effect sizes

README.rst

@@ -157,7 +157,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio
    :widths: auto
 
    ======  =====  =============  =======  =============  =========  ======  =======
-        T    dof  alternative      p_val  CI95             cohen-d    BF10    power
+        T    dof  alternative      p_val  CI95             cohen_d    BF10    power
    ======  =====  =============  =======  =============  =========  ======  =======
    -3.401     58  two-sided        0.001  [-1.68 -0.43]      0.878  26.155    0.917
    ======  =====  =============  =======  =============  =========  ======  =======

docs/index.rst

@@ -135,7 +135,7 @@ Quick start
    :widths: auto
 
    ======  =====  =============  =======  =============  =========  ======  =======
-        T    dof  alternative      p_val  CI95             cohen-d    BF10    power
+        T    dof  alternative      p_val  CI95             cohen_d    BF10    power
    ======  =====  =============  =======  =============  =========  ======  =======
    -3.401     58  two-sided        0.001  [-1.68 -0.43]      0.878  26.155    0.917
    ======  =====  =============  =======  =============  =========  ======  =======

src/pingouin/effsize.py

@@ -501,8 +501,8 @@ def convert_effsize(ef, input_type, output_type, nx=None, ny=None):
         * ``'cohen'``: Unbiased Cohen d
         * ``'hedges'``: Hedges g
         * ``'pointbiserialr'``: Point-biserial correlation
-        * ``'eta-square'``: Eta-square
-        * ``'odds-ratio'``: Odds ratio
+        * ``'eta_square'``: Eta-square
+        * ``'odds_ratio'``: Odds ratio
         * ``'AUC'``: Area Under the Curve
         * ``'none'``: pass-through (return ``ef``)
 
@@ -570,7 +570,7 @@ def convert_effsize(ef, input_type, output_type, nx=None, ny=None):
 
     >>> import pingouin as pg
     >>> d = .45
-    >>> eta = pg.convert_effsize(d, 'cohen', 'eta-square')
+    >>> eta = pg.convert_effsize(d, 'cohen', 'eta_square')
     >>> print(eta)
     0.048185603807257595
 
@@ -630,10 +630,10 @@ def convert_effsize(ef, input_type, output_type, nx=None, ny=None):
         else:
             a = 4
         return d / np.sqrt(d**2 + a)
-    elif ot == "eta-square":
+    elif ot == "eta_square":
         # Cohen 1988
         return (d / 2) ** 2 / (1 + (d / 2) ** 2)
-    elif ot == "odds-ratio":
+    elif ot == "odds_ratio":
         # Borenstein et al. 2009
         return np.exp(d * np.pi / np.sqrt(3))
     elif ot == "r":
@@ -670,8 +670,8 @@ def compute_effsize(x, y, paired=False, eftype="cohen"):
         * ``'hedges'``: Hedges g
         * ``'r'``: Pearson correlation coefficient
         * ``'pointbiserialr'``: Point-biserial correlation
-        * ``'eta-square'``: Eta-square
-        * ``'odds-ratio'``: Odds ratio
+        * ``'eta_square'``: Eta-square
+        * ``'odds_ratio'``: Odds ratio
         * ``'AUC'``: Area Under the Curve
         * ``'CLES'``: Common Language Effect Size
 

src/pingouin/pairwise.py

@@ -100,8 +100,8 @@ def pairwise_tests(
         * ``'cohen'``: Unbiased Cohen d
         * ``'hedges'``: Hedges g
         * ``'r'``: Pearson correlation coefficient
-        * ``'eta-square'``: Eta-square
-        * ``'odds-ratio'``: Odds ratio
+        * ``'eta_square'``: Eta-square
+        * ``'odds_ratio'``: Odds ratio
         * ``'AUC'``: Area Under the Curve
         * ``'CLES'``: Common Language Effect Size
     correction : string or boolean
@@ -787,8 +787,8 @@ def pairwise_tukey(data=None, dv=None, between=None, effsize="hedges"):
         * ``'cohen'``: Unbiased Cohen d
         * ``'hedges'``: Hedges g
         * ``'r'``: Pearson correlation coefficient
-        * ``'eta-square'``: Eta-square
-        * ``'odds-ratio'``: Odds ratio
+        * ``'eta_square'``: Eta-square
+        * ``'odds_ratio'``: Odds ratio
         * ``'AUC'``: Area Under the Curve
         * ``'CLES'``: Common Language Effect Size
 
@@ -951,8 +951,8 @@ def pairwise_gameshowell(data=None, dv=None, between=None, effsize="hedges"):
         * ``'cohen'``: Unbiased Cohen d
         * ``'hedges'``: Hedges g
         * ``'r'``: Pearson correlation coefficient
-        * ``'eta-square'``: Eta-square
-        * ``'odds-ratio'``: Odds ratio
+        * ``'eta_square'``: Eta-square
+        * ``'odds_ratio'``: Odds ratio
         * ``'AUC'``: Area Under the Curve
         * ``'CLES'``: Common Language Effect Size
 

src/pingouin/parametric.py

@@ -62,7 +62,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70
         * ``'alternative'``: alternative of the test
         * ``'p_val'``: p-value
         * ``'CI95'``: confidence intervals of the difference in means
-        * ``'cohen-d'``: Cohen d effect size
+        * ``'cohen_d'``: Cohen d effect size
         * ``'BF10'``: Bayes Factor of the alternative hypothesis
         * ``'power'``: achieved power of the test ( = 1 - type II error)
 
@@ -143,21 +143,21 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70
     >>> from pingouin import ttest
     >>> x = [5.5, 2.4, 6.8, 9.6, 4.2]
     >>> ttest(x, 4).round(2)
-              T  dof alternative  p_val          CI95  cohen-d   BF10  power
+              T  dof alternative  p_val          CI95  cohen_d   BF10  power
     T_test  1.4    4   two-sided   0.23  [2.32, 9.08]     0.62  0.766   0.19
 
     2. One sided paired T-test.
 
     >>> pre = [5.5, 2.4, 6.8, 9.6, 4.2]
     >>> post = [6.4, 3.4, 6.4, 11., 4.8]
     >>> ttest(pre, post, paired=True, alternative='less').round(2)
-               T  dof alternative  p_val           CI95  cohen-d   BF10  power
+               T  dof alternative  p_val           CI95  cohen_d   BF10  power
     T_test -2.31    4        less   0.04  [-inf, -0.05]     0.25  3.122   0.12
 
     Now testing the opposite alternative hypothesis
 
     >>> ttest(pre, post, paired=True, alternative='greater').round(2)
-               T  dof alternative  p_val          CI95  cohen-d  BF10  power
+               T  dof alternative  p_val          CI95  cohen_d  BF10  power
     T_test -2.31    4     greater   0.96  [-1.35, inf]     0.25  0.32   0.02
 
     3. Paired T-test with missing values.
@@ -166,7 +166,7 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70
     >>> pre = [5.5, 2.4, np.nan, 9.6, 4.2]
     >>> post = [6.4, 3.4, 6.4, 11., 4.8]
     >>> ttest(pre, post, paired=True).round(3)
-                T  dof alternative  p_val           CI95  cohen-d   BF10  power
+                T  dof alternative  p_val           CI95  cohen_d   BF10  power
     T_test -5.902    3   two-sided   0.01  [-1.5, -0.45]    0.306  7.169  0.073
 
     Compare with SciPy
@@ -181,21 +181,21 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70
     >>> x = np.random.normal(loc=7, size=20)
     >>> y = np.random.normal(loc=4, size=20)
     >>> ttest(x, y)
-                   T  dof alternative         p_val          CI95   cohen-d       BF10  power
+                   T  dof alternative         p_val          CI95   cohen_d       BF10  power
     T_test  9.106452   38   two-sided  4.306971e-11  [2.64, 4.15]  2.879713  1.366e+08    1.0
 
     5. Independent two-sample T-test with unequal sample size. A Welch's T-test is used.
 
     >>> np.random.seed(123)
     >>> y = np.random.normal(loc=6.5, size=15)
     >>> ttest(x, y)
-                   T        dof alternative     p_val           CI95   cohen-d   BF10     power
+                   T        dof alternative     p_val           CI95   cohen_d   BF10     power
     T_test  1.996537  31.567592   two-sided  0.054561  [-0.02, 1.65]  0.673518  1.469  0.481867
 
