Add test and example for yli.pearsonr

tests/test_correlation.py

@@ -0,0 +1,51 @@
+#   scipy-yli: Helpful SciPy utilities and recipes
+#   Copyright © 2022  Lee Yingtong Li (RunasSudo)
+#
+#   This program is free software: you can redistribute it and/or modify
+#   it under the terms of the GNU Affero General Public License as published by
+#   the Free Software Foundation, either version 3 of the License, or
+#   (at your option) any later version.
+#
+#   This program is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU Affero General Public License for more details.
+#
+#   You should have received a copy of the GNU Affero General Public License
+#   along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from pytest import approx
+
+import pandas as pd
+
+import yli
+
+def test_pearsonr_ol11_15():
+	"""Compare yli.pearsonr for Ott & Longnecker (2016) example 11.15"""
+	
+	df = pd.DataFrame({
+		'y': [41, 39, 47, 51, 43, 40, 57, 46, 50, 59, 61, 52],
+		'x': [24, 30, 33, 35, 36, 36, 37, 37, 38, 40, 43, 49]
+	})
+	
+	result = yli.pearsonr(df, 'y', 'x')
+	
+	assert result.statistic.point == approx(0.646, abs=0.001)
+	assert result.pvalue == approx(0.0234, abs=0.0001)
+	
+	expected_summary = 'r (95% CI) = 0.65 (0.11–0.89); p = 0.02*'
+	assert result.summary() == expected_summary
+
+def test_pearsonr_ol11_16():
+	"""Compare yli.pearsonr for Ott & Longnecker (2016) example 11.16"""
+	
+	df = pd.DataFrame({
+		'Eggs': [27, 32, 39, 48, 59, 67, 71, 65, 73, 67, 78, 72, 81, 74, 83, 75, 84, 77, 83, 76, 82, 75, 78, 77, 75, 73, 71, 70, 68, 65],
+		'Weight': [2.1, 2.3, 2.4, 2.5, 2.9, 3.1, 3.2, 3.3, 3.4, 3.4, 3.5, 3.5, 3.5, 3.6, 3.6, 3.6, 3.6, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0, 4.3, 4.4, 4.7, 4.8, 4.9, 5.0, 5.1]
+	})
+	
+	result = yli.pearsonr(df, 'Eggs', 'Weight')
+	
+	assert result.statistic.point == approx(0.606, abs=0.001)
+	assert result.statistic.ci_lower == approx(0.314, abs=0.001)
+	assert result.statistic.ci_upper == approx(0.793, abs=0.001)

yli/sig_tests.py

@@ -543,6 +543,22 @@ def pearsonr(df, dep, ind, *, nan_policy='warn'):
 	:type nan_policy: str
 	
 	:rtype: :class:`yli.sig_tests.PearsonRResult`
+	
+	**Example:**
+	
+	.. code-block::
+		
+		df = pd.DataFrame({
+			'y': [41, 39, 47, 51, 43, 40, 57, 46, 50, 59, 61, 52],
+			'x': [24, 30, 33, 35, 36, 36, 37, 37, 38, 40, 43, 49]
+		})
+		yli.pearsonr(df, 'y', 'x')
+	
+	.. code-block:: text
+		
+		r (95% CI) = 0.65 (0.11–0.89); p = 0.02*
+	
+	The output states that the value of the Pearson correlation coefficient is 0.65, with 95% confidence interval 0.11–0.89, and the test is significant with *p* value 0.02.
 	"""
 	
 	# Check for/clean NaNs