Implement yli.logrank
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@ -22,7 +22,7 @@ from .graphs import init_fonts
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from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
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from .regress import OrdinalLogit, PenalisedLogit, logit_then_regress, regress, vif
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from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, spearman, ttest_ind
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from .survival import kaplanmeier
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from .survival import kaplanmeier, logrank
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from .utils import as_ordinal
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def reload_me():
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@ -513,7 +513,7 @@ class ChiSquaredResult:
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:rtype: str
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"""
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return 'χ²({}) = {:.2f}; p {}'.format(self.ct, self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION))
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return 'χ²({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION))
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class PearsonChiSquaredResult(ChiSquaredResult):
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"""
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@ -18,6 +18,7 @@ from scipy import stats
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import statsmodels.api as sm
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from .config import config
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from .sig_tests import ChiSquaredResult
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from .utils import check_nan
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def kaplanmeier(df, time, status, by=None, ci=True, nan_policy='warn'):
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@ -102,3 +103,16 @@ def plot_survfunc(ax, time, status, ci):
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ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
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return handle
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def logrank(df, time, status, by, nan_policy='warn'):
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# TODO: Documentation
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# Check for/clean NaNs
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df = check_nan(df[[time, status, by]], nan_policy)
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if df[time].dtype == '<m8[ns]':
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df[time] = df[time].dt.total_seconds()
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statistic, pvalue = sm.duration.survdiff(df[time], df[status], df[by])
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return ChiSquaredResult(statistic=statistic, dof=1, pvalue=pvalue)
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