     6. However, the Welch's correction can be disabled:
 
     >>> ttest(x, y, correction=False)
-                   T  dof alternative     p_val           CI95   cohen-d   BF10     power
+                   T  dof alternative     p_val           CI95   cohen_d   BF10     power
     T_test  1.971859   33   two-sided  0.057056  [-0.03, 1.66]  0.673518  1.418  0.481867
 
     Compare with SciPy
@@ -320,14 +320,14 @@ def ttest(x, y, paired=False, alternative="two-sided", correction="auto", r=0.70
         "T": tval,
         "p_val": pval,
         "alternative": alternative,
-        "cohen-d": abs(d),
+        "cohen_d": abs(d),
         ci_name: [ci],
         "power": power,
         "BF10": bf,
     }
 
     # Convert to dataframe
-    col_order = ["T", "dof", "alternative", "p_val", ci_name, "cohen-d", "BF10", "power"]
+    col_order = ["T", "dof", "alternative", "p_val", ci_name, "cohen_d", "BF10", "power"]
     stats = pd.DataFrame(stats, columns=col_order, index=["T_test"])
     return _postprocess_dataframe(stats)
 

src/pingouin/utils.py

@@ -327,8 +327,8 @@ def _check_eftype(eftype):
         "cohen",
         "r",
         "pointbiserialr",
-        "eta-square",
-        "odds-ratio",
+        "eta_square",
+        "odds_ratio",
         "auc",
         "cles",
     ]:

tests/test_effsize.py

@@ -234,8 +234,8 @@ def test_convert_effsize(self):
         assert round(cef(d, "cohen", "pointbiserialr"), 4) == 0.1961
         cef(d, "cohen", "pointbiserialr", nx=10, ny=12)  # When nx and ny are specified
         assert np.allclose(cef(1.002549, "cohen", "pointbiserialr"), 0.4481248)  # R
-        assert round(cef(d, "cohen", "eta-square"), 4) == 0.0385
-        assert round(cef(d, "cohen", "odds-ratio"), 4) == 2.0658
+        assert round(cef(d, "cohen", "eta_square"), 4) == 0.0385
+        assert round(cef(d, "cohen", "odds_ratio"), 4) == 2.0658
         cef(d, "cohen", "hedges", nx=10, ny=10)
         cef(d, "cohen", "pointbiserialr")
         cef(d, "cohen", "hedges")
@@ -245,8 +245,8 @@ def test_convert_effsize(self):
         assert cef(rpb, "pointbiserialr", "none") == rpb
         assert round(cef(rpb, "pointbiserialr", "cohen"), 4) == 1.7107
         assert np.allclose(cef(0.4481248, "pointbiserialr", "cohen"), 1.002549)
-        assert round(cef(rpb, "pointbiserialr", "eta-square"), 4) == 0.4225
-        assert round(cef(rpb, "pointbiserialr", "odds-ratio"), 4) == 22.2606
+        assert round(cef(rpb, "pointbiserialr", "eta_square"), 4) == 0.4225
+        assert round(cef(rpb, "pointbiserialr", "odds_ratio"), 4) == 22.2606
         # Using actual values
         np.random.seed(42)
         x1, y1 = np.random.multivariate_normal(mean=[1, 2], cov=[[1, 0.5], [0.5, 1]], size=100).T
@@ -270,15 +270,15 @@ def test_convert_effsize(self):
         with pytest.raises(ValueError):
             cef(d, "coucou", "hibou")
         with pytest.raises(ValueError):
-            cef(d, "AUC", "eta-square")
+            cef(d, "AUC", "eta_square")
 
     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="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="pointbiserialr", paired=False)
         compute_effsize(x=x, y=y, eftype="none", paired=False)

tests/test_parametric.py

@@ -120,7 +120,7 @@ def test_ttest(self):
         tt = ttest(a, a, paired=True)
         assert str(tt.loc["T_test", "T"]) == str(np.nan)
         assert str(tt.loc["T_test", "p_val"]) == str(np.nan)
-        assert tt.loc["T_test", "cohen-d"] == 0.0
+        assert tt.loc["T_test", "cohen_d"] == 0.0
         assert tt.loc["T_test", "BF10"] == str(np.nan)
 
         # 4) Independent two-samples, equal variance (no correction)