# scipy-yli: Helpful SciPy utilities and recipes
# Copyright © 2022 Lee Yingtong Li (RunasSudo)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see .
import numpy as np
import pandas as pd
import patsy
from scipy import stats
from scipy.special import expit
import statsmodels, statsmodels.miscmodels.ordinal_model
import statsmodels.api as sm
from statsmodels.iolib.table import SimpleTable
from statsmodels.stats.outliers_influence import variance_inflation_factor
from tqdm import tqdm
from datetime import datetime
import itertools
import warnings
from .bayes_factors import BayesFactor, bayesfactor_afbf
from .config import config
from .sig_tests import FTestResult
from .utils import Estimate, PValueStyle, check_nan, cols_for_formula, convert_pandas_nullable, fmt_p, formula_factor_ref_category, parse_patsy_term
def vif(df, formula=None, *, nan_policy='warn'):
"""
Calculate the variance inflation factor (VIF) for each variable in *df*
:param df: Data to calculate the VIF for
:type df: DataFrame
:param formula: If specified, calculate the VIF only for the variables in the formula
:type formula: str
:return: The variance inflation factors
:rtype: Series
**Example:**
.. code-block::
df = pd.DataFrame({
'D': [68.58, 67.33, 67.33, ...],
'T1': [14, 10, 10, ...],
'T2': [46, 73, 85, ...],
...
})
yli.vif(df[['D', 'T1', 'T2', ...]])
.. code-block:: text
D 8.318301
T1 6.081590
T2 2.457122
...
dtype: float64
The output shows the variance inflation factor for each variable in *df*.
"""
if formula:
# Only consider columns in the formula
df = df[cols_for_formula(formula, df)]
# Check for/clean NaNs
df = check_nan(df, nan_policy)
# Convert all to float64 otherwise statsmodels chokes with "ufunc 'isfinite' not supported for the input types ..."
df = pd.get_dummies(df, drop_first=True) # Convert categorical dtypes
df = df.astype('float64') # Convert all other dtypes
# Add intercept column
orig_columns = list(df.columns)
df['Intercept'] = [1] * len(df)
vifs = {}
for i, col in enumerate(orig_columns):
vifs[col] = variance_inflation_factor(df, i)
return pd.Series(vifs)
# ----------
# Regression
class LikelihoodRatioTestResult:
"""
Result of a likelihood ratio test for regression
See :meth:`RegressionResult.lrtest_null`.
"""
def __init__(self, statistic, dof, pvalue):
#: Likelihood ratio test statistic (*float*)
self.statistic = statistic
#: Degrees of freedom for the likelihood ratio test statistic (*int*)
self.dof = dof
#: *p* value for the likelihood ratio test (*float*)
self.pvalue = pvalue
def __repr__(self):
if config.repr_is_summary:
return self.summary()
return super().__repr__()
def _repr_html_(self):
return 'LR({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION | PValueStyle.HTML))
def summary(self):
"""
Return a stringified summary of the likelihood ratio test
:rtype: str
"""
return 'LR({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, PValueStyle.RELATION))
class RegressionResult:
"""
Result of a regression
See :func:`yli.regress`.
