"""
Generalized linear models currently supports estimation using the one-parameter
exponential families
References
----------
Gill, Jeff. 2000. Generalized Linear Models: A Unified Approach.
SAGE QASS Series.
Green, PJ. 1984. "Iteratively reweighted least squares for maximum
likelihood estimation, and some robust and resistant alternatives."
Journal of the Royal Statistical Society, Series B, 46, 149-192.
Hardin, J.W. and Hilbe, J.M. 2007. "Generalized Linear Models and
Extensions." 2nd ed. Stata Press, College Station, TX.
McCullagh, P. and Nelder, J.A. 1989. "Generalized Linear Models." 2nd ed.
Chapman & Hall, Boca Rotan.
"""
from statsmodels.compat.numpy import np_matrix_rank
import numpy as np
from . import families
from statsmodels.tools.decorators import cache_readonly, resettable_cache
import statsmodels.base.model as base
import statsmodels.regression.linear_model as lm
import statsmodels.base.wrapper as wrap
import statsmodels.regression._tools as reg_tools
from statsmodels.graphics._regressionplots_doc import (
_plot_added_variable_doc,
_plot_partial_residuals_doc,
_plot_ceres_residuals_doc)
# need import in module instead of lazily to copy `__doc__`
from . import _prediction as pred
from statsmodels.genmod._prediction import PredictionResults
from statsmodels.tools.sm_exceptions import (PerfectSeparationError,
DomainWarning)
__all__ = ['GLM', 'PredictionResults']
def _check_convergence(criterion, iteration, atol, rtol):
return np.allclose(criterion[iteration], criterion[iteration + 1],
atol=atol, rtol=rtol)
[docs]class GLM(base.LikelihoodModel):
__doc__ = """
Generalized Linear Models class
GLM inherits from statsmodels.base.model.LikelihoodModel
Parameters
-----------
endog : array-like
1d array of endogenous response variable. This array can be 1d or 2d.
Binomial family models accept a 2d array with two columns. If
supplied, each observation is expected to be [success, failure].
exog : array-like
A nobs x k array where `nobs` is the number of observations and `k`
is the number of regressors. An intercept is not included by default
and should be added by the user (models specified using a formula
include an intercept by default). See `statsmodels.tools.add_constant`.
family : family class instance
The default is Gaussian. To specify the binomial distribution
family = sm.family.Binomial()
Each family can take a link instance as an argument. See
statsmodels.family.family for more information.
offset : array-like or None
An offset to be included in the model. If provided, must be
an array whose length is the number of rows in exog.
exposure : array-like or None
Log(exposure) will be added to the linear prediction in the model.
Exposure is only valid if the log link is used. If provided, it must be
an array with the same length as endog.
freq_weights : array-like
1d array of frequency weights. The default is None. If None is selected
or a blank value, then the algorithm will replace with an array of 1's
with length equal to the endog.
WARNING: Using weights is not verified yet for all possible options
and results, see Notes.
var_weights : array-like
1d array of variance (analytic) weights. The default is None. If None
is selected or a blank value, then the algorithm will replace with an
array of 1's with length equal to the endog.
WARNING: Using weights is not verified yet for all possible options
and results, see Notes.
%(extra_params)s
Attributes
-----------
df_model : float
`p` - 1, where `p` is the number of regressors including the intercept.
df_resid : float
The number of observation `n` minus the number of regressors `p`.
endog : array
See Parameters.
exog : array
See Parameters.
family : family class instance
A pointer to the distribution family of the model.
freq_weights : array
See Parameters.
var_weights : array
See Parameters.
mu : array
The estimated mean response of the transformed variable.
n_trials : array
See Parameters.
normalized_cov_params : array
`p` x `p` normalized covariance of the design / exogenous data.
scale : float
The estimate of the scale / dispersion. Available after fit is called.
scaletype : str
The scaling used for fitting the model. Available after fit is called.
weights : array
The value of the weights after the last iteration of fit.
