Handle OrderedModel for yli.regress
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@ -21,6 +21,7 @@ from scipy import stats
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import statsmodels
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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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@ -139,6 +140,7 @@ class RegressionResult:
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terms,
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llf, llnull,
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dof_resid, rsquared, f_statistic,
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comments,
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exp
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):
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#: Raw result from statsmodels *model.fit*
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@ -182,6 +184,9 @@ class RegressionResult:
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#: *F* statistic (*float*; *None* if N/A)
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self.f_statistic = f_statistic
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#: Comments for the model (*List[str]*)
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self.comments = comments or []
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# Config for display style
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#: See :func:`yli.regress`
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self.exp = exp
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@ -255,7 +260,8 @@ class RegressionResult:
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left_col.append(('Method:', self.fit_method))
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left_col.append(('Date:', self.fitted_dt.strftime('%Y-%m-%d')))
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left_col.append(('Time:', self.fitted_dt.strftime('%H:%M:%S')))
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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))
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if self.cov_type:
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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))
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# Right column
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right_col = []
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@ -345,6 +351,12 @@ class RegressionResult:
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# TODO: Have a detailed view which shows SE, t/z, etc.
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if self.comments:
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out += '<ol>'
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for comment in self.comments:
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out += '<li>{}</li>'.format(comment)
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out += '</ol>'
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return out
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def summary(self):
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@ -412,6 +424,11 @@ class RegressionResult:
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out += '\n'.join(table2_lines[1:])
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if self.comments:
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out += '\n'
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for i, comment in enumerate(self.comments):
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out += '\n{}. {}'.format(i + 1, comment)
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return out
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class SingleTerm:
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@ -526,6 +543,8 @@ 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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exp = True
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else:
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exp = False
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@ -546,6 +565,11 @@ 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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# 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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# Fit model
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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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@ -577,6 +601,9 @@ 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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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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@ -598,6 +625,12 @@ 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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@ -628,13 +661,18 @@ 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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dep, result.nobs, result.df_model, datetime.now(), result.cov_type,
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dep, result.nobs, result.df_model, datetime.now(), getattr(result, 'cov_type', 'nonrobust'),
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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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exp
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)
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@ -731,6 +769,7 @@ def regress_bootstrap(
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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.comments,
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full_model.exp
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)
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@ -848,5 +887,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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terms,
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model['loglik'][0], model['loglik'][1],
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None, None, None,
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[],
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None # Set exp in regress()
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)
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