"""
def __init__(self,
raw_result,
full_name, model_name, fit_method,
dep, nobs, dof_model, fitted_dt, cov_type,
terms,
llf, llnull,
dof_resid, rsquared, f_statistic,
comments,
exp
):
#: Raw result from statsmodels *model.fit*
self.raw_result = raw_result
# Information for display
#: Full name of the regression model type (*str*)
self.full_name = full_name
#: Short name of the regression model type (*str*)
self.model_name = model_name
#: Method for fitting the regression model (*str*)
self.fit_method = fit_method
# Basic fitted model information
#: Name of the dependent variable (*str*)
self.dep = dep
#: Number of observations (*int*)
self.nobs = nobs
#: Degrees of freedom for the model (*int*)
self.dof_model = dof_model
#: Date and time of fitting the model (Python *datetime*)
self.fitted_dt = fitted_dt
#: Method for computing the covariance matrix (*str*)
self.cov_type = cov_type
# Regression coefficients/p values
#: Coefficients and *p* values for each term in the model (*dict* of :class:`SingleTerm` or :class:`CategoricalTerm`)
self.terms = terms
# Model log-likelihood
#: Log-likelihood of fitted model (*float*)
self.llf = llf
#: Log-likelihood of null model (*float*)
self.llnull = llnull
# Extra statistics (not all regression models have these)
#: Degrees of freedom for the residuals (*int*; *None* if N/A)
self.dof_resid = dof_resid
#: *R*:sup:`2` statistic (*float*; *None* if N/A)
self.rsquared = rsquared
#: *F* statistic (*float*; *None* if N/A)
self.f_statistic = f_statistic
#: Comments for the model (*List[str]*)
self.comments = comments or []
# Config for display style
#: See :func:`yli.regress`
self.exp = exp
@property
def pseudo_rsquared(self):
"""McFadden's pseudo *R*:sup:`2` statistic"""
return 1 - self.llf/self.llnull
def lrtest_null(self):
"""
Compute the likelihood ratio test comparing the model to the null model
:rtype: :class:`LikelihoodRatioTestResult`
"""
statistic = -2 * (self.llnull - self.llf)
pvalue = 1 - stats.chi2.cdf(statistic, self.dof_model)
return LikelihoodRatioTestResult(statistic, self.dof_model, pvalue)
def ftest(self):
"""
Perform the *F* test that all slopes are 0
:rtype: :class:`yli.sig_tests.FTestResult`
"""
pvalue = 1 - stats.f(self.dof_model, self.dof_resid).cdf(self.f_statistic)
return FTestResult(self.f_statistic, self.dof_model, self.dof_resid, pvalue)
def bayesfactor_beta_zero(self, term):
"""
Compute a Bayes factor testing the hypothesis that the given beta is 0
Uses the R *BFpack* library.
Requires the regression to be from statsmodels.
The term must be specified as the *raw name* from the statsmodels regression, available via :attr:`RegressionResult.raw_result`.
:param term: Raw name of the term to be tested
:type term: str
:rtype: :class:`yli.bayes_factors.BayesFactor`
"""
# FIXME: Allow specifying our renamed terms
# Get parameters required for AFBF
params = pd.Series({raw_name.replace('[', '_').replace(']', '_'): beta for raw_name, beta in self.raw_result.params.items()})
cov = self.raw_result.cov_params()
# Compute BF matrix
bf01 = bayesfactor_afbf(params, cov, self.nobs, '{} = 0'.format(term.replace('[', '_').replace(']', '_')))
bf01 = BayesFactor(bf01.factor, '0', '{} = 0'.format(term), '1', '{} ≠ 0'.format(term))
if bf01.factor >= 1:
return bf01
else:
return bf01.invert()
def _header_table(self, html):
"""Return the entries for the header table"""