Examples
--------
>>> import statsmodels.api as sm
>>> data = sm.datasets.scotland.load()
>>> data.exog = sm.add_constant(data.exog)
Instantiate a gamma family model with the default link function.
>>> gamma_model = sm.GLM(data.endog, data.exog,
... family=sm.families.Gamma())
>>> gamma_results = gamma_model.fit()
>>> gamma_results.params
array([-0.01776527, 0.00004962, 0.00203442, -0.00007181, 0.00011185,
-0.00000015, -0.00051868, -0.00000243])
>>> gamma_results.scale
0.0035842831734919055
>>> gamma_results.deviance
0.087388516416999198
>>> gamma_results.pearson_chi2
0.086022796163805704
>>> gamma_results.llf
-83.017202161073527
See also
--------
statsmodels.genmod.families.family.Family
:ref:`families`
:ref:`links`
Notes
-----
Only the following combinations make sense for family and link:
============= ===== === ===== ====== ======= === ==== ====== ====== ====
Family ident log logit probit cloglog pow opow nbinom loglog logc
============= ===== === ===== ====== ======= === ==== ====== ====== ====
Gaussian x x x x x x x x x
inv Gaussian x x x
binomial x x x x x x x x x
Poission x x x
neg binomial x x x x
gamma x x x
Tweedie x x x
============= ===== === ===== ====== ======= === ==== ====== ====== ====
Not all of these link functions are currently available.
Endog and exog are references so that if the data they refer to are already
arrays and these arrays are changed, endog and exog will change.
Statsmodels supports two separte definitions of weights: frequency weights
and variance weights.
Frequency weights produce the same results as repeating observations by the
frequencies (if those are integers). Frequency weights will keep the number
of observations consistent, but the degrees of freedom will change to
reflect the new weights.
Variance weights (referred to in other packages as analytic weights) are
used when ``endog`` represents an an average or mean. This relies on the
assumption that that the inverse variance scales proportionally to the
weight--an observation that is deemed more credible should have less
variance and therefore have more weight. For the ``Poisson`` family--which
assumes that occurences scale proportionally with time--a natural practice
would be to use the amount of time as the variance weight and set ``endog``
to be a rate (occurrances per period of time). Similarly, using a
compound Poisson family, namely ``Tweedie``, makes a similar assumption
about the rate (or frequency) of occurences having variance proportional to
time.
Both frequency and variance weights are verified for all basic results with
nonrobust or heteroscedasticity robust ``cov_type``. Other robust
covariance types have not yet been verified, and at least the small sample
correction is currently not based on the correct total frequency count.
Currently, all residuals are not weighted by frequency, although they may
incorporate ``n_trials`` for ``Binomial`` and ``var_weights``
+---------------+----------------------------------+
| Residual Type | Applicable weights |
+===============+==================================+
| Anscombe | ``var_weights`` |
+---------------+----------------------------------+
| Deviance | ``var_weights`` |
+---------------+----------------------------------+
| Pearson | ``var_weights`` and ``n_trials`` |
+---------------+----------------------------------+
| Reponse | ``n_trials`` |
+---------------+----------------------------------+
| Working | ``n_trials`` |
+---------------+----------------------------------+
WARNING: Loglikelihood and deviance are not valid in models where
scale is equal to 1 (i.e., ``Binomial``, ``NegativeBinomial``, and
``Poisson``). If variance weights are specified, then results such as
``loglike`` and ``deviance`` are based on a quasi-likelihood
interpretation. The loglikelihood is not correctly specified in this case,
and statistics based on it, such AIC or likelihood ratio tests, are not
appropriate.
