if len(labels) >= 2: combs = list(combinations(labels, 2)) combs = np.array(combs) A = combs[:, 0] B = combs[:, 1] else: raise ValueError('Columns must have at least two unique values.') # Initialize dataframe stats = pd.DataFrame(dtype=np.float64, index=range(len(combs)), columns=col_order) # Force dtype conversion cols_str = ['Contrast', 'Time', 'A', 'B', 'Tail', 'p-adjust', 'BF10'] cols_bool = ['Parametric', 'Paired'] stats[cols_str] = stats[cols_str].astype(object) stats[cols_bool] = stats[cols_bool].astype(bool) # Fill str columns stats.loc[:, 'A'] = A stats.loc[:, 'B'] = B stats.loc[:, 'Contrast'] = col stats.loc[:, 'Tail'] = tail stats.loc[:, 'Paired'] = paired for i in range(stats.shape[0]): col1, col2 = stats.at[i, 'A'], stats.at[i, 'B'] x = grp_col.get_group(col1).to_numpy(dtype=np.float64) y = grp_col.get_group(col2).to_numpy(dtype=np.float64) if parametric: stat_name = 'T' df_ttest = ttest(x, y, paired=paired, tail=tail) stats.at[i, 'BF10'] = df_ttest.at['T-test', 'BF10'] stats.at[i, 'dof'] = df_ttest.at['T-test', 'dof'] else: if paired: stat_name = 'W-val' df_ttest = wilcoxon(x, y, tail=tail) else: stat_name = 'U-val' df_ttest = mwu(x, y, tail=tail) # Compute Hedges / Cohen ef = np.round( compute_effsize(x=x, y=y, eftype=effsize, paired=paired), 3) if return_desc: stats.at[i, 'mean(A)'] = np.round(np.nanmean(x), 3) stats.at[i, 'mean(B)'] = np.round(np.nanmean(y), 3) stats.at[i, 'std(A)'] = np.round(np.nanstd(x), 3) stats.at[i, 'std(B)'] = np.round(np.nanstd(y), 3) stats.at[i, stat_name] = df_ttest[stat_name].iat[0] stats.at[i, 'p-unc'] = df_ttest['p-val'].iat[0] stats.at[i, effsize] = ef # Multiple comparisons padjust = None if stats['p-unc'].size <= 1 else padjust if padjust is not None: if padjust.lower() != 'none': _, stats['p-corr'] = multicomp(stats['p-unc'].values, alpha=alpha, method=padjust) stats['p-adjust'] = padjust else: stats['p-corr'] = None stats['p-adjust'] = None else: # B1: BETWEEN1 + BETWEEN2 + BETWEEN1 * BETWEEN2 # B2: WITHIN1 + WITHIN2 + WITHIN1 * WITHIN2 # B3: WITHIN + BETWEEN + WITHIN * BETWEEN if contrast == 'multiple_between': # B1 factors = between fbt = factors fwt = [None, None] # eft = ['between', 'between'] paired = False elif contrast == 'multiple_within': # B2 factors = within fbt = [None, None] fwt = factors # eft = ['within', 'within'] paired = True else: # B3 factors = [within, between] fbt = [None, between] fwt = [within, None] # eft = ['within', 'between'] paired = False stats = pd.DataFrame() for i, f in enumerate(factors): stats = stats.append(pairwise_ttests(dv=dv, between=fbt[i], within=fwt[i], subject=subject, data=data, parametric=parametric, alpha=alpha, tail=tail, padjust=padjust, effsize=effsize, return_desc=return_desc), ignore_index=True, sort=False) # Then compute the interaction between the factors if interaction: nrows = stats.shape[0] grp_fac1 = data.groupby(factors[0], sort=False)[dv] grp_fac2 = data.groupby(factors[1], sort=False)[dv] grp_both = data.groupby(factors, sort=False)[dv] labels_fac1 = grp_fac1.groups.keys() labels_fac2 = grp_fac2.groups.keys() # comb_fac1 = list(combinations(labels_fac1, 2)) comb_fac2 = list(combinations(labels_fac2, 2)) # Pairwise comparisons combs_list = list(product(labels_fac1, comb_fac2)) ncombs = len(combs_list) # np.array(combs_list) does not work because of tuples # we therefore need to flatten the tupple combs = np.zeros(shape=(ncombs, 3), dtype=object) for i in range(ncombs): combs[i] = _flatten_list(combs_list[i], include_tuple=True) # Append empty rows idxiter = np.arange(nrows, nrows + ncombs) stats = stats.append(pd.DataFrame(columns=stats.columns, index=idxiter), ignore_index=True) # Update other columns stats.loc[idxiter, 'Contrast'] = factors[0] + ' * ' + factors[1] stats.loc[idxiter, 'Time'] = combs[:, 0] stats.loc[idxiter, 'Paired'] = paired stats.loc[idxiter, 'Tail'] = tail stats.loc[idxiter, 'A'] = combs[:, 1] stats.loc[idxiter, 'B'] = combs[:, 2] for i, comb in enumerate(combs): ic = nrows + i # Take into account previous rows fac1, col1, col2 = comb x = grp_both.get_group((fac1, col1)).to_numpy(dtype=np.float64) y = grp_both.get_group((fac1, col2)).to_numpy(dtype=np.float64) ef = np.round( compute_effsize(x=x, y=y, eftype=effsize, paired=paired), 3) if parametric: stat_name = 'T' df_ttest = ttest(x, y, paired=paired, tail=tail) stats.at[ic, 'BF10'] = df_ttest.at['T-test', 'BF10'] stats.at[ic, 'dof'] = df_ttest.at['T-test', 'dof'] else: if paired: stat_name = 'W-val' df_ttest = wilcoxon(x, y, tail=tail) else: stat_name = 'U-val' df_ttest = mwu(x, y, tail=tail) # Append to stats if return_desc: stats.at[ic, 'mean(A)'] = np.round(np.nanmean(x), 3) stats.at[ic, 'mean(B)'] = np.round(np.nanmean(y), 3) stats.at[ic, 'std(A)'] = np.round(np.nanstd(x), 3) stats.at[ic, 'std(B)'] = np.round(np.nanstd(y), 3) stats.at[ic, stat_name] = df_ttest[stat_name].iat[0] stats.at[ic, 'p-unc'] = df_ttest['p-val'].iat[0] stats.at[ic, effsize] = ef # Multi-comparison columns if padjust is not None and padjust.lower() != 'none': _, pcor = multicomp(stats.loc[idxiter, 'p-unc'].values, alpha=alpha, method=padjust) stats.loc[idxiter, 'p-corr'] = pcor stats.loc[idxiter, 'p-adjust'] = padjust # --------------------------------------------------------------------- # Append parametric columns stats.loc[:, 'Parametric'] = parametric # Reorder and drop empty columns stats = stats[np.array(col_order)[np.isin(col_order, stats.columns)]] stats = stats.dropna(how='all', axis=1) # Rename Time columns if (contrast in ['multiple_within', 'multiple_between', 'within_between'] and interaction): stats['Time'].fillna('-', inplace=True) stats.rename(columns={'Time': factors[0]}, inplace=True) if export_filename is not None: _export_table(stats, export_filename) return stats
def pairwise_ttests(data=None, dv=None, between=None, within=None, subject=None, parametric=True, marginal=True, alpha=.05, tail='two-sided', padjust='none', effsize='hedges', correction='auto', nan_policy='listwise', return_desc=False, interaction=True): """Pairwise T-tests. Parameters ---------- data : :py:class:`pandas.DataFrame` DataFrame. Note that this function can also directly be used as a Pandas method, in which case this argument is no longer needed. dv : string Name of column containing the dependant variable. between : string or list with 2 elements Name of column(s) containing the between-subject factor(s). .. warning:: Note that Pingouin gives slightly different T and p-values compared to JASP posthoc tests for 2-way factorial design, because Pingouin does not pool the standard error for each factor, but rather calculate each pairwise T-test completely independent of others. within : string or list with 2 elements Name of column(s) containing the within-subject factor(s), i.e. the repeated measurements. subject : string Name of column containing the subject identifier. This is compulsory when ``within`` is specified. parametric : boolean If True (default), use the parametric :py:func:`ttest` function. If False, use :py:func:`pingouin.wilcoxon` or :py:func:`pingouin.mwu` for paired or unpaired samples, respectively. marginal : boolean If True, average over repeated measures factor when working with mixed or two-way repeated measures design. For instance, in mixed design, the between-subject pairwise T-test(s) will be calculated after averaging across all levels of the within-subject repeated measures factor (the so-called *"marginal means"*). Similarly, in two-way repeated measures factor, the pairwise T-test(s) will be calculated after averaging across all levels of the other repeated measures factor. Setting ``marginal=True`` is recommended when doing posthoc testing with multiple factors in order to avoid violating the assumption of independence and conflating the degrees of freedom by the number of repeated measurements. This is the default behavior of JASP. .. warning:: The default behavior of Pingouin <0.3.2 was ``marginal = False``, which may have led to incorrect p-values for mixed or two-way repeated measures design. Make sure to always use the latest version of Pingouin. .. versionadded:: 0.3.2 