Implement yli.spearman

tests/test_correlation.py

@@ -49,3 +49,18 @@ def test_pearsonr_ol11_16():
 	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)
+
+def test_spearman_ol11_17():
+	"""Compare yli.spearman for Ott & Longnecker (2016) example 11.17"""
+	
+	df = pd.DataFrame({
+		'Profit': [2.5, 6.2, 3.1, 4.6, 7.3, 4.5, 6.1, 11.6, 10.0, 14.2, 16.1, 19.5],
+		'Quality': [50, 57, 61, 68, 77, 80, 82, 85, 89, 91, 95, 99]
+	})
+	
+	result = yli.spearman(df, 'Profit', 'Quality')
+	
+	assert result.statistic.point == approx(0.874, abs=0.001)
+	
+	expected_summary = 'ρ (95% CI) = 0.87 (0.60–0.96); p < 0.001*'  # NB: The confidence intervals are unvalidated
+	assert result.summary() == expected_summary

yli/__init__.py

@@ -20,7 +20,7 @@ from .descriptives import auto_descriptives
 from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
 from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
 from .regress import OrdinalLogit, PenalisedLogit, logit_then_regress, regress, vif
-from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, ttest_ind
+from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, spearman, ttest_ind
 
 def reload_me():
 	import importlib

yli/sig_tests.py

@@ -23,7 +23,7 @@ import functools
 import warnings
 
 from .config import config
-from .utils import Estimate, Interval, PValueStyle, as_2groups, check_nan, convert_pandas_nullable, fmt_p
+from .utils import Estimate, Interval, PValueStyle, as_2groups, as_numeric, check_nan, convert_pandas_nullable, fmt_p
 
 # ----------------
 # Student's t test
@@ -718,6 +718,46 @@ def pearsonr(df, dep, ind, *, nan_policy='warn'):
 	
 	return PearsonRResult(statistic=Estimate(result.statistic, ci.low, ci.high), pvalue=result.pvalue)
 
+# --------------------
+# Spearman correlation
+
+class SpearmanResult:
+	def __init__(self, statistic, pvalue):
+		self.statistic = statistic
+		self.pvalue = pvalue
+	
+	def __repr__(self):
+		if config.repr_is_summary:
+			return self.summary()
+		return super().__repr__()
+	
+	def _repr_html_(self):
+		return '<i>ρ</i> ({:g}% CI) = {}; <i>p</i> {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, PValueStyle.RELATION | PValueStyle.HTML))
+	
+	def summary(self):
+		"""
+		Return a stringified summary of the Spearman correlation
+		
+		:rtype: str
+		"""
+		
+		return 'ρ ({:g}% CI) = {}; p {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, PValueStyle.RELATION))
+
+def spearman(df, dep, ind, *, nan_policy='warn'):
+	# Check for/clean NaNs
+	df = check_nan(df[[ind, dep]], nan_policy)
+	
+	# Ensure numeric, factorising categorical variables as required
+	ind, _ = as_numeric(df[ind])
+	dep, _ = as_numeric(df[dep])
+	
+	# Compute Spearman's rho
+	result = stats.spearmanr(ind, dep)
+	
+	# Compute confidence interval
+	ci = stats._stats_py._pearsonr_fisher_ci(result.correlation, len(dep), 1 - config.alpha, 'two-sided')
+	return SpearmanResult(statistic=Estimate(result.correlation, ci.low, ci.high), pvalue=result.pvalue)
+
 # ----------------------------
 # Automatic selection of tests