Attributes
----------
df_model : float
Model degrees of freedom is equal to p - 1, where p is the number
of regressors. Note that the intercept is not reported as a
degree of freedom.
df_resid : float
Residual degrees of freedom is equal to the number of observation n
minus the number of regressors p.
endog : array
See above. Note that `endog` is a reference to the data so that if
data is already an array and it is changed, then `endog` changes
as well.
exposure : array-like
Include ln(exposure) in model with coefficient constrained to 1. Can
only be used if the link is the logarithm function.
exog : array
See above. Note that `exog` is a reference to the data so that if
data is already an array and it is changed, then `exog` changes
as well.
freq_weights : array
See above. Note that `freq_weights` is a reference to the data so that
if data is already an array and it is changed, then `freq_weights`
changes as well.
var_weights : array
See above. Note that `var_weights` is a reference to the data so that
if data is already an array and it is changed, then `var_weights`
changes as well.
iteration : int
The number of iterations that fit has run. Initialized at 0.
family : family class instance
The distribution family of the model. Can be any family in
statsmodels.families. Default is Gaussian.
mu : array
The mean response of the transformed variable. `mu` is the value of
the inverse of the link function at lin_pred, where lin_pred is the
linear predicted value of the WLS fit of the transformed variable.
`mu` is only available after fit is called. See
statsmodels.families.family.fitted of the distribution family for more
information.
n_trials : array
See above. Note that `n_trials` is a reference to the data so that if
data is already an array and it is changed, then `n_trials` changes
as well. `n_trials` is the number of binomial trials and only available
with that distribution. See statsmodels.families.Binomial for more
information.
normalized_cov_params : array
The p x p normalized covariance of the design / exogenous data.
This is approximately equal to (X.T X)^(-1)
offset : array-like
Include offset in model with coefficient constrained to 1.
scale : float
The estimate of the scale / dispersion of the model fit. Only
available after fit is called. See GLM.fit and GLM.estimate_scale
for more information.
scaletype : str
The scaling used for fitting the model. This is only available after
fit is called. The default is None. See GLM.fit for more information.
weights : array
The value of the weights after the last iteration of fit. Only
available after fit is called. See statsmodels.families.family for
the specific distribution weighting functions.
""" % {'extra_params': base._missing_param_doc}
def __init__(self, endog, exog, family=None, offset=None,
exposure=None, freq_weights=None, var_weights=None,
missing='none', **kwargs):
if (family is not None) and not isinstance(family.link,
tuple(family.safe_links)):
import warnings
warnings.warn(("The %s link function does not respect the domain "
"of the %s family.") %
(family.link.__class__.__name__,
family.__class__.__name__),
DomainWarning)
if exposure is not None:
exposure = np.log(exposure)
if offset is not None: # this should probably be done upstream
offset = np.asarray(offset)
if freq_weights is not None:
freq_weights = np.asarray(freq_weights)
if var_weights is not None:
var_weights = np.asarray(var_weights)
self.freq_weights = freq_weights
self.var_weights = var_weights
super(GLM, self).__init__(endog, exog, missing=missing,
offset=offset, exposure=exposure,
freq_weights=freq_weights,
var_weights=var_weights, **kwargs)
self._check_inputs(family, self.offset, self.exposure, self.endog,
self.freq_weights, self.var_weights)
if offset is None:
delattr(self, 'offset')
if exposure is None:
delattr(self, 'exposure')
self.nobs = self.endog.shape[0]
# things to remove_data
self._data_attr.extend(['weights', 'mu', 'freq_weights',
'var_weights', 'iweights', '_offset_exposure',
'n_trials'])
# register kwds for __init__, offset and exposure are added by super
self._init_keys.append('family')
self._setup_binomial()
# internal usage for recreating a model
if 'n_trials' in kwargs:
self.n_trials = kwargs['n_trials']
# Construct a combined offset/exposure term. Note that
# exposure has already been logged if present.
offset_exposure = 0.
if hasattr(self, 'offset'):
offset_exposure = self.offset
if hasattr(self, 'exposure'):
offset_exposure = offset_exposure + self.exposure
self._offset_exposure = offset_exposure
self.scaletype = None
[docs] def initialize(self):
"""
Initialize a generalized linear model.
