def loglikelihood(self, restricted=False, vcs=None, vmat=None):
"""
Returns the loglikelihood of the model with the current model
parameters
:returns: loglikelihood
:rtype: float
"""
if self.mle is not None and vcs is None and vmat is None:
if restricted:
return self.mle.restricted_loglikelihood
else:
return self.mle.full_loglikelihood
if vcs is not None:
V = self._makeV(vcs=vcs)
elif vmat is not None:
V = vmat
elif self.V is None:
self.V = self._makeV()
else:
V = self.V
if not restricted:
return full_loglikelihood(self.y, V, self.X, self.beta)
else:
return REML(self).loglikelihood()
def loglikelihood(self, restricted=False, vcs=None, vmat=None):
"""
Returns the loglikelihood of the model with the current model
parameters
:returns: loglikelihood
:rtype: float
"""
if self.mle is not None and vcs is None and vmat is None:
if restricted:
return self.mle.restricted_loglikelihood
else:
return self.mle.full_loglikelihood
if vcs is not None:
V = self._makeV(vcs=vcs)
elif vmat is not None:
V = vmat
elif self.V is None:
self.V = self._makeV()
else:
V = self.V
if not restricted:
return full_loglikelihood(self.y, V, self.X, self.beta)
else:
return REML(self).loglikelihood()
def maximize(self,
method="Average Information",
restricted=False,
starts=None,
verbose=False):
"""
Finds the optimal values for variance components in the model using
provided optimization methods.
:param restricted: Uses REML estimation
:param starts: starting values for the variance components
:param method: maximization method
:param verbose: output maximization progress
:type restricted: bool
:type method: string
:type starts: iterable of numerics
:type verbose: bool:
"""
if (isinstance(self.mle, MLEResult)
and self.maximized.method == method):
return
self.fit_model()
if starts is None:
starts = self._starting_variance_components()
likefunc = REML if restricted else ML
llik = likefunc(self, info=method)
llik.set_parameters(starts)
# if method.lower().startswith('minque'):
# mle = minque(self, value=0, verbose=verbose, starts=starts)
if method.lower() in {'em', 'emreml', 'expectation-maximization'}:
mle = expectation_maximization(self, llik, verbose=verbose)
elif method.lower() == 'grid':
mle = grid_search(self, llik, nevals=20, oob=False)
else:
mle = newtonlike_maximization(self, llik, verbose=verbose)
self.mle = mle
self.set_variance_components(mle.parameters)
self.fit_model()
# Get the full loglikelihood at the REML maximimum so we
# can use it later
self.mle.full_loglikelihood = full_loglikelihood(
self.y, self.V, self.X, self.beta)
def maximize(self, method="Average Information", restricted=False,
starts=None, verbose=False):
"""
Finds the optimal values for variance components in the model using
provided optimization methods.
:param restricted: Uses REML estimation
:param starts: starting values for the variance components
:param method: maximization method
:param verbose: output maximization progress
:type restricted: bool
:type method: string
:type starts: iterable of numerics
:type verbose: bool:
"""
if (isinstance(self.mle, MLEResult) and
self.maximized.method == method):
return
self.fit_model()
if starts is None:
starts = self._starting_variance_components()
likefunc = REML if restricted else ML
llik = likefunc(self, info=method)
llik.set_parameters(starts)
# if method.lower().startswith('minque'):
# mle = minque(self, value=0, verbose=verbose, starts=starts)
if method.lower() in {'em', 'emreml', 'expectation-maximization'}:
mle = expectation_maximization(self, llik, verbose=verbose)
elif method.lower() == 'grid':
mle = grid_search(self, llik, nevals=20, oob=False)
else:
mle = newtonlike_maximization(self, llik, verbose=verbose)
self.mle = mle
self.set_variance_components(mle.parameters)
self.fit_model()
# Get the full loglikelihood at the REML maximimum so we
# can use it later
self.mle.full_loglikelihood = full_loglikelihood(self.y, self.V,
self.X, self.beta)
def maximize(self, method="Average Information", restricted=False,
starts=None, verbose=False):
"""
Finds the optimal values for variance components of the model by
restricted maximum likelihood estimation.
"""
if (isinstance(self.mle, MLEResult) and
self.maximized.method == method):
return
self.fit_model()
if starts is None:
starts = self._starting_variance_components()
likefunc = REML if restricted else ML
llik = likefunc(self, info=method)
llik.set_parameters(starts)
if method.lower().startswith('minque'):
mle = minque(self, value=0, verbose=verbose, starts=starts)
elif method.lower() in {'em', 'emreml', 'expectation-maximization'}:
mle = expectation_maximization(self, llik, verbose=verbose)
elif method.lower() == 'grid':
mle = grid_search(self, llik, nevals=20, oob=False)
else:
mle = newtonlike_maximization(self, llik, verbose=verbose)
self.mle = mle
self.set_variance_components(mle.parameters)
self.fit_model()
# Get the full loglikelihood at the REML maximimum so we
# can use it later
self.mle.full_loglikelihood = full_loglikelihood(self.y, self.V,
self.X, self.beta)