# Left column
left_col = []
left_col.append(('Dep. Variable:', self.dep))
left_col.append(('Model:', self.model_name))
left_col.append(('Method:', self.fit_method))
left_col.append(('Date:', self.fitted_dt.strftime('%Y-%m-%d')))
left_col.append(('Time:', self.fitted_dt.strftime('%H:%M:%S')))
if self.cov_type:
left_col.append(('Std. Errors:', 'Non-Robust' if self.cov_type == 'nonrobust' else self.cov_type.upper() if self.cov_type.startswith('hc') else self.cov_type))
# Right column
right_col = []
right_col.append(('No. Observations:', format(self.nobs, '.0f')))
right_col.append(('Df. Model:', format(self.dof_model, '.0f')))
if self.dof_resid:
right_col.append(('Df. Residuals:', format(self.dof_resid, '.0f')))
if self.rsquared:
right_col.append(('R2:' if html else 'R²:', format(self.rsquared, '.2f')))
else:
right_col.append(('Pseudo R2:' if html else 'Pseudo R²:', format(self.pseudo_rsquared, '.2f')))
if self.f_statistic:
# Report the F test if available
f_result = self.ftest()
if html:
right_col.append(('F:', format(f_result.statistic, '.2f')))
right_col.append(('p (F):', fmt_p(f_result.pvalue, PValueStyle.VALUE_ONLY | PValueStyle.HTML)))
else:
right_col.append(('F:', format(f_result.statistic, '.2f')))
right_col.append(('p (F):', fmt_p(f_result.pvalue, PValueStyle.VALUE_ONLY)))
else:
# Otherwise report likelihood ratio test as overall test
lrtest_result = self.lrtest_null()
right_col.append(('LL-Model:', format(self.llf, '.2f')))
right_col.append(('LL-Null:', format(self.llnull, '.2f')))
if html:
right_col.append(('p (LR):', fmt_p(lrtest_result.pvalue, PValueStyle.VALUE_ONLY | PValueStyle.HTML)))
else:
right_col.append(('p (LR):', fmt_p(lrtest_result.pvalue, PValueStyle.VALUE_ONLY)))
return left_col, right_col
def __repr__(self):
if config.repr_is_summary:
return self.summary()
return super().__repr__()
def _repr_html_(self):
# Render header table
left_col, right_col = self._header_table(html=True)
out = '{} Results'.format(self.full_name)
for left_cell, right_cell in itertools.zip_longest(left_col, right_col):
out += '| {} | {} | {} | {} |
|---|
'.format(
left_cell[0] if left_cell else '',
left_cell[1] if left_cell else '',
right_cell[0] if right_cell else '',
right_cell[1] if right_cell else ''
)
out += '
'
# Render coefficients table
out += ' | {} | ({:g}% CI) | p |
|---|
'.format('exp(β)' if self.exp else 'β', (1-config.alpha)*100)
for term_name, term in self.terms.items():
if isinstance(term, SingleTerm):
# Single term
# Exponentiate beta if requested
beta = term.beta
if self.exp:
beta = np.exp(beta)
out += '| {} | {:.2f} | ({:.2f} | – | {:.2f}) | {} |
|---|
'.format(term_name, beta.point, beta.ci_lower, beta.ci_upper, fmt_p(term.pvalue, PValueStyle.TABULAR | PValueStyle.HTML))
elif isinstance(term, CategoricalTerm):
# Categorical term
out += '| {} | | | | | |
|---|
'.format(term_name)
# Render reference category
if term.ref_category is not None:
out += '| {} | Ref. | | | | |
'.format(term.ref_category)
# Loop over terms
for sub_term_name, sub_term in term.categories.items():
# Exponentiate beta if requested
beta = sub_term.beta
if self.exp:
beta = np.exp(beta)
out += '| {} | {:.2f} | ({:.2f} | – | {:.2f}) | {} |
'.format(sub_term_name, beta.point, beta.ci_lower, beta.ci_upper, fmt_p(sub_term.pvalue, PValueStyle.TABULAR | PValueStyle.HTML))
else:
raise Exception('Attempt to render unknown term type')
out += '
'
