def _fit_pirls(self, alpha, start_params=None, maxiter=100, tol=1e-8,
scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None,
weights=None):
"""fit model with penalized reweighted least squares
"""
# TODO: this currently modifies several attributes
# self.scale, self.scaletype, self.mu, self.weights
# self.data_weights,
# and possibly self._offset_exposure
# several of those might not be necessary, e.g. mu and weights
# alpha = alpha * len(y) * self.scale / 100
# TODO: we need to rescale alpha
endog = self.endog
wlsexog = self.exog # smoother.basis
spl_s = self.penal.penalty_matrix(alpha=alpha)
nobs, n_columns = wlsexog.shape
# TODO what are these values?
if weights is None:
self.data_weights = np.array([1.] * nobs)
else:
self.data_weights = weights
if not hasattr(self, '_offset_exposure'):
self._offset_exposure = 0
self.scaletype = scale
# TODO: check default scale types
# self.scaletype = 'dev'
# during iteration
self.scale = 1
if start_params is None:
mu = self.family.starting_mu(endog)
lin_pred = self.family.predict(mu)
else:
lin_pred = np.dot(wlsexog, start_params) + self._offset_exposure
mu = self.family.fitted(lin_pred)
dev = self.family.deviance(endog, mu)
history = dict(params=[None, start_params], deviance=[np.inf, dev])
converged = False
criterion = history['deviance']
# This special case is used to get the likelihood for a specific
# params vector.
if maxiter == 0:
mu = self.family.fitted(lin_pred)
self.scale = self.estimate_scale(mu)
wls_results = lm.RegressionResults(self, start_params, None)
iteration = 0
for iteration in range(maxiter):
# TODO: is this equivalent to point 1 of page 136:
# w = 1 / (V(mu) * g'(mu)) ?
self.weights = self.data_weights * self.family.weights(mu)
# TODO: is this equivalent to point 1 of page 136:
# z = g(mu)(y - mu) + X beta ?
wlsendog = (lin_pred + self.family.link.deriv(mu) * (endog - mu)
- self._offset_exposure)
# this defines the augmented matrix point 2a on page 136
wls_results = penalized_wls(wlsendog, wlsexog, spl_s, self.weights)
lin_pred = np.dot(wlsexog, wls_results.params).ravel()
lin_pred += self._offset_exposure
mu = self.family.fitted(lin_pred)
# We don't need to update scale in GLM/LEF models
# We might need it in dispersion models.
# self.scale = self.estimate_scale(mu)
history = self._update_history(wls_results, mu, history)
if endog.squeeze().ndim == 1 and np.allclose(mu - endog, 0):
msg = "Perfect separation detected, results not available"
raise PerfectSeparationError(msg)
# TODO need atol, rtol
# args of _check_convergence: (criterion, iteration, atol, rtol)
converged = _check_convergence(criterion, iteration, tol, 0)
if converged:
break
self.mu = mu
self.scale = self.estimate_scale(mu)
glm_results = GLMGamResults(self, wls_results.params,
wls_results.normalized_cov_params,
self.scale,
cov_type=cov_type, cov_kwds=cov_kwds,
use_t=use_t)
glm_results.method = "PIRLS"
history['iteration'] = iteration + 1
glm_results.fit_history = history
glm_results.converged = converged
return GLMGamResultsWrapper(glm_results)
def _fit_pirls(self, alpha, start_params=None, maxiter=100, tol=1e-8,
scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None,
weights=None):
"""fit model with penalized reweighted least squares
"""
# TODO: this currently modifies several attributes
# self.scale, self.scaletype, self.mu, self.weights
# self.data_weights,
# and possibly self._offset_exposure
# several of those might not be necessary, e.g. mu and weights
# alpha = alpha * len(y) * self.scale / 100
# TODO: we need to rescale alpha
endog = self.endog
wlsexog = self.exog # smoother.basis
spl_s = self.penal.penalty_matrix(alpha=alpha)
nobs, n_columns = wlsexog.shape
# TODO what are these values?
if weights is None:
self.data_weights = np.array([1.] * nobs)
else:
self.data_weights = weights
if not hasattr(self, '_offset_exposure'):
self._offset_exposure = 0
self.scaletype = scale
# TODO: check default scale types
# self.scaletype = 'dev'
# during iteration
self.scale = 1
if start_params is None:
mu = self.family.starting_mu(endog)
lin_pred = self.family.predict(mu)
else:
lin_pred = np.dot(wlsexog, start_params) + self._offset_exposure
mu = self.family.fitted(lin_pred)
dev = self.family.deviance(endog, mu)
history = dict(params=[None, start_params], deviance=[np.inf, dev])
converged = False
criterion = history['deviance']
# This special case is used to get the likelihood for a specific
# params vector.
if maxiter == 0:
mu = self.family.fitted(lin_pred)
self.scale = self.estimate_scale(mu)
wls_results = lm.RegressionResults(self, start_params, None)
iteration = 0
for iteration in range(maxiter):
# TODO: is this equivalent to point 1 of page 136:
# w = 1 / (V(mu) * g'(mu)) ?
self.weights = self.data_weights * self.family.weights(mu)
# TODO: is this equivalent to point 1 of page 136:
# z = g(mu)(y - mu) + X beta ?
wlsendog = (lin_pred + self.family.link.deriv(mu) * (endog - mu)
- self._offset_exposure)
# this defines the augmented matrix point 2a on page 136
wls_results = penalized_wls(wlsendog, wlsexog, spl_s, self.weights)
lin_pred = np.dot(wlsexog, wls_results.params).ravel()
lin_pred += self._offset_exposure
mu = self.family.fitted(lin_pred)
# We don't need to update scale in GLM/LEF models
# We might need it in dispersion models.
# self.scale = self.estimate_scale(mu)
history = self._update_history(wls_results, mu, history)
if endog.squeeze().ndim == 1 and np.allclose(mu - endog, 0):
msg = "Perfect separation detected, results not available"
raise PerfectSeparationError(msg)
# TODO need atol, rtol
# args of _check_convergence: (criterion, iteration, atol, rtol)
converged = _check_convergence(criterion, iteration, tol, 0)
if converged:
break
self.mu = mu
self.scale = self.estimate_scale(mu)
glm_results = GLMGamResults(self, wls_results.params,
wls_results.normalized_cov_params,
self.scale,
cov_type=cov_type, cov_kwds=cov_kwds,
use_t=use_t)
glm_results.method = "PIRLS"
history['iteration'] = iteration + 1
glm_results.fit_history = history
glm_results.converged = converged
return GLMGamResultsWrapper(glm_results)