"""
# TODO: intended for public use?
self.history = {'fittedvalues': [],
'params': [np.inf],
'deviance': [np.inf]}
self.df_model = np_matrix_rank(self.exog) - 1
if (self.freq_weights is not None) and \
(self.freq_weights.shape[0] == self.endog.shape[0]):
self.wnobs = self.freq_weights.sum()
self.df_resid = self.wnobs - self.df_model - 1
else:
self.wnobs = self.exog.shape[0]
self.df_resid = self.exog.shape[0] - self.df_model - 1
def _check_inputs(self, family, offset, exposure, endog, freq_weights,
var_weights):
# Default family is Gaussian
if family is None:
family = families.Gaussian()
self.family = family
if exposure is not None:
if not isinstance(self.family.link, families.links.Log):
raise ValueError("exposure can only be used with the log "
"link function")
elif exposure.shape[0] != endog.shape[0]:
raise ValueError("exposure is not the same length as endog")
if offset is not None:
if offset.shape[0] != endog.shape[0]:
raise ValueError("offset is not the same length as endog")
if freq_weights is not None:
if freq_weights.shape[0] != endog.shape[0]:
raise ValueError("freq weights not the same length as endog")
if len(freq_weights.shape) > 1:
raise ValueError("freq weights has too many dimensions")
# internal flag to store whether freq_weights were not None
self._has_freq_weights = (self.freq_weights is not None)
if self.freq_weights is None:
self.freq_weights = np.ones((endog.shape[0]))
# TODO: check do we want to keep None as sentinel for freq_weights
if np.shape(self.freq_weights) == () and self.freq_weights > 1:
self.freq_weights = (self.freq_weights *
np.ones((endog.shape[0])))
if var_weights is not None:
if var_weights.shape[0] != endog.shape[0]:
raise ValueError("var weights not the same length as endog")
if len(var_weights.shape) > 1:
raise ValueError("var weights has too many dimensions")
# internal flag to store whether var_weights were not None
self._has_var_weights = (var_weights is not None)
if var_weights is None:
self.var_weights = np.ones((endog.shape[0]))
# TODO: check do we want to keep None as sentinel for var_weights
self.iweights = np.asarray(self.freq_weights * self.var_weights)
def _get_init_kwds(self):
# this is a temporary fixup because exposure has been transformed
# see #1609, copied from discrete_model.CountModel
kwds = super(GLM, self)._get_init_kwds()
if 'exposure' in kwds and kwds['exposure'] is not None:
kwds['exposure'] = np.exp(kwds['exposure'])
return kwds
[docs] def loglike_mu(self, mu, scale=1.):
"""
Evaluate the log-likelihood for a generalized linear model.
"""
return self.family.loglike(self.endog, mu, self.var_weights,
self.freq_weights, scale)
[docs] def loglike(self, params, scale=None):
"""
Evaluate the log-likelihood for a generalized linear model.
"""
lin_pred = np.dot(self.exog, params) + self._offset_exposure
expval = self.family.link.inverse(lin_pred)
if scale is None:
scale = self.estimate_scale(expval)
llf = self.family.loglike(self.endog, expval, self.var_weights,
self.freq_weights, scale)
return llf
[docs] def score_obs(self, params, scale=None):
"""score first derivative of the loglikelihood for each observation.
Parameters
----------
params : ndarray
parameter at which score is evaluated
scale : None or float
If scale is None, then the default scale will be calculated.
Default scale is defined by `self.scaletype` and set in fit.
If scale is not None, then it is used as a fixed scale.
Returns
-------
score_obs : ndarray, 2d
The first derivative of the loglikelihood function evaluated at
params for each observation.
"""
score_factor = self.score_factor(params, scale=scale)
return score_factor[:, None] * self.exog
[docs] def score(self, params, scale=None):
"""score, first derivative of the loglikelihood function
Parameters
----------
params : ndarray
parameter at which score is evaluated
scale : None or float
If scale is None, then the default scale will be calculated.
Default scale is defined by `self.scaletype` and set in fit.
If scale is not None, then it is used as a fixed scale.