# TODO: Have a detailed view which shows SE, t/z, etc.
if self.comments:
out += ''
for comment in self.comments:
out += '- {}
'.format(comment)
out += '
'
return out
def summary(self):
"""
Return a stringified summary of the regression model
:rtype: str
"""
# Render header table
left_col, right_col = self._header_table(html=False)
# Ensure equal sizes for SimpleTable
if len(right_col) > len(left_col):
left_col.extend([('', '')] * (len(right_col) - len(left_col)))
elif len(left_col) > len(right_col):
right_col.extend([('', '')] * (len(left_col) - len(right_col)))
table1 = SimpleTable(np.concatenate([left_col, right_col], axis=1), title='{} Results'.format(self.full_name))
table1.insert_stubs(2, [' | '] * len(left_col))
# Get rid of last line (merge with next table)
table1_lines = table1.as_text().splitlines(keepends=False)
out = '\n'.join(table1_lines[:-1]) + '\n'
# Render coefficients table
table_data = []
for term_name, term in self.terms.items():
if isinstance(term, SingleTerm):
# Single term
# Exponentiate beta if requested
beta = term.beta
if self.exp:
beta = np.exp(beta)
# Add some extra padding
table_data.append([term_name + ' ', format(beta.point, '.2f'), '({:.2f}'.format(beta.ci_lower), '-', '{:.2f})'.format(beta.ci_upper), ' ' + fmt_p(term.pvalue, PValueStyle.TABULAR)])
elif isinstance(term, CategoricalTerm):
# Categorical term
table_data.append([term_name + ' ', '', '', '', '', ''])
# Render reference category
if term.ref_category is not None:
table_data.append(['{} '.format(term.ref_category), 'Ref.', '', '', '', ''])
# Loop over terms
for sub_term_name, sub_term in term.categories.items():
# Exponentiate beta if requested
beta = sub_term.beta
if self.exp:
beta = np.exp(beta)
table_data.append([sub_term_name + ' ', format(beta.point, '.2f'), '({:.2f}'.format(beta.ci_lower), '-', '{:.2f})'.format(beta.ci_upper), ' ' + fmt_p(sub_term.pvalue, PValueStyle.TABULAR)])
else:
raise Exception('Attempt to render unknown term type')
table2 = SimpleTable(data=table_data, headers=['', 'exp(β)' if self.exp else 'β', '', '\ue000', '', ' p']) # U+E000 is in Private Use Area, mark middle of CI column
table2_text = table2.as_text().replace(' \ue000 ', '({:g}% CI)'.format((1-config.alpha)*100)) # Render heading in the right spot
table2_lines = table2_text.splitlines(keepends=False)
# Render divider line between 2 tables
max_table_len = max(len(table1_lines[-1]), len(table2_lines[-1]))
out += '=' * max_table_len + '\n'
out += '\n'.join(table2_lines[1:])
if self.comments:
out += '\n'
for i, comment in enumerate(self.comments):
out += '\n{}. {}'.format(i + 1, comment)
return out
class SingleTerm:
"""A term in a :class:`RegressionResult` which is a single term"""
def __init__(self, raw_name, beta, pvalue):
#: Raw name of the term (*str*; e.g. in :attr:`RegressionResult.raw_result`)
self.raw_name = raw_name
#: :class:`yli.utils.Estimate` of the coefficient
self.beta = beta
#: *p* value for the coefficient (*float*)
self.pvalue = pvalue
class CategoricalTerm:
"""A group of terms in a :class:`RegressionResult` corresponding to a categorical variable"""
def __init__(self, categories, ref_category):
#: Terms for each of the categories, excluding the reference category (*dict* of :class:`SingleTerm`)
self.categories = categories
#: Name of the reference category (*str*)
self.ref_category = ref_category
def regress(
model_class, df, dep, formula, *,
nan_policy='warn',
model_kwargs=None, fit_kwargs=None,
family=None, # common model_kwargs
cov_type=None, method=None, maxiter=None, start_params=None, # common fit_kwargs
bool_baselevels=False, exp=None,
_dmatrices=None,
):
"""
Fit a statsmodels regression model
:param model_class: Type of regression model to fit
:type model_class: statsmodels model class
:param df: Data to perform regression on
:type df: DataFrame
:param dep: Column in *df* for the dependent variable (numeric)
:type dep: str
:param formula: Patsy formula for the regression model
:type formula: str
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
:type nan_policy: str
:param model_kwargs: Keyword arguments to pass to *model_class* constructor
:type model_kwargs: dict
:param fit_kwargs: Keyword arguments to pass to *model.fit*
:type fit_kwargs: dict
:param family: See statsmodels *GLM* constructor
:param cov_type: See statsmodels *model.fit*
:param method: See statsmodels *model.fit*
:param maxiter: See statsmodels *model.fit*
:param start_params: See statsmodels *model.fit*
:param bool_baselevels: Show reference categories for boolean independent variables even if reference category is *False*
:type bool_baselevels: bool
:param exp: Report exponentiated parameters rather than raw parameters, default (*None*) is to autodetect based on *model_class*
:type exp: bool
:rtype: :class:`yli.regress.RegressionResult`
**Example:**
.. code-block::
df = pd.DataFrame({
'Unhealthy': [False, False, False, ...],
'Fibrinogen': [2.52, 2.46, 2.29, ...],
'GammaGlobulin': [38, 36, 36, ...]
