Implement yli.Cox
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GPG Key ID:
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@ -20,7 +20,7 @@ from .descriptives import auto_correlations, auto_descriptives
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from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
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from .graphs import init_fonts, HorizontalEffectPlot
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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 GLM, IntervalCensoredCox, Logit, OLS, OrdinalLogit, PenalisedLogit, Poisson, regress, vif
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from .regress import Cox, GLM, IntervalCensoredCox, Logit, OLS, OrdinalLogit, PenalisedLogit, Poisson, regress, vif
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from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, spearman, ttest_ind, ttest_ind_multiple
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from .survival import kaplanmeier, logrank, turnbull
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from .utils import as_ordinal
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@ -101,7 +101,7 @@ def regress(
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model_class, df, dep, formula,
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*,
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nan_policy='warn',
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exposure=None, family=None,
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exposure=None, family=None, status=None,
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method=None, maxiter=None, start_params=None, tolerance=None,
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reduced=None,
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bool_baselevels=False, exp=None
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@ -123,6 +123,8 @@ def regress(
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:type exposure: str
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:param family: See :class:`yli.GLM`
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:type family: str
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:param status: See :class:`yli.Cox`
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:type status: str
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:param method: See statsmodels *model.fit*
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:param maxiter: See statsmodels *model.fit*
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:param start_params: See statsmodels *model.fit*
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@ -141,15 +143,17 @@ def regress(
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if not any(x.__name__ == 'RegressionModel' for x in model_class.__bases__):
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raise ValueError('model_class must be a RegressionModel')
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df_ref = weakref.ref(df)
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dmatrices, dep_categories = df_to_dmatrices(df, dep, formula, nan_policy)
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# Additional columns to check for NaN - exposure, status, etc.
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additional_columns = []
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# Build function call arguments
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fit_kwargs = {}
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if exposure is not None:
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fit_kwargs['exposure'] = exposure
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additional_columns.append(exposure)
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if family is not None:
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fit_kwargs['family'] = family
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if status is not None:
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additional_columns.append(status)
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if method is not None:
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fit_kwargs['method'] = method
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if maxiter is not None:
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@ -161,6 +165,16 @@ def regress(
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if reduced is not None:
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fit_kwargs['reduced'] = reduced
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# Preprocess data, check for NaN and get design matrices
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df_ref = weakref.ref(df)
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df_clean, dmatrices, dep_categories = df_to_dmatrices(df, dep, formula, nan_policy, additional_columns)
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# Add function call arguments for supplemental columns - exposure, status, etc.
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if exposure is not None:
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fit_kwargs['exposure'] = df_clean[exposure]
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if status is not None:
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fit_kwargs['status'] = df_clean[status]
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# Fit model
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result = model_class.fit(dmatrices[0], dmatrices[1], **fit_kwargs)
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@ -197,7 +211,7 @@ def regress(
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result.terms[raw_name].categories[term] = cutoff_term
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else:
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# Parse if required
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factor, column, contrast = parse_patsy_term(formula, df, raw_name)
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factor, column, contrast = parse_patsy_term(formula, df_clean, raw_name)
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if contrast is not None:
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# Categorical term
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@ -208,7 +222,7 @@ def regress(
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else:
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# Add a new categorical term if not exists
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if column not in result.terms:
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ref_category = formula_factor_ref_category(formula, df, factor)
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ref_category = formula_factor_ref_category(formula, df_clean, factor)
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result.terms[column] = CategoricalTerm({}, ref_category)
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result.terms[column].categories[contrast] = raw_term
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@ -218,10 +232,13 @@ def regress(
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return result
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def df_to_dmatrices(df, dep, formula, nan_policy):
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def df_to_dmatrices(df, dep, formula, nan_policy, additional_columns=[]):
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# Check for/clean NaNs in input columns
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columns = cols_for_formula(dep, df) + cols_for_formula(formula, df)
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if additional_columns:
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columns += additional_columns
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df = df[columns]
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df = check_nan(df, nan_policy)
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@ -237,7 +254,7 @@ def df_to_dmatrices(df, dep, formula, nan_policy):
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# Construct design matrix from formula
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dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
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return dmatrices, dep_categories
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return df, dmatrices, dep_categories
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class RegressionModel:
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# TODO: Documentation
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@ -630,19 +647,55 @@ class CategoricalTerm:
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#: Name of the reference category (*str*)
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self.ref_category = ref_category
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def raw_terms_from_statsmodels_result(raw_result):
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def raw_terms_from_statsmodels_result(raw_result, *, wrapped=True):
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if wrapped:
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zipped_iter = zip(raw_result.model.exog_names, raw_result.params.values, raw_result.conf_int(config.alpha).values, raw_result.pvalues.values)
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else:
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zipped_iter = zip(raw_result.model.exog_names, raw_result.params, raw_result.conf_int(config.alpha), raw_result.pvalues)
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return {
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('(Intercept)' if raw_name == 'Intercept' else raw_name): SingleTerm(
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raw_name=raw_name,
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beta=Estimate(raw_param, raw_ci[0], raw_ci[1]),
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pvalue=raw_p
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)
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for raw_name, raw_param, raw_ci, raw_p in zip(raw_result.model.exog_names, raw_result.params.values, raw_result.conf_int(config.alpha).values, raw_result.pvalues.values)
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for raw_name, raw_param, raw_ci, raw_p in zipped_iter
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}
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# ------------------------
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# Concrete implementations
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class Cox(RegressionModel):
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# TODO: Documentation
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@property
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def model_long_name(self):
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return 'Cox Regression'
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@property
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def model_short_name(self):
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return 'Cox'
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@classmethod
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def fit(cls, data_dep, data_ind, *, status=None):
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# Drop explicit intercept term
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if 'Intercept' in data_ind:
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del data_ind['Intercept']
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result = cls()
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result.exp = True
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result.cov_type = 'nonrobust'
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# Perform regression
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raw_result = sm.PHReg(endog=data_dep, exog=data_ind, status=status, missing='raise').fit(disp=False)
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result.ll_model = raw_result.llf
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result.terms = raw_terms_from_statsmodels_result(raw_result, wrapped=False)
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result.vcov = raw_result.cov_params()
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return result
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class GLM(RegressionModel):
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# TODO: Documentation
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@ -1093,7 +1146,7 @@ class OrdinalLogit(RegressionModel):
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dep = self.dep
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# Precompute design matrices
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dmatrices, dep_categories = df_to_dmatrices(df, dep, self.formula, 'omit')
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_, dmatrices, dep_categories = df_to_dmatrices(df, dep, self.formula, 'omit')
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s_dep = dmatrices[0][dmatrices[0].columns[0]] # df[dep] as series - must get this from dmatrices to account for as_numeric
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dmatrix_right = dmatrices[1]
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dmatrix_right.reset_index(drop=True, inplace=True) # Otherwise this confuses matrix multiplication
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