Returns
-------
score : ndarray_1d
The first derivative of the loglikelihood function calculated as
the sum of `score_obs`
"""
score_factor = self.score_factor(params, scale=scale)
return np.dot(score_factor, self.exog)
[docs] def score_factor(self, params, scale=None):
"""weights for score for each observation
This can be considered as score residuals.
Parameters
----------
params : ndarray
parameter at which score is evaluated
scale : None or float
If scale is None, then the default scale will be calculated.
Default scale is defined by `self.scaletype` and set in fit.
If scale is not None, then it is used as a fixed scale.
Returns
-------
score_factor : ndarray_1d
A 1d weight vector used in the calculation of the score_obs.
The score_obs are obtained by `score_factor[:, None] * exog`
"""
mu = self.predict(params)
if scale is None:
scale = self.estimate_scale(mu)
score_factor = (self.endog - mu) / self.family.link.deriv(mu)
score_factor /= self.family.variance(mu)
score_factor *= self.iweights * self.n_trials
if not scale == 1:
score_factor /= scale
return score_factor
[docs] def hessian_factor(self, params, scale=None, observed=True):
"""Weights for calculating Hessian
Parameters
----------
params : ndarray
parameter at which Hessian is evaluated
scale : None or float
If scale is None, then the default scale will be calculated.
Default scale is defined by `self.scaletype` and set in fit.
If scale is not None, then it is used as a fixed scale.
observed : bool
If True, then the observed Hessian is returned. If false then the
expected information matrix is returned.
Returns
-------
hessian_factor : ndarray, 1d
A 1d weight vector used in the calculation of the Hessian.
The hessian is obtained by `(exog.T * hessian_factor).dot(exog)`
"""
# calculating eim_factor
mu = self.predict(params)
if scale is None:
scale = self.estimate_scale(mu)
eim_factor = 1 / (self.family.link.deriv(mu)**2 *
self.family.variance(mu))
eim_factor *= self.iweights * self.n_trials
if not observed:
if not scale == 1:
eim_factor /= scale
return eim_factor
# calculating oim_factor, eim_factor is with scale=1
score_factor = self.score_factor(params, scale=1.)
if eim_factor.ndim > 1 or score_factor.ndim > 1:
raise RuntimeError('something wrong')
tmp = self.family.variance(mu) * self.family.link.deriv2(mu)
tmp += self.family.variance.deriv(mu) * self.family.link.deriv(mu)
tmp = score_factor * tmp
# correct for duplicatee iweights in oim_factor and score_factor
tmp /= self.iweights * self.n_trials
oim_factor = eim_factor * (1 + tmp)
if tmp.ndim > 1:
raise RuntimeError('something wrong')
if not scale == 1:
oim_factor /= scale
return oim_factor
[docs] def hessian(self, params, scale=None, observed=None):
"""Hessian, second derivative of loglikelihood function
Parameters
----------
params : ndarray
parameter at which Hessian is evaluated
scale : None or float
If scale is None, then the default scale will be calculated.
Default scale is defined by `self.scaletype` and set in fit.
If scale is not None, then it is used as a fixed scale.
observed : bool
If True, then the observed Hessian is returned (default).
If false then the expected information matrix is returned.
Returns
-------
hessian : ndarray
Hessian, i.e. observed information, or expected information matrix.
"""
if observed is None:
if getattr(self, '_optim_hessian', None) == 'eim':
observed = False
else:
observed = True
factor = self.hessian_factor(params, scale=scale, observed=observed)
hess = -np.dot(self.exog.T * factor, self.exog)
return hess
[docs] def score_test(self, params_constrained, k_constraints=None,
exog_extra=None, observed=True):
"""score test for restrictions or for omitted variables
The covariance matrix for the score is based on the Hessian, i.e.
observed information matrix or optionally on the expected information
matrix..
Parameters
----------
params_constrained : array_like
estimated parameter of the restricted model. This can be the
parameter estimate for the current when testing for omitted
variables.
k_constraints : int or None
Number of constraints that were used in the estimation of params
restricted relative to the number of exog in the model.