})
yli.regress(sm.Logit, df, 'Unhealthy', 'Fibrinogen + GammaGlobulin')
.. code-block:: text
Logistic Regression Results
======================================================
Dep. Variable: Unhealthy | No. Observations: 32
Model: Logit | Df. Model: 2
Method: MLE | Df. Residuals: 29
Date: 2022-10-18 | Pseudo R²: 0.26
Time: 19:00:34 | LL-Model: -11.47
Std. Errors: Non-Robust | LL-Null: -15.44
| p (LR): 0.02*
======================================================
exp(β) (95% CI) p
-----------------------------------------------
(Intercept) 0.00 (0.00 - 0.24) 0.03*
Fibrinogen 6.80 (1.01 - 45.79) 0.049*
GammaGlobulin 1.17 (0.92 - 1.48) 0.19
-----------------------------------------------
The output summarises the results of the regression.
Note that the parameter estimates are automatically exponentiated.
For example, the odds ratio for unhealthiness per unit increase in fibrinogen is 6.80, with 95% confidence interval 1.01–45.79, and is significant with *p* value 0.049.
"""
# Populate model_kwargs
if model_kwargs is None:
model_kwargs = {}
if family is not None:
model_kwargs['family'] = family
# Populate fit_kwargs
if fit_kwargs is None:
fit_kwargs = {}
if cov_type is not None:
fit_kwargs['cov_type'] = cov_type
if method is not None:
fit_kwargs['method'] = method
if maxiter is not None:
fit_kwargs['maxiter'] = maxiter
if start_params is not None:
fit_kwargs['start_params'] = start_params
# Autodetect whether to exponentiate
if exp is None:
if model_class in (sm.Logit, sm.Poisson, PenalisedLogit):
exp = True
elif model_class is OrdinalLogit:
exp = True
else:
exp = False
if _dmatrices is None:
# Check for/clean NaNs
df = df[[dep] + cols_for_formula(formula, df)]
df = check_nan(df, nan_policy)
# Ensure numeric type for dependent variable
if df[dep].dtype != 'float64':
df[dep] = df[dep].astype('float64')
# Convert pandas nullable types for independent variables as this breaks statsmodels
df = convert_pandas_nullable(df)
# Construct design matrix from formula
dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
else:
dmatrices = _dmatrices
if model_class is OrdinalLogit:
# Drop explicit intercept term
# FIXME: Check before dropping
dmatrices = (dmatrices[0], dmatrices[1].iloc[:,1:])
# Fit model
model = model_class(endog=dmatrices[0], exog=dmatrices[1], **model_kwargs)
model.formula = dep + ' ~ ' + formula
result = model.fit(disp=False, **fit_kwargs)
if isinstance(result, RegressionResult):
# Already processed!
result.exp = exp
return result
# Check convergence
if hasattr(result, 'mle_retvals') and not result.mle_retvals['converged']:
warnings.warn('Maximum likelihood estimation failed to converge. Check raw_result.mle_retvals.')
