Implement yli.cox_interval_censored
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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, logrank, turnbull
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from .survival import cox_interval_censored, kaplanmeier, logrank, turnbull
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from .utils import as_ordinal
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def reload_me():
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113
yli/regress.py
113
yli/regress.py
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@ -19,7 +19,7 @@ import pandas as pd
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import patsy
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from scipy import stats
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from scipy.special import expit
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import statsmodels, statsmodels.miscmodels.ordinal_model
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import statsmodels, statsmodels.base.model, statsmodels.miscmodels.ordinal_model
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import statsmodels.api as sm
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from statsmodels.iolib.table import SimpleTable
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from statsmodels.stats.outliers_influence import variance_inflation_factor
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@ -513,7 +513,8 @@ class RegressionResult:
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right_col.append(('No. Observations:', format(self.nobs, '.0f')))
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if self.nevents:
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right_col.append(('No. Events:', format(self.nevents, '.0f')))
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right_col.append(('Df. Model:', format(self.dof_model, '.0f')))
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if self.dof_model:
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right_col.append(('Df. Model:', format(self.dof_model, '.0f')))
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if self.dof_resid:
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right_col.append(('Df. Residuals:', format(self.dof_resid, '.0f')))
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if self.rsquared:
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@ -943,7 +944,7 @@ def regress(
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if model_class is sm.OLS:
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method_name = 'Least Squares'
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elif model_class is sm.PHReg:
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method_name = 'MPLE'
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method_name = 'MLE'
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else:
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# Parse raw regression results to get fit method
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# Avoid this in general as it can be expensive to summarise all the post hoc tests, etc.
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@ -1013,6 +1014,59 @@ def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kw
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# Perform desired regression
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return regress(model_class, df, dep, formula, start_params=logit_params, **kwargs)
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class _Dummy: pass
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def _wald_test(param_names, params, formula, vcov, df):
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# Hack! Piggyback off statsmodels to compute a Wald test
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lmr = statsmodels.base.model.LikelihoodModelResults(model=None, params=None)
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lmr.model = _Dummy()
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lmr.model.data = _Dummy()
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lmr.model.data.cov_names = param_names
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lmr.params = params
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lmr.df_resid = df
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return lmr.wald_test(formula, cov_p=vcov, use_f=False, scalar=True)
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class BrantResult:
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"""
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Result of a Brant test for ordinal regression
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See :meth:`RegressionResult.brant`.
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"""
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def __init__(self, tests):
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#: Results for Brant test on each coefficient (*Dict[str, ChiSquaredResult]*)
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self.tests = tests
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def __repr__(self):
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if config.repr_is_summary:
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return self.summary()
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return super().__repr__()
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def _repr_html_(self):
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out = '<table><caption>Brant Test Results</caption><thead><tr><th></th><th style="text-align:center"><i>χ</i><sup>2</sup></th><th style="text-align:center">df</th><th style="text-align:center"><i>p</i></th></thead><tbody>'
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for raw_name, test in self.tests.items():
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out += '<tr><th>{}</th><td>{:.2f}</td><td>{:.0f}</td><td style="text-align:left">{}</td></tr>'.format(raw_name, test.statistic, test.dof, fmt_p(test.pvalue, PValueStyle.TABULAR | PValueStyle.HTML))
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out += '</tbody></table>'
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return out
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def summary(self):
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"""
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Return a stringified summary of the *χ*:sup:`2` test
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:rtype: str
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"""
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table = pd.DataFrame([
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['{:.2f}'.format(test.statistic), '{:.0f}'.format(test.dof), fmt_p(test.pvalue, PValueStyle.TABULAR)]
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for test in self.tests.values()
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], index=self.tests.keys(), columns=['χ² ', 'df', 'p '])
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return str(table)
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# -----------------------------
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# Penalised logistic regression
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@ -1155,56 +1209,3 @@ class OrdinalLogit(statsmodels.miscmodels.ordinal_model.OrderedModel):
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def transform_reverse_threshold_params(self, params):
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return params[:-1]
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class _Dummy: pass
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def _wald_test(param_names, params, formula, vcov, df):
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# Hack! Piggyback off statsmodels to compute a Wald test
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lmr = statsmodels.base.model.LikelihoodModelResults(model=None, params=None)
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lmr.model = _Dummy()
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lmr.model.data = _Dummy()
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lmr.model.data.cov_names = param_names
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lmr.params = params
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lmr.df_resid = df
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return lmr.wald_test(formula, cov_p=vcov, use_f=False, scalar=True)
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class BrantResult:
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"""
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Result of a Brant test for ordinal regression
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See :meth:`RegressionResult.brant`.
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"""
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def __init__(self, tests):
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#: Results for Brant test on each coefficient (*Dict[str, ChiSquaredResult]*)
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self.tests = tests
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def __repr__(self):
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if config.repr_is_summary:
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return self.summary()
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return super().__repr__()
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def _repr_html_(self):
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out = '<table><caption>Brant Test Results</caption><thead><tr><th></th><th style="text-align:center"><i>χ</i><sup>2</sup></th><th style="text-align:center">df</th><th style="text-align:center"><i>p</i></th></thead><tbody>'
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for raw_name, test in self.tests.items():
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out += '<tr><th>{}</th><td>{:.2f}</td><td>{:.0f}</td><td style="text-align:left">{}</td></tr>'.format(raw_name, test.statistic, test.dof, fmt_p(test.pvalue, PValueStyle.TABULAR | PValueStyle.HTML))
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out += '</tbody></table>'
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return out
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def summary(self):
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"""
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Return a stringified summary of the *χ*:sup:`2` test
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:rtype: str
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"""
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table = pd.DataFrame([
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['{:.2f}'.format(test.statistic), '{:.0f}'.format(test.dof), fmt_p(test.pvalue, PValueStyle.TABULAR)]
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for test in self.tests.values()
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], index=self.tests.keys(), columns=['χ² ', 'df', 'p '])
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return str(table)
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@ -14,12 +14,17 @@
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import numpy as np
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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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from .regress import RegressionResult, SingleTerm
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from .utils import Estimate, check_nan, cols_for_formula, convert_pandas_nullable
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from datetime import datetime
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import weakref
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def kaplanmeier(df, time, status, by=None, *, ci=True, transform_x=None, transform_y=None, nan_policy='warn'):
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"""
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@ -271,3 +276,91 @@ def logrank(df, time, status, by, nan_policy='warn'):
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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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# --------------------------------
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# Interval-censored Cox regression
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def cox_interval_censored(
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df, time_left, time_right, formula, *,
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bootstrap_samples=100,
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nan_policy='warn',
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bool_baselevels=False, exp=True,
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):
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# TODO: Documentation
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df_ref = weakref.ref(df)
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# Check for/clean NaNs in input columns
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columns = [time_left, time_right] + cols_for_formula(formula, df)
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df = df[columns]
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df = check_nan(df, nan_policy)
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# FIXME: Ensure numeric type for dependent variable
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#df[dep], dep_categories = as_numeric(df[dep])
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if df[time_left].dtype != 'float64' or df[time_right].dtype != 'float64':
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raise NotImplementedError('Time dtypes must be float64')
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# Convert pandas nullable types for independent variables
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df = convert_pandas_nullable(df)
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# ---------
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# Fit model
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# lifelines.CoxPHFitter doesn't do confidence intervals so we use R
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import rpy2.robjects as ro
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import rpy2.robjects.packages
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import rpy2.robjects.pandas2ri
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# Convert bool to int otherwise rpy2 chokes
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df = df.replace({False: 0, True: 1})
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# Import icenReg
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ro.packages.importr('icenReg')
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with ro.conversion.localconverter(ro.default_converter + ro.pandas2ri.converter):
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with ro.local_context() as lc:
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# Convert DataFrame to R
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lc['df'] = df
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# Transfer other parameters to R
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lc['formula_'] = 'Surv({}, {}, type="interval2") ~ {}'.format(time_left, time_right, formula)
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lc['bootstrap_samples'] = bootstrap_samples
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# FIXME: Seed bootstrap RNG?
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# Fit the model
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ro.r('model <- ic_sp(as.formula(formula_), data=df, bs_samples=bootstrap_samples)')
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model = ro.r('model')
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# Hard to access attributes through rpy2
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term_parameters = ro.r('model$coef')
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term_names = ro.r('names(model$coef)')
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term_cis = ro.r('confint(model)')
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cov_matrix = ro.r('model$var')
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llf = ro.r('model$llk')[0]
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# TODO: Handle categorical terms?
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terms = {}
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for i in range(len(term_parameters)):
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# These values not directly exposed so we must calculate them
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se = np.sqrt(cov_matrix[i, i])
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pvalue = 2 * stats.norm(loc=0, scale=se).cdf(-np.abs(term_parameters[i]))
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term = SingleTerm(term_names[i], Estimate(term_parameters[i], term_cis[i][0], term_cis[i][1]), pvalue)
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terms[term_names[i]] = term
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result = RegressionResult(
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None, df_ref, '({}, {}]'.format(time_left, time_right), formula, nan_policy, None, None,
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model,
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'Interval-Censored Cox Regression', 'CoxIC', 'MLE',
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len(df), None, None, datetime.now(), 'Bootstrap',
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terms,
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llf, None,
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None, None, None,
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[],
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exp
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)
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return result
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