alpha : float Significance level tail : string 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. padjust : string Method used for testing and adjustment of pvalues. * ``'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 effsize : string 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 correction : string or boolean For unpaired two sample T-tests, specify whether or not to correct for unequal variances using Welch separate variances T-test. If `'auto'`, it will automatically uses Welch T-test when the sample sizes are unequal, as recommended by Zimmerman 2004. .. versionadded:: 0.3.2 nan_policy : string 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). .. versionadded:: 0.2.9 return_desc : boolean If True, append group means and std to the output dataframe interaction : boolean If there are multiple factors and ``interaction`` is True (default), Pingouin will also calculate T-tests for the interaction term (see Notes). .. versionadded:: 0.2.9 Returns ------- stats : :py:class:`pandas.DataFrame` * ``'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``) See also -------- ttest, mwu, wilcoxon, compute_effsize, multicomp Notes ----- Data are expected to be in long-format. If your data is in wide-format, you can use the :py:func:`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 output model is within + between + within * between (= mixed design). 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 <https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/pairwise.t.test>`_ R function. .. warning:: Versions of Pingouin below 0.3.2 gave incorrect results for mixed and two-way repeated measures design (see above warning for the ``marginal`` argument). .. warning:: Pingouin gives slightly different results than the JASP's posthoc module when working with multiple factors (e.g. mixed, factorial or 2-way repeated measures design). This is mostly caused by the fact that Pingouin does not pool the standard error for between-subject and interaction contrasts. You should always double check your results with JASP or another statistical software. Examples -------- For more examples, please refer to the `Jupyter notebooks <https://github.com/raphaelvallat/pingouin/blob/master/notebooks/01_ANOVA.ipynb>`_ 1. One between-subject factor >>> from pingouin import pairwise_ttests, read_dataset >>> df = read_dataset('mixed_anova.csv') >>> pairwise_ttests(dv='Scores', between='Group', data=df) # doctest: +SKIP 2. One within-subject factor >>> post_hocs = pairwise_ttests(dv='Scores', within='Time', ... subject='Subject', data=df) >>> print(post_hocs) # doctest: +SKIP 3. Non-parametric pairwise paired test (wilcoxon) >>> pairwise_ttests(dv='Scores', within='Time', subject='Subject', ... data=df, parametric=False) # doctest: +SKIP 4. Mixed design (within and between) with bonferroni-corrected p-values >>> posthocs = pairwise_ttests(dv='Scores', within='Time', ... subject='Subject', between='Group', ... padjust='bonf', data=df) 5. Two between-subject factors. The order of the list matters! >>> posthocs = pairwise_ttests(dv='Scores', between=['Group', 'Time'], ... data=df) 6. Same but without the interaction >>> posthocs = df.pairwise_ttests(dv='Scores', between=['Group', 'Time'], ... interaction=False) """ from .parametric import ttest from .nonparametric import wilcoxon, mwu # Safety checks _check_dataframe(dv=dv, between=between, within=within, subject=subject, effects='all', data=data) assert tail in ['one-sided', 'two-sided', 'greater', 'less'] assert isinstance(alpha, float), 'alpha must be float.' assert nan_policy in ['listwise', 'pairwise'] # Check if we have multiple between or within factors multiple_between = False multiple_within = False contrast = None if isinstance(between, list): if len(between) > 1: multiple_between = True contrast = 'multiple_between' assert all([b in data.keys() for b in between]) else: between = between[0] if isinstance(within, list): if len(within) > 1: multiple_within = True contrast = 'multiple_within' assert all([w in data.keys() for w in