# Process terms
terms = {}
# Join term names manually because statsmodels wrapper is very slow
#confint = result.conf_int(config.alpha)
confint = {k: v for k, v in zip(model.exog_names, result._results.conf_int(config.alpha))}
pvalues = {k: v for k, v in zip(model.exog_names, result._results.pvalues)}
#for raw_name, raw_beta in result.params.items():
for raw_name, raw_beta in zip(model.exog_names, result._results.params):
beta = Estimate(raw_beta, confint[raw_name][0], confint[raw_name][1])
# Rename terms
if raw_name == 'Intercept':
# Intercept term (single term)
term = '(Intercept)'
terms[term] = SingleTerm(raw_name, beta, pvalues[raw_name])
elif model_class is OrdinalLogit and '/' in raw_name:
# Group ordinal regression cutoffs
if '(Cutoffs)' not in terms:
terms['(Cutoffs)'] = CategoricalTerm({}, None)
terms['(Cutoffs)'].categories[raw_name] = SingleTerm(raw_name, beta, pvalues[raw_name])
else:
# Parse if required
factor, column, contrast = parse_patsy_term(formula, df, raw_name)
if contrast is not None:
# Categorical term
if bool_baselevels is False and contrast == 'True' and set(df[column].unique()) == set([True, False]):
# Treat as single term
terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
else:
# Add a new categorical term if not exists
if column not in terms:
ref_category = formula_factor_ref_category(formula, df, factor)
terms[column] = CategoricalTerm({}, ref_category)
terms[column].categories[contrast] = SingleTerm(raw_name, beta, pvalues[raw_name])
else:
# Single term
terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
# Fit null model (for llnull)
if hasattr(result, 'llnull'):
llnull = result.llnull
else:
# Construct null (intercept-only) model
#result_null = model_class.from_formula(formula=dep + ' ~ 1', data=df).fit()
dm_exog = pd.DataFrame(index=dmatrices[0].index)
dm_exog['Intercept'] = pd.Series(dtype='float64')
dm_exog['Intercept'].fillna(1, inplace=True)
result_null = model_class(endog=dmatrices[0], exog=dm_exog).fit()
llnull = result_null.llf
if model_class is sm.OLS:
method_name = 'Least Squares'
else:
# Parse raw regression results to get fit method
# Avoid this in general as it can be expensive to summarise all the post hoc tests, etc.
header_entries = np.vectorize(str.strip)(np.concatenate(np.split(np.array(result.summary().tables[0].data), 2, axis=1)))
header_dict = {x[0]: x[1] for x in header_entries}
method_name = header_dict['Method:']
# Get full name to display
if model_class is sm.Logit:
full_name = 'Logistic Regression'
else:
full_name = '{} Regression'.format(model_class.__name__)
if fit_kwargs.get('cov_type', 'nonrobust') != 'nonrobust':
full_name = 'Robust {}'.format(full_name)
return RegressionResult(
result,
full_name, model_class.__name__, method_name,
dep, result.nobs, result.df_model, datetime.now(), getattr(result, 'cov_type', 'nonrobust'),
terms,
result.llf, llnull,
getattr(result, 'df_resid', None), getattr(result, 'rsquared', None), getattr(result, 'fvalue', None),
[],
exp
)
def regress_bootstrap(
model_class, df, dep, formula, *,
nan_policy='warn',
model_kwargs=None, fit_kwargs=None,
samples=1000,
**kwargs
):
"""
Fit a statsmodels regression model, using bootstrapping to compute confidence intervals and *p* values
:param model_class: See :func:`yli.regress`
:param df: See :func:`yli.regress`
:param dep: See :func:`yli.regress`
:param formula: See :func:`yli.regress`
:param nan_policy: See :func:`yli.regress`
:param model_kwargs: See :func:`yli.regress`
:param fit_kwargs: See :func:`yli.regress`
:param samples: Number of bootstrap samples to draw
:type samples: int
:param kwargs: Other arguments to pass to :func:`yli.regress`
:rtype: :class:`yli.regress.RegressionResult`
"""
if model_kwargs is None:
model_kwargs = {}
if fit_kwargs is None:
fit_kwargs = {}
# Check for/clean NaNs
# Following this, we pass nan_policy='raise' to assert no NaNs remaining
df = df[[dep] + cols_for_formula(formula, df)]
