本文整理汇总了Python中statsmodels.base.covtype.get_robustcov_results函数的典型用法代码示例。如果您正苦于以下问题：Python get_robustcov_results函数的具体用法？Python get_robustcov_results怎么用？Python get_robustcov_results使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了get_robustcov_results函数的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setup_class
def setup_class(cls):
cls.res2 = results_st.results_poisson_clu
mod = smd.Poisson(endog, exog)
cls.res1 = res1 = mod.fit(disp=False)
debug = False
if debug:
# for debugging
cls.bse_nonrobust = cls.res1.bse.copy()
cls.res1 = res1 = mod.fit(disp=False)
cls.get_robust_clu()
cls.res3 = cls.res1
cls.bse_rob3 = cls.bse_rob.copy()
cls.res1 = res1 = mod.fit(disp=False)
from statsmodels.base.covtype import get_robustcov_results
#res_hc0_ = cls.res1.get_robustcov_results('HC1')
get_robustcov_results(cls.res1._results, 'cluster',
groups=group,
use_correction=True,
df_correction=True, #TODO has no effect
use_t=False, #True,
use_self=True)
cls.bse_rob = cls.res1.bse
nobs, k_vars = res1.model.exog.shape
k_params = len(res1.params)
#n_groups = len(np.unique(group))
corr_fact = (nobs-1.) / float(nobs - k_params)
# for bse we need sqrt of correction factor
cls.corr_fact = np.sqrt(corr_fact)示例2: setup_class
def setup_class(cls):
cls.res2 = results_st.results_poisson_clu
mod = smd.Poisson(endog, exog)
cls.res1 = res1 = mod.fit(disp=False)
debug = False
if debug:
# for debugging
cls.bse_nonrobust = cls.res1.bse.copy()
cls.res1 = res1 = mod.fit(disp=False)
cls.get_robust_clu()
cls.res3 = cls.res1
cls.bse_rob3 = cls.bse_rob.copy()
cls.res1 = res1 = mod.fit(disp=False)
from statsmodels.base.covtype import get_robustcov_results
#res_hc0_ = cls.res1.get_robustcov_results('HC1')
get_robustcov_results(cls.res1._results, 'cluster',
groups=group,
use_correction=True,
df_correction=True, #TODO has no effect
use_t=False, #True,
use_self=True)
cls.bse_rob = cls.res1.bse
cls.corr_fact = cls.get_correction_factor(cls.res1)示例3: __init__
def __init__(self, model, params, normalized_cov_params=None, scale=1.,
**kwargs):
super(LikelihoodModelResults, self).__init__(model, params)
self.normalized_cov_params = normalized_cov_params
self.scale = scale
# robust covariance
# We put cov_type in kwargs so subclasses can decide in fit whether to
# use this generic implementation
if 'use_t' in kwargs:
use_t = kwargs['use_t']
if use_t is not None:
self.use_t = use_t
if 'cov_type' in kwargs:
cov_type = kwargs.get('cov_type', 'nonrobust')
cov_kwds = kwargs.get('cov_kwds', {})
if cov_type == 'nonrobust':
self.cov_type = 'nonrobust'
self.cov_kwds = {'description': 'Standard Errors assume that the ' +
'covariance matrix of the errors is correctly ' +
'specified.'}
else:
from statsmodels.base.covtype import get_robustcov_results
if cov_kwds is None:
cov_kwds = {}
use_t = self.use_t
# TODO: we shouldn't need use_t in get_robustcov_results
get_robustcov_results(self, cov_type=cov_type, use_self=True,
use_t=use_t, **cov_kwds)示例4: _get_robustcov_results
def _get_robustcov_results(self, cov_type='nonrobust', use_self=True,
use_t=None, **cov_kwds):
from statsmodels.base.covtype import get_robustcov_results
if cov_kwds is None:
cov_kwds = {}
if cov_type == 'nonrobust':
self.cov_type = 'nonrobust'
self.cov_kwds = {'description': 'Standard Errors assume that the ' +
'covariance matrix of the errors is correctly ' +
'specified.'}
else:
# TODO: we shouldn't need use_t in get_robustcov_results
get_robustcov_results(self, cov_type=cov_type, use_self=True,
use_t=use_t, **cov_kwds)示例5: __init__
def __init__(self, model, params, normalized_cov_params, scale,
cov_type='nonrobust', cov_kwds=None, use_t=None):
super(GLMResults, self).__init__(model, params,
normalized_cov_params=
normalized_cov_params, scale=scale)
self.family = model.family
self._endog = model.endog
self.nobs = model.endog.shape[0]
self.mu = model.mu
self._data_weights = model.data_weights
self.df_resid = model.df_resid
self.df_model = model.df_model
self.pinv_wexog = model.pinv_wexog
self._cache = resettable_cache()
# are these intermediate results needed or can we just
# call the model's attributes?
# for remove data and pickle without large arrays
self._data_attr.extend(['results_constrained'])
self.data_in_cache = getattr(self, 'data_in_cache', [])
self.data_in_cache.extend(['null'])
# robust covariance
from statsmodels.base.covtype import get_robustcov_results
if use_t is None:
self.use_t = False # TODO: class default
else:
self.use_t = use_t
if cov_type == 'nonrobust':
self.cov_type = 'nonrobust'
self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
'covariance matrix of the errors is correctly ' +
'specified.'}
else:
if cov_kwds is None:
cov_kwds = {}
get_robustcov_results(self, cov_type=cov_type, use_self=True,
use_t=use_t, **cov_kwds)