within]) else: within = within[0] if all([multiple_within, multiple_between]): raise ValueError("Multiple between and within factors are", "currently not supported. Please select only one.") # Check the other cases if isinstance(between, str) and within is None: contrast = 'simple_between' assert between in data.keys() if isinstance(within, str) and between is None: contrast = 'simple_within' assert within in data.keys() if isinstance(between, str) and isinstance(within, str): contrast = 'within_between' assert all([between in data.keys(), within in data.keys()]) # Reorganize column order col_order = ['Contrast', 'Time', 'A', 'B', 'mean(A)', 'std(A)', 'mean(B)', 'std(B)', 'Paired', 'Parametric', 'T', 'U-val', 'W-val', 'dof', 'Tail', 'p-unc', 'p-corr', 'p-adjust', 'BF10', effsize] if contrast in ['simple_within', 'simple_between']: # OPTION A: SIMPLE MAIN EFFECTS, WITHIN OR BETWEEN paired = True if contrast == 'simple_within' else False col = within if contrast == 'simple_within' else between # Remove NAN in repeated measurements if contrast == 'simple_within' and data[dv].isnull().to_numpy().any(): # Only if nan_policy == 'listwise'. For pairwise deletion, # missing values will be removed directly in the lower-level # functions (e.g. pg.ttest) if nan_policy == 'listwise': data = remove_rm_na(dv=dv, within=within, subject=subject, data=data) else: # The `remove_rm_na` also aggregate other repeated measures # factor using the mean. Here, we ensure this behavior too. data = data.groupby([subject, within])[dv].mean().reset_index() # Now we check that subjects are present in all conditions # For example, if we have four subjects and 3 conditions, # and if subject 2 have missing data at the third condition, # we still need a row with missing values for this subject. if data.groupby(within)[subject].count().nunique() != 1: raise ValueError("Repeated measures dataframe is not balanced." " `Subjects` must have the same number of " "elements in all conditions, " "even when missing values are present.") # Extract effects grp_col = data.groupby(col, sort=False)[dv] labels = grp_col.groups.keys() # Number and labels of possible comparisons if len(labels) >= 2: combs = list(combinations(labels, 2)) combs = np.array(combs) A = combs[:, 0] B = combs[:, 1] else: raise ValueError('Columns must have at least two unique values.') # Initialize dataframe stats = pd.DataFrame(dtype=np.float64, index=range(len(combs)), columns=col_order) # Force dtype conversion cols_str = ['Contrast', 'Time', 'A', 'B', 'Tail', 'p-adjust', 'BF10'] cols_bool = ['Parametric', 'Paired'] stats[cols_str] = stats[cols_str].astype(object) stats[cols_bool] = stats[cols_bool].astype(bool) # Fill str columns stats.loc[:, 'A'] = A stats.loc[:, 'B'] = B stats.loc[:, 'Contrast'] = col stats.loc[:, 'Tail'] = tail stats.loc[:, 'Paired'] = paired for i in range(stats.shape[0]): col1, col2 = stats.at[i, 'A'], stats.at[i, 'B'] x = grp_col.get_group(col1).to_numpy(dtype=np.float64) y = grp_col.get_group(col2).to_numpy(dtype=np.float64) if parametric: stat_name = 'T' df_ttest = ttest(x, y, paired=paired, tail=tail, correction=correction) stats.at[i, 'BF10'] = df_ttest.at['T-test', 'BF10'] stats.at[i, 'dof'] = df_ttest.at['T-test', 'dof'] else: if paired: stat_name = 'W-val' df_ttest = wilcoxon(x, y, tail=tail) else: stat_name = 'U-val' df_ttest = mwu(x, y, tail=tail) # Compute Hedges / Cohen ef = compute_effsize(x=x, y=y, eftype=effsize, paired=paired) if return_desc: stats.at[i, 'mean(A)'] = np.nanmean(x) stats.at[i, 'mean(B)'] = np.nanmean(y) stats.at[i, 'std(A)'] = np.nanstd(x, ddof=1) stats.at[i, 'std(B)'] = np.nanstd(y, ddof=1) stats.at[i, stat_name] = df_ttest[stat_name].iat[0] stats.at[i, 'p-unc'] = df_ttest['p-val'].iat[0] stats.at[i, effsize] = ef # Multiple comparisons padjust = None if stats['p-unc'].size <= 1 else padjust if padjust is not None: if padjust.lower() != 'none': _, stats['p-corr'] = multicomp(stats['p-unc'].to_numpy(), alpha=alpha, method=padjust) stats['p-adjust'] = padjust else: stats['p-corr'] = None stats['p-adjust'] = None else: # Multiple factors if contrast == 'multiple_between': # B1: BETWEEN1 + BETWEEN2 + BETWEEN1 * BETWEEN2 factors = between fbt = factors fwt = [None, None] paired = False # the interaction is not paired agg = [False, False] # TODO: add a pool SD option, as in JASP and JAMOVI? elif contrast == 'multiple_within': # B2: WITHIN1 + WITHIN2 + WITHIN1 * WITHIN2 factors = within fbt = [None, None] fwt = factors paired = True agg = [True, True] # Calculate marginal means for both factors else: # B3: WITHIN + BETWEEN + WITHIN * BETWEEN factors = [within, between] fbt = [None, between] fwt = [within, None] paired = False agg = [False, True] stats = pd.DataFrame() for i, f in enumerate(factors): # Introduced in Pingouin v0.3.2 if all([agg[i], marginal]): tmp = data.groupby([subject, f], as_index=False, sort=False).mean() else: tmp = data stats = stats.append(pairwise_ttests(dv=dv, between=fbt[i], within=fwt[i], subject=subject, data=tmp, parametric=parametric, marginal=marginal, alpha=alpha, tail=tail, padjust=padjust, effsize=effsize, correction=correction, nan_policy=nan_policy, return_desc=return_desc), ignore_index=True, sort=False) # Then compute the interaction between the factors if interaction: nrows = stats.shape[0] grp_fac1 = data.groupby(factors[0], sort=False)[dv] grp_fac2 = data.groupby(factors[1], sort=False)[dv] grp_both = data.groupby(factors, sort=False)[dv] labels_fac1 = grp_fac1.groups.keys() labels_fac2 = grp_fac2.groups.keys() # comb_fac1 = list(combinations(labels_fac1, 2)) comb_fac2 = list(combinations(labels_fac2, 2)) # Pairwise comparisons combs_list = list(product(labels_fac1, comb_fac2)) ncombs = len(combs_list) # np.array(combs_list) does not work because of tuples # we therefore need to flatten the tupple combs = np.zeros(shape=(ncombs, 3), dtype=object) for i in range(ncombs): combs[i] = _flatten_list(combs_list[i], include_tuple=True) # Append empty rows idxiter = np.arange(nrows, nrows + ncombs) stats = stats.append(pd.DataFrame(columns=stats.columns, index=idxiter), ignore_index=True) # Update other columns stats.loc[idxiter, 'Contrast'] = factors[0] + ' * ' + factors[1] stats.loc[idxiter, 'Time'] = combs[:, 0] stats.loc[idxiter, 'Paired'] = paired stats.loc[idxiter, 'Tail'] = tail stats.loc[idxiter, 'A'] = combs[:, 1] stats.loc[idxiter, 'B'] = combs[:, 2] for i, comb in enumerate(combs): ic = nrows + i # Take into account previous rows fac1, col1, col2 = comb x = grp_both.get_group((fac1, col1)).to_numpy(dtype=np.float64) y = grp_both.get_group((fac1, col2)).to_numpy(dtype=np.float64) ef = compute_effsize(x=x, y=y, eftype=effsize, paired=paired) if parametric: stat_name = 'T' df_ttest = ttest(x, y, paired=paired, tail=tail, correction=correction) stats.at[ic, 'BF10'] = df_ttest.at['T-test', 'BF10'] stats.at[ic, 'dof'] = df_ttest.at['T-test', 'dof'] else: if paired: stat_name = 'W-val' df_ttest = wilcoxon(x, y, tail=tail) else: stat_name = 'U-val' df_ttest = mwu(x, y, tail=tail) # Append to stats if return_desc: stats.at[ic, 'mean(A)'] = np.nanmean(x) stats.at[ic, 'mean(B)'] = np.nanmean(y) stats.at[ic, 'std(A)'] = np.nanstd(x, ddof=1) stats.at[ic, 'std(B)'] = np.nanstd(y, ddof=1) stats.at[ic, stat_name] = df_ttest[stat_name].iat[0] stats.at[ic, 'p-unc'] = df_ttest['p-val'].iat[0] stats.at[ic, effsize] = ef # Multi-comparison columns if padjust is not None and padjust.lower() != 'none': _, pcor = multicomp(stats.loc[idxiter, 'p-unc'].to_numpy(), alpha=alpha, method=padjust) stats.loc[idxiter, 'p-corr'] = pcor stats.loc[idxiter, 'p-adjust'] = padjust # --------------------------------------------------------------------- # Append parametric columns stats.loc[:, 'Parametric'] = parametric # Reorder and drop empty columns stats = stats[np.array(col_order)[np.isin(col_order, stats.columns)]] stats = stats.dropna(how='all', axis=1) # Rename Time columns if (contrast in ['multiple_within', 'multiple_between', 'within_between'] and interaction): stats['Time'].fillna('-', inplace=True) stats.rename(columns={'Time': factors[0]}, inplace=True) return stats