df = check_nan(df, nan_policy)
# Ensure numeric type for dependent variable
if df[dep].dtype != 'float64':
df[dep] = df[dep].astype('float64')
# Convert pandas nullable types for independent variables as this breaks statsmodels
df = convert_pandas_nullable(df)
# Precompute design matrices
dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
# Fit full model
full_model = regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=dmatrices, model_kwargs=model_kwargs, fit_kwargs=fit_kwargs, **kwargs)
# Initialise bootstrap_results
bootstrap_results = {} # Dict mapping term raw names to bootstrap betas
for term in full_model.terms.values():
if isinstance(term, SingleTerm):
bootstrap_results[term.raw_name] = []
else:
for sub_term in term.categories.values():
bootstrap_results[sub_term.raw_name] = []
# Draw bootstrap samples and regress
dmatrices = dmatrices[0].join(dmatrices[1])
for i in tqdm(range(samples)):
bootstrap_rows = dmatrices.sample(len(df), replace=True)
model = model_class(endog=bootstrap_rows.iloc[:,0], exog=bootstrap_rows.iloc[:,1:], **model_kwargs)
model.formula = dep + ' ~ ' + formula
result = model.fit(disp=False, **fit_kwargs)
for raw_name, raw_beta in zip(model.exog_names, result._results.params):
bootstrap_results[raw_name].append(raw_beta)
# Combine bootstrap results
terms = {}
for term_name, term in full_model.terms.items():
if isinstance(term, SingleTerm):
bootstrap_betas = bootstrap_results[term.raw_name]
bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
terms[term_name] = SingleTerm(term.raw_name, Estimate(term.beta.point, np.quantile(bootstrap_betas, config.alpha/2), np.quantile(bootstrap_betas, 1-config.alpha/2)), bootstrap_pvalue)
else:
categories = {}
for sub_term_name, sub_term in term.categories.items():
bootstrap_betas = bootstrap_results[sub_term.raw_name]
bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
categories[sub_term_name] = SingleTerm(sub_term.raw_name, Estimate(sub_term.beta.point, np.quantile(bootstrap_betas, config.alpha/2), np.quantile(bootstrap_betas, 1-config.alpha/2)), bootstrap_pvalue)
terms[term_name] = CategoricalTerm(categories, term.ref_category)
return RegressionResult(
None,
full_model.full_name, full_model.model_name, full_model.fit_method,
dep, full_model.nobs, full_model.dof_model, datetime.now(), 'Bootstrap',
terms,
full_model.llf, full_model.llnull,
full_model.dof_resid, full_model.rsquared, full_model.f_statistic,
full_model.comments,
full_model.exp
)
def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kwargs):
"""
Perform logistic regression, then use parameters as start parameters for desired regression
:param model_class: Type of regression model to fit
:type model_class: statsmodels model class
:param df: Data to perform regression on
:type df: DataFrame
:param dep: Column in *df* for the dependent variable (numeric)
:type dep: str
:param formula: Patsy formula for the regression model
:type formula: str
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
:type nan_policy: str
:param kwargs: Passed through to :func:`yli.regress`
:rtype: :class:`yli.regress.RegressionResult`
"""
# Check for/clean NaNs
# Do this once here so we only get 1 warning
df = df[[dep] + cols_for_formula(formula, df)]
df = check_nan(df, nan_policy)
# Perform logistic regression
logit_result = regress(sm.Logit, df, dep, formula, **kwargs)
logit_params = logit_result.raw_result.params
# Check convergence
if not logit_result.raw_result.mle_retvals['converged']:
return None
# Perform desired regression
return regress(model_class, df, dep, formula, start_params=logit_params, **kwargs)
# -----------------------------
# Penalised logistic regression
class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
"""
statsmodels-compatible model for computing Firth penalised logistic regression
Uses the R *logistf* library.
