Implement yli.OrdinalLogit as preferred model for ordinal logistic regression
OrdinalLogit uses a parameterisation where the cutoff terms are directly incorporated
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@ -19,7 +19,7 @@ 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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from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
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from .regress import PenalisedLogit, logit_then_regress, regress, regress_bootstrap, vif
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from .regress import OrdinalLogit, PenalisedLogit, logit_then_regress, regress, regress_bootstrap, vif
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from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, ttest_ind
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def reload_me():
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@ -18,10 +18,10 @@ 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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from scipy.special import expit
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import statsmodels, 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.miscmodels.ordinal_model import OrderedModel
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from statsmodels.stats.outliers_influence import variance_inflation_factor
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from tqdm import tqdm
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@ -334,7 +334,8 @@ class RegressionResult:
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out += '<tr><th>{}</th><td></td><td style="padding-right:0"></td><td></td><td style="padding-left:0"></td><td></td></tr>'.format(term_name)
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# Render reference category
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out += '<tr><td style="text-align:right;font-style:italic">{}</td><td>Ref.</td><td style="padding-right:0"></td><td></td><td style="padding-left:0"></td><td></td></tr>'.format(term.ref_category)
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if term.ref_category is not None:
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out += '<tr><td style="text-align:right;font-style:italic">{}</td><td>Ref.</td><td style="padding-right:0"></td><td></td><td style="padding-left:0"></td><td></td></tr>'.format(term.ref_category)
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# Loop over terms
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for sub_term_name, sub_term in term.categories.items():
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@ -401,7 +402,8 @@ class RegressionResult:
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table_data.append([term_name + ' ', '', '', '', '', ''])
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# Render reference category
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table_data.append(['{} '.format(term.ref_category), 'Ref.', '', '', '', ''])
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if term.ref_category is not None:
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table_data.append(['{} '.format(term.ref_category), 'Ref.', '', '', '', ''])
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# Loop over terms
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for sub_term_name, sub_term in term.categories.items():
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@ -546,7 +548,7 @@ def regress(
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if exp is None:
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if model_class in (sm.Logit, sm.Poisson, PenalisedLogit):
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exp = True
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elif model_class is OrderedModel and model_kwargs.get('distr', 'probit') == 'logit':
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elif model_class is OrdinalLogit:
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exp = True
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else:
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exp = False
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@ -568,7 +570,7 @@ def regress(
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else:
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dmatrices = _dmatrices
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if model_class is OrderedModel:
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if model_class is OrdinalLogit:
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# Drop explicit intercept term
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# FIXME: Check before dropping
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dmatrices = (dmatrices[0], dmatrices[1].iloc[:,1:])
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@ -604,9 +606,11 @@ def regress(
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# Intercept term (single term)
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term = '(Intercept)'
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terms[term] = SingleTerm(raw_name, beta, pvalues[raw_name])
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elif model_class is OrderedModel and '/' in raw_name:
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# Ignore ordinal regression intercepts
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pass
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elif model_class is OrdinalLogit and '/' in raw_name:
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# Group ordinal regression cutoffs
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if '(Cutoffs)' not in terms:
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terms['(Cutoffs)'] = CategoricalTerm({}, None)
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terms['(Cutoffs)'].categories[raw_name] = 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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@ -628,12 +632,6 @@ def regress(
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# Single term
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terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
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# Handle ordinal regression intercepts
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#if model_class is OrderedModel:
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# intercept_names = [raw_name.split('/')[0] for raw_name in model.exog_names if '/' in raw_name]
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# intercepts = model.transform_threshold_params(result._results.params[-len(intercept_names):])
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# print(intercepts)
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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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@ -664,10 +662,6 @@ def regress(
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if fit_kwargs.get('cov_type', 'nonrobust') != 'nonrobust':
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full_name = 'Robust {}'.format(full_name)
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comments = []
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if model_class is OrderedModel:
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comments.append('Cutpoints are omitted from the table of model parameters.')
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return RegressionResult(
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result,
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full_name, model_class.__name__, method_name,
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@ -675,7 +669,7 @@ def regress(
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terms,
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result.llf, llnull,
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getattr(result, 'df_resid', None), getattr(result, 'rsquared', None), getattr(result, 'fvalue', None),
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comments,
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[],
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exp
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)
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@ -816,7 +810,7 @@ def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kw
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class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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"""
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statsmodel-compatible model for computing Firth penalised logistic regression
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statsmodels-compatible model for computing Firth penalised logistic regression
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Uses the R *logistf* library.
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@ -894,3 +888,28 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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None # Set exp in regress()
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)
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# ------------------------------------------------------
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# Ordinal logistic regression (R/Stata parameterisation)
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class OrdinalLogit(statsmodels.miscmodels.ordinal_model.OrderedModel):
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"""
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statsmodels-compatible model for computing ordinal logistic (or probit) regression
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The implementation subclasses statsmodels' native *OrderedModel*, but substitutes an alternative parameterisation used by R and Stata.
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The the native statsmodels implementation, the first cutoff term is the true cutoff, and further cutoff terms are log differences between consecutive cutoffs.
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In this parameterisation, cutoff terms are represented directly in the model.
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"""
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def __init__(self, endog, exog, **kwargs):
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if 'distr' not in kwargs:
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kwargs['distr'] = 'logit'
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super().__init__(endog, exog, **kwargs)
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def transform_threshold_params(self, params):
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th_params = params[-(self.k_levels - 1):]
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thresh = np.concatenate(([-np.inf], th_params, [np.inf]))
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return thresh
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def transform_reverse_threshold_params(self, params):
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return params[:-1]
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