Implement yli.logrank

yli/__init__.py

@@ -22,7 +22,7 @@ from .graphs import init_fonts
 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, spearman, ttest_ind
-from .survival import kaplanmeier
+from .survival import kaplanmeier, logrank
 from .utils import as_ordinal
 
 def reload_me():

yli/sig_tests.py

@@ -513,7 +513,7 @@ class ChiSquaredResult:
 		:rtype: str
 		"""
 		
-		return 'χ²({}) = {:.2f}; p {}'.format(self.ct, self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION))
+		return 'χ²({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION))
 
 class PearsonChiSquaredResult(ChiSquaredResult):
 	"""

yli/survival.py

@@ -18,6 +18,7 @@ from scipy import stats
 import statsmodels.api as sm
 
 from .config import config
+from .sig_tests import ChiSquaredResult
 from .utils import check_nan
 
 def kaplanmeier(df, time, status, by=None, ci=True, nan_policy='warn'):
@@ -102,3 +103,16 @@ def plot_survfunc(ax, time, status, ci):
 		ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
 	
 	return handle
+
+def logrank(df, time, status, by, nan_policy='warn'):
+	# TODO: Documentation
+	
+	# Check for/clean NaNs
+	df = check_nan(df[[time, status, by]], nan_policy)
+	
+	if df[time].dtype == '<m8[ns]':
+		df[time] = df[time].dt.total_seconds()
+	
+	statistic, pvalue = sm.duration.survdiff(df[time], df[status], df[by])
+	
+	return ChiSquaredResult(statistic=statistic, dof=1, pvalue=pvalue)