Handle OrderedModel for yli.regress
This commit is contained in:
parent
5b08784498
commit
a48e96e96c
GPG Key ID:
7234E476BF21C61A
|
|
@ -21,6 +21,7 @@ from scipy import stats
|
|||
import statsmodels
|
||||
import statsmodels.api as sm
|
||||
from statsmodels.iolib.table import SimpleTable
|
||||
from statsmodels.miscmodels.ordinal_model import OrderedModel
|
||||
from statsmodels.stats.outliers_influence import variance_inflation_factor
|
||||
from tqdm import tqdm
|
||||
|
||||
|
|
@ -139,6 +140,7 @@ class RegressionResult:
|
|||
terms,
|
||||
llf, llnull,
|
||||
dof_resid, rsquared, f_statistic,
|
||||
comments,
|
||||
exp
|
||||
):
|
||||
#: Raw result from statsmodels *model.fit*
|
||||
|
|
@ -182,6 +184,9 @@ class RegressionResult:
|
|||
#: *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
|
||||
|
|
@ -255,7 +260,8 @@ class RegressionResult:
|
|||
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')))
|
||||
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))
|
||||
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 = []
|
||||
|
|
@ -345,6 +351,12 @@ class RegressionResult:
|
|||
|
||||
# TODO: Have a detailed view which shows SE, t/z, etc.
|
||||
|
||||
if self.comments:
|
||||
out += '<ol>'
|
||||
for comment in self.comments:
|
||||
out += '<li>{}</li>'.format(comment)
|
||||
out += '</ol>'
|
||||
|
||||
return out
|
||||
|
||||
def summary(self):
|
||||
|
|
@ -412,6 +424,11 @@ class RegressionResult:
|
|||
|
||||
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:
|
||||
|
|
@ -526,6 +543,8 @@ def regress(
|
|||
if exp is None:
|
||||
if model_class in (sm.Logit, sm.Poisson, PenalisedLogit):
|
||||
exp = True
|
||||
elif model_class is OrderedModel and model_kwargs.get('distr', 'probit') == 'logit':
|
||||
exp = True
|
||||
else:
|
||||
exp = False
|
||||
|
||||
|
|
@ -546,6 +565,11 @@ def regress(
|
|||
else:
|
||||
dmatrices = _dmatrices
|
||||
|
||||
if model_class is OrderedModel:
|
||||
# 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
|
||||
|
|
@ -577,6 +601,9 @@ def regress(
|
|||
# Intercept term (single term)
|
||||
term = '(Intercept)'
|
||||
terms[term] = SingleTerm(raw_name, beta, pvalues[raw_name])
|
||||
elif model_class is OrderedModel and '/' in raw_name:
|
||||
# Ignore ordinal regression intercepts
|
||||
pass
|
||||
else:
|
||||
# Parse if required
|
||||
factor, column, contrast = parse_patsy_term(formula, df, raw_name)
|
||||
|
|
@ -598,6 +625,12 @@ def regress(
|
|||
# Single term
|
||||
terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
|
||||
|
||||
# Handle ordinal regression intercepts
|
||||
#if model_class is OrderedModel:
|
||||
# intercept_names = [raw_name.split('/')[0] for raw_name in model.exog_names if '/' in raw_name]
|
||||
# intercepts = model.transform_threshold_params(result._results.params[-len(intercept_names):])
|
||||
# print(intercepts)
|
||||
|
||||
# Fit null model (for llnull)
|
||||
if hasattr(result, 'llnull'):
|
||||
llnull = result.llnull
|
||||
|
|
@ -628,13 +661,18 @@ def regress(
|
|||
if fit_kwargs.get('cov_type', 'nonrobust') != 'nonrobust':
|
||||
full_name = 'Robust {}'.format(full_name)
|
||||
|
||||
comments = []
|
||||
if model_class is OrderedModel:
|
||||
comments.append('Cutpoints are omitted from the table of model parameters.')
|
||||
|
||||
return RegressionResult(
|
||||
result,
|
||||
full_name, model_class.__name__, method_name,
|
||||
dep, result.nobs, result.df_model, datetime.now(), result.cov_type,
|
||||
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),
|
||||
comments,
|
||||
exp
|
||||
)
|
||||
|
||||
|
|
@ -731,6 +769,7 @@ def regress_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
|
||||
)
|
||||
|
||||
|
|
@ -848,5 +887,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
|
|||
terms,
|
||||
model['loglik'][0], model['loglik'][1],
|
||||
None, None, None,
|
||||
[],
|
||||
None # Set exp in regress()
|
||||
)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue