def __init__(self, regs, glmfit_kwargs=None, **kwargs):
"""
Parameters
----------
regs : list
Names of sample attributes to be extracted from an input dataset and
used as design matrix columns.
glmfit_kwargs : dict, optional
Keyword arguments to be passed to GeneralLinearModel.fit().
By default an AR1 model is used.
"""
GLMMapper.__init__(self, regs, **kwargs)
if glmfit_kwargs is None:
glmfit_kwargs = {}
self.glmfit_kwargs = glmfit_kwargs
def __init__(self, regs, glmfit_kwargs=None, **kwargs):
"""
Parameters
----------
regs : list
Names of sample attributes to be extracted from an input dataset and
used as design matrix columns.
glmfit_kwargs : dict, optional
Keyword arguments to be passed to GeneralLinearModel.fit().
By default an AR1 model is used.
"""
GLMMapper.__init__(self, regs, **kwargs)
if glmfit_kwargs is None:
glmfit_kwargs = {}
self.glmfit_kwargs = glmfit_kwargs
def __init__(self, regs, model_gen=None, results='params', **kwargs):
"""
Parameters
----------
regs : list
Names of sample attributes to be extracted from an input dataset and
used as design matrix columns.
model_gen : callable, optional
See UnivariateStatsModels documentation for details on the
specification of the model fitting procedure. By default an
OLS model is used.
results : str or array, optional
See UnivariateStatsModels documentation for details on the
specification of model fit results. By default parameter
estimates are returned.
"""
GLMMapper.__init__(self, regs, **kwargs)
self.result_expr = results
if model_gen is None:
model_gen = lambda y, x: sm.OLS(y, x)
self.model_gen = model_gen
def __init__(self, regs, model_gen=None, results="params", **kwargs):
"""
Parameters
----------
regs : list
Names of sample attributes to be extracted from an input dataset and
used as design matrix columns.
model_gen : callable, optional
See UnivariateStatsModels documentation for details on the
specification of the model fitting procedure. By default an
OLS model is used.
results : str or array, optional
See UnivariateStatsModels documentation for details on the
specification of model fit results. By default parameter
estimates are returned.
"""
GLMMapper.__init__(self, regs, **kwargs)
self.result_expr = results
if model_gen is None:
model_gen = lambda y, x: sm.OLS(y, x)
self.model_gen = model_gen