Implement RegressionResult.bayesfactor_beta_zero
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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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from pytest import approx
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import pandas as pd
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import statsmodels.api as sm
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import yli
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def test_afbf_logit_beta_zero():
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"""Compare RegressionResult.bayesfactor_beta_zero for Ott & Longnecker (2016) chapter 12.23 with R BFpack"""
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df = pd.DataFrame({
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'Unhealthy': [False, False, False, False, False, False, False, True, False, False, False, True, False, False, False, False, False, False, True, False, True, False, False, False, False, False, True, False, False, True, False, False],
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'Fibrinogen': [2.52, 2.46, 2.29, 3.15, 2.88, 2.29, 2.99, 2.38, 2.56, 3.22, 2.35, 3.53, 2.65, 2.15, 3.32, 2.23, 2.19, 2.21, 5.06, 2.68, 2.09, 2.54, 2.18, 2.68, 3.41, 3.15, 3.35, 2.60, 2.28, 3.93, 2.60, 3.34],
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'GammaGlobulin': [38, 36, 36, 36, 30, 31, 36, 37, 31, 38, 29, 46, 46, 31, 35, 37, 33, 37, 37, 34, 44, 28, 31, 39, 37, 39, 32, 38, 36, 32, 41, 30]
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})
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result = yli.regress(sm.Logit, df, 'Unhealthy', 'Fibrinogen + GammaGlobulin')
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# model <- glm(Unhealthy ~ Fibrinogen + GammaGlobulin, data=df, family=binomial())
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# bf_fit <- BF(model, hypothesis="Fibrinogen = 0")
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# summary(bf_fit)
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# bf_fit <- BF(model, hypothesis="GammaGlobulin = 0")
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# summary(bf_fit)
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bf = result.bayesfactor_beta_zero('Fibrinogen')
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assert bf.factor == approx(1.229, abs=0.001)
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assert bf.num_desc == 'Fibrinogen ≠ 0'
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assert bf.denom_desc == 'Fibrinogen = 0'
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bf = result.bayesfactor_beta_zero('GammaGlobulin')
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assert bf.factor == approx(2.417, abs=0.001)
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assert bf.num_desc == 'GammaGlobulin = 0'
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assert bf.denom_desc == 'GammaGlobulin ≠ 0'
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@ -14,6 +14,7 @@
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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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from .bayes_factors import BayesFactor, bayesfactor_afbf
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from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
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from .fs import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
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from .regress import PenalisedLogit, regress, vif
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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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class BayesFactor:
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"""
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A Bayes factor
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"""
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def __init__(self, factor, num_symbol, num_desc, denom_symbol, denom_desc):
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self.factor = factor
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self.num_symbol = num_symbol
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self.num_desc = num_desc
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self.denom_symbol = denom_symbol
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self.denom_desc = denom_desc
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def _repr_html_(self):
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return 'BF<sub>{0}{1}</sub> = {2:.2f}, {5}<br>H<sub>{0}</sub>: {3}<br>H<sub>{1}</sub>: {4}'.format(self.num_symbol, self.denom_symbol, self.factor, self.num_desc, self.denom_desc, self.interpret_lw(html=True))
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def summary(self):
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return 'BF{0}{1} = {2:.2f}, {5}\nH{0}: {3}\nH{1}: {4}'.format(self.num_symbol, self.denom_symbol, self.factor, self.num_desc, self.denom_desc, self.interpret_lw(html=False))
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def invert(self):
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"""Invert the Bayes factor"""
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return BayesFactor(1 / self.factor, self.denom_symbol, self.denom_desc, self.num_symbol, self.num_desc)
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def interpret_lw(self, html):
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"""Interpret the Bayes factor according to the Lee & Wagenmakers classification scheme"""
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if self.factor == 1:
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return 'Evidence favours neither hypothesis to the other'
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if self.factor < 1:
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return self.invert().interpret_lw()
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if self.factor < 3:
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level = 'Anecdotal'
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elif self.factor < 10:
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level = 'Moderate'
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elif self.factor < 30:
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level = 'Strong'
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elif self.factor < 100:
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level = 'Very strong'
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else:
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level = 'Extreme'
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if html:
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return '{} evidence in favour of H<sub>{}</sub>'.format(level, self.num_symbol)
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else:
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return '{} evidence in favour of H{}'.format(level, self.num_symbol)
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def bayesfactor_afbf(params, cov, n, hypothesis):
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"""
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Compute an adjusted fractional Bayes factor for the hypothesis
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Using R "BFpack" library
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See R documentation for BFpack.BF
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Returns Bayes factor for hypothesis vs its complement
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"""
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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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# Import BFpack
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ro.packages.importr('BFpack')
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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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lc['params'] = params
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lc['cov'] = cov
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lc['n'] = n
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lc['hypothesis'] = hypothesis
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ro.r('bf_fit <- BF(params, Sigma=as.matrix(cov), n=n, hypothesis=hypothesis)')
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bf_matrix = ro.r('bf_fit$BFmatrix_confirmatory')
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return BayesFactor(bf_matrix[0][1], '1', hypothesis, 'C', 'Complement')
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@ -26,6 +26,7 @@ from statsmodels.stats.outliers_influence import variance_inflation_factor
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from datetime import datetime
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import itertools
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from .bayes_factors import BayesFactor, bayesfactor_afbf
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from .utils import Estimate, check_nan, fmt_p_html, fmt_p_text
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def vif(df, formula=None, nan_policy='warn'):
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@ -171,6 +172,25 @@ class RegressionResult:
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pvalue = 1 - stats.f(self.dof_model, self.dof_resid).cdf(self.f_statistic)
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return FTestResult(self.f_statistic, self.dof_model, self.dof_resid, pvalue)
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def bayesfactor_beta_zero(self, term):
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"""
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Compute Bayes factor testing the hypothesis that the given beta is 0
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Requires statsmodels regression
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"""
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# Get parameters required for AFBF
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params = pd.Series({term.replace('[', '_').replace(']', '_'): beta.point for term, beta in self.beta.items()})
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cov = self.raw_result.cov_params()
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# Compute BF matrix
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bf01 = bayesfactor_afbf(params, cov, self.nobs, '{} = 0'.format(term.replace('[', '_').replace(']', '_')))
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bf01 = BayesFactor(bf01.factor, '0', '{} = 0'.format(term), '1', '{} ≠ 0'.format(term))
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if bf01.factor >= 1:
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return bf01
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else:
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return bf01.invert()
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def _header_table(self, html):
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"""Return the entries for the header table"""
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