Implement bootstrap_regress
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@ -15,6 +15,7 @@
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from .bayes_factors import bayesfactor_afbf
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from .bootstrap import bootstrap_regress
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from .config import config
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from .descriptives import auto_descriptives
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from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
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@ -0,0 +1,103 @@
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# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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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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import pandas as pd
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import patsy
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from tqdm import tqdm
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from datetime import datetime
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from .regress import CategoricalTerm, RegressionResult, SingleTerm, regress
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from .utils import Estimate, check_nan, cols_for_formula, convert_pandas_nullable, fmt_p, formula_factor_ref_category, parse_patsy_term
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def bootstrap_regress(
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model_class, df, dep, formula, *,
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nan_policy='warn',
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samples=1000,
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**kwargs
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):
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"""
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Fit a statsmodels regression model, using bootstrapping to compute confidence intervals and *p* values
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:param model_class: See :func:`yli.regress`
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:param df: See :func:`yli.regress`
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:param dep: See :func:`yli.regress`
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:param formula: See :func:`yli.regress`
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:param nan_policy: See :func:`yli.regress`
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:param samples: Number of bootstrap samples to draw
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:type samples: int
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:param kwargs: See :func:`yli.regress`
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:rtype: :class:`yli.regress.RegressionResult`
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"""
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# Check for/clean NaNs
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# Following this, we pass nan_policy='raise' to assert no NaNs remaining
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df = df[[dep] + cols_for_formula(formula, df)]
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df = check_nan(df, nan_policy)
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# Ensure numeric type for dependent variable
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if df[dep].dtype != 'float64':
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df[dep] = df[dep].astype('float64')
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# Convert pandas nullable types for independent variables as this breaks statsmodels
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df = convert_pandas_nullable(df)
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# Precompute design matrices
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dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
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# Fit full model
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full_model = regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=dmatrices, **kwargs)
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# Cache reference categories
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ref_categories = {term_name: term.ref_category for term_name, term in full_model.terms.items() if isinstance(term, CategoricalTerm)}
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# Draw bootstrap samples and regress
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bootstrap_results = []
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for i in tqdm(range(samples)):
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#bootstrap_sample = df.sample(len(df), replace=True)
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#bootstrap_results.append(regress(model_class, bootstrap_sample, dep, formula, nan_policy='raise', _dmatrices=dmatrices, _ref_categories=ref_categories, **kwargs))
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bootstrap_rows = pd.Series(dmatrices[0].index).sample(len(df), replace=True)
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bootstrap_dmatrices = (dmatrices[0].loc[bootstrap_rows], dmatrices[1].loc[bootstrap_rows])
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bootstrap_results.append(regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=bootstrap_dmatrices, _ref_categories=ref_categories, **kwargs))
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# Combine bootstrap results
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terms = {}
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for term_name, term in full_model.terms.items():
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if isinstance(term, SingleTerm):
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bootstrap_betas = [r.terms[term_name].beta.point for r in bootstrap_results]
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bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
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bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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terms[term_name] = SingleTerm(term.raw_name, Estimate(term.beta.point, np.quantile(bootstrap_betas, 0.025), np.quantile(bootstrap_betas, 0.975)), bootstrap_pvalue)
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else:
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categories = {}
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for sub_term_name, sub_term in term.categories.items():
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bootstrap_betas = [r.terms[term_name].categories[sub_term_name].beta.point for r in bootstrap_results if sub_term_name in r.terms[term_name].categories]
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bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
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bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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categories[sub_term_name] = SingleTerm(sub_term.raw_name, Estimate(sub_term.beta.point, np.quantile(bootstrap_betas, 0.025), np.quantile(bootstrap_betas, 0.975)), bootstrap_pvalue)
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terms[term_name] = CategoricalTerm(categories, term.ref_category)
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return RegressionResult(
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None,
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full_model.full_name, full_model.model_name, full_model.fit_method,
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dep, full_model.nobs, full_model.dof_model, datetime.now(), full_model.cov_type,
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terms,
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full_model.llf, full_model.llnull,
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full_model.dof_resid, full_model.rsquared, full_model.f_statistic,
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full_model.exp
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)
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@ -16,6 +16,7 @@
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import numpy as np
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import pandas as pd
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import patsy
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from scipy import stats
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import statsmodels
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import statsmodels.api as sm
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@ -438,7 +439,8 @@ def regress(
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model_kwargs=None, fit_kwargs=None,
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family=None, # common model_kwargs
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cov_type=None, maxiter=None, start_params=None, # common fit_kwargs
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bool_baselevels=False, exp=None
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bool_baselevels=False, exp=None,
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_dmatrices=None, _ref_categories=None,
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):
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"""
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Fit a statsmodels regression model
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@ -526,19 +528,26 @@ def regress(
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else:
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exp = False
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# Check for/clean NaNs
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df = df[[dep] + cols_for_formula(formula, df)]
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df = check_nan(df, nan_policy)
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# Ensure numeric type for dependent variable
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if df[dep].dtype != 'float64':
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df[dep] = df[dep].astype('float64')
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# Convert pandas nullable types for independent variables as this breaks statsmodels
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df = convert_pandas_nullable(df)
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if _dmatrices is None:
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# Check for/clean NaNs
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df = df[[dep] + cols_for_formula(formula, df)]
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df = check_nan(df, nan_policy)
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# Ensure numeric type for dependent variable
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if df[dep].dtype != 'float64':
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df[dep] = df[dep].astype('float64')
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# Convert pandas nullable types for independent variables as this breaks statsmodels
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df = convert_pandas_nullable(df)
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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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else:
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dmatrices = _dmatrices
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# Fit model
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model = model_class.from_formula(formula=dep + ' ~ ' + formula, data=df, **model_kwargs)
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model = model_class(endog=dmatrices[0], exog=dmatrices[1], **model_kwargs)
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model.formula = dep + ' ~ ' + formula
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result = model.fit(disp=False, **fit_kwargs)
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if isinstance(result, RegressionResult):
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@ -553,16 +562,20 @@ def regress(
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# Process terms
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terms = {}
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confint = result.conf_int(config.alpha)
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# Join term names manually because statsmodels wrapper is very slow
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#confint = result.conf_int(config.alpha)
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confint = {k: v for k, v in zip(model.exog_names, result._results.conf_int(config.alpha))}
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pvalues = {k: v for k, v in zip(model.exog_names, result._results.pvalues)}
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for raw_name, raw_beta in result.params.items():
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beta = Estimate(raw_beta, confint[0][raw_name], confint[1][raw_name])
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#for raw_name, raw_beta in result.params.items():
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for raw_name, raw_beta in zip(model.exog_names, result._results.params):
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beta = Estimate(raw_beta, confint[raw_name][0], confint[raw_name][1])
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# Rename terms
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if raw_name == 'Intercept':
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# Intercept term (single term)
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term = '(Intercept)'
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terms[term] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
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terms[term] = SingleTerm(raw_name, beta, pvalues[raw_name])
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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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@ -572,28 +585,42 @@ def regress(
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if bool_baselevels is False and contrast == 'True' and set(df[column].unique()) == set([True, False]):
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# Treat as single term
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terms[column] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
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terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
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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 terms:
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ref_category = formula_factor_ref_category(formula, df, factor)
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if _ref_categories is None:
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ref_category = formula_factor_ref_category(formula, df, factor)
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else:
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ref_category = _ref_categories[column]
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terms[column] = CategoricalTerm({}, ref_category)
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terms[column].categories[contrast] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
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terms[column].categories[contrast] = SingleTerm(raw_name, beta, pvalues[raw_name])
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else:
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# Single term
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terms[column] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
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terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
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# Fit null model (for llnull)
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if hasattr(result, 'llnull'):
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llnull = result.llnull
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else:
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result_null = model_class.from_formula(formula=dep + ' ~ 1', data=df).fit()
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# Construct null (intercept-only) model
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#result_null = model_class.from_formula(formula=dep + ' ~ 1', data=df).fit()
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dm_exog = pd.DataFrame(index=dmatrices[0].index)
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dm_exog['Intercept'] = pd.Series(dtype='float64')
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dm_exog['Intercept'].fillna(1, inplace=True)
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result_null = model_class(endog=dmatrices[0], exog=dm_exog).fit()
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llnull = result_null.llf
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# Parse raw regression results (to get fit method)
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header_entries = np.vectorize(str.strip)(np.concatenate(np.split(np.array(result.summary().tables[0].data), 2, axis=1)))
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header_dict = {x[0]: x[1] for x in header_entries}
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if model_class is sm.OLS:
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method_name = 'Least Squares'
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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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header_entries = np.vectorize(str.strip)(np.concatenate(np.split(np.array(result.summary().tables[0].data), 2, axis=1)))
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header_dict = {x[0]: x[1] for x in header_entries}
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method_name = header_dict['Method:']
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# Get full name to display
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if model_class is sm.Logit:
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@ -605,7 +632,7 @@ def regress(
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return RegressionResult(
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result,
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full_name, model_class.__name__, header_dict['Method:'],
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full_name, model_class.__name__, method_name,
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dep, result.nobs, result.df_model, datetime.now(), result.cov_type,
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terms,
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result.llf, llnull,
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@ -697,7 +724,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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import rpy2.robjects.pandas2ri
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# Assume data is already cleaned from regress()
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df = self.data.frame.copy()
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df = self.data.orig_endog.join(self.data.orig_exog)
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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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