This class should be used in conjunction with :func:`yli.regress`.
**Example:**
.. code-block::
df = pd.DataFrame({
'Pred': [1] * 20 + [0] * 220,
'Outcome': [1] * 40 + [0] * 200
})
yli.regress(yli.PenalisedLogit, df, 'Outcome', 'Pred', exp=False)
.. code-block:: text
Penalised Logistic Regression Results
=========================================================
Dep. Variable: Outcome | No. Observations: 240
Model: Logit | Df. Model: 1
Method: Penalised ML | Pseudo R²: 0.37
Date: 2022-10-19 | LL-Model: -66.43
Time: 07:50:40 | LL-Null: -105.91
Std. Errors: Non-Robust | p (LR): <0.001*
=========================================================
β (95% CI) p
---------------------------------------------
(Intercept) -2.28 (-2.77 - -1.85) <0.001*
Pred 5.99 (3.95 - 10.85) <0.001*
---------------------------------------------
The output summarises the result of the regression.
The summary table shows that a penalised method was applied.
Note that because `exp=False` was passed, the parameter estimates are not automatically exponentiated.
"""
def fit(self, disp=False):
import rpy2.robjects as ro
import rpy2.robjects.packages
import rpy2.robjects.pandas2ri
# Assume data is already cleaned from regress()
df = self.data.orig_endog.join(self.data.orig_exog)
# Convert bool to int otherwise rpy2 chokes
df = df.replace({False: 0, True: 1})
# Import logistf
ro.packages.importr('logistf')
with ro.conversion.localconverter(ro.default_converter + ro.pandas2ri.converter):
with ro.local_context() as lc:
# Convert DataFrame to R
lc['df'] = df
# Transfer other parameters to R
lc['formula_'] = self.formula
lc['alpha'] = config.alpha
# Fit the model
model = ro.r('logistf(formula_, data=df, alpha=alpha)')
# TODO: Handle categorical terms?
terms = {t: SingleTerm(t, Estimate(b, ci0, ci1), p) for t, b, ci0, ci1, p in zip(model['terms'], model['coefficients'], model['ci.lower'], model['ci.upper'], model['prob'])}
return RegressionResult(
model,
'Penalised Logistic Regression', 'Logit', 'Penalised ML',
self.endog_names, model['n'][0], model['df'][0], datetime.now(), 'nonrobust',
terms,
model['loglik'][0], model['loglik'][1],
None, None, None,
[],
None # Set exp in regress()
)
# ------------------------------------------------------
# Ordinal logistic regression (R/Stata parameterisation)
class OrdinalLogit(statsmodels.miscmodels.ordinal_model.OrderedModel):
"""
statsmodels-compatible model for computing ordinal logistic (or probit) regression
The implementation subclasses statsmodels' native *OrderedModel*, but substitutes an alternative parameterisation used by R and Stata.
The the native statsmodels implementation, the first cutoff term is the true cutoff, and further cutoff terms are log differences between consecutive cutoffs.
In this parameterisation, cutoff terms are represented directly in the model.
"""
def __init__(self, endog, exog, **kwargs):
if 'distr' not in kwargs:
kwargs['distr'] = 'logit'
super().__init__(endog, exog, **kwargs)
def transform_threshold_params(self, params):
th_params = params[-(self.k_levels - 1):]
thresh = np.concatenate(([-np.inf], th_params, [np.inf]))
return thresh
def transform_reverse_threshold_params(self, params):
return params[:-1]