This must be provided if no exog_extra are given. If exog_extra is
not None, then k_constraints is assumed to be zero if it is None.
exog_extra : None or array_like
Explanatory variables that are jointly tested for inclusion in the
model, i.e. omitted variables.
observed : bool
If True, then the observed Hessian is used in calculating the
covariance matrix of the score. If false then the expected
information matrix is used.
Returns
-------
chi2_stat : float
chisquare statistic for the score test
p-value : float
P-value of the score test based on the chisquare distribution.
df : int
Degrees of freedom used in the p-value calculation. This is equal
to the number of constraints.
Notes
-----
not yet verified for case with scale not equal to 1.
"""
if exog_extra is None:
if k_constraints is None:
raise ValueError('if exog_extra is None, then k_constraints'
'needs to be given')
score = self.score(params_constrained)
hessian = self.hessian(params_constrained, observed=observed)
else:
# exog_extra = np.asarray(exog_extra)
if k_constraints is None:
k_constraints = 0
ex = np.column_stack((self.exog, exog_extra))
k_constraints += ex.shape[1] - self.exog.shape[1]
score_factor = self.score_factor(params_constrained)
score = (score_factor[:, None] * ex).sum(0)
hessian_factor = self.hessian_factor(params_constrained,
observed=observed)
hessian = -np.dot(ex.T * hessian_factor, ex)
from scipy import stats
# TODO check sign, why minus?
chi2stat = -score.dot(np.linalg.solve(hessian, score[:, None]))
pval = stats.chi2.sf(chi2stat, k_constraints)
# return a stats results instance instead? Contrast?
return chi2stat, pval, k_constraints
def _update_history(self, tmp_result, mu, history):
"""
Helper method to update history during iterative fit.
"""
history['params'].append(tmp_result.params)
history['deviance'].append(self.family.deviance(self.endog, mu,
self.var_weights,
self.freq_weights,
self.scale))
return history
[docs] def estimate_scale(self, mu):
"""
Estimates the dispersion/scale.
Type of scale can be chose in the fit method.
Parameters
----------
mu : array
mu is the mean response estimate
Returns
-------
Estimate of scale
Notes
-----
The default scale for Binomial and Poisson families is 1. The default
for the other families is Pearson's Chi-Square estimate.
See also
--------
statsmodels.genmod.generalized_linear_model.GLM.fit for more
information
"""
if not self.scaletype:
if isinstance(self.family, (families.Binomial, families.Poisson,
families.NegativeBinomial)):
return 1.
else:
return self._estimate_x2_scale(mu)
if isinstance(self.scaletype, float):
return np.array(self.scaletype)
if isinstance(self.scaletype, str):
if self.scaletype.lower() == 'x2':
return self._estimate_x2_scale(mu)
elif self.scaletype.lower() == 'dev':
return (self.family.deviance(self.endog, mu, self.var_weights,
self.freq_weights, self.scale) /
(self.df_resid))
else:
raise ValueError("Scale %s with type %s not understood" %
(self.scaletype, type(self.scaletype)))
else:
raise ValueError("Scale %s with type %s not understood" %
(self.scaletype, type(self.scaletype)))
def _estimate_x2_scale(self, mu):
resid = np.power(self.endog - mu, 2) * self.iweights
return np.sum(resid / self.family.variance(mu)) / self.df_resid
[docs] def estimate_tweedie_power(self, mu, method='brentq', low=1.01, high=5.):
"""
Tweedie specific function to estimate scale and the variance parameter.
The variance parameter is also referred to as p, xi, or shape.
Parameters
----------
mu : array-like
Fitted mean response variable
method : str, defaults to 'brentq'
Scipy optimizer used to solve the Pearson equation. Only brentq
currently supported.
low : float, optional
Low end of the bracketing interval [a,b] to be used in the search
for the power. Defaults to 1.01.
high : float, optional
High end of the bracketing interval [a,b] to be used in the search
for the power. Defaults to 5.
Returns
-------
power : float
The estimated shape or power
"""
if method == 'brentq':
from scipy.optimize import brentq
def psi_p(power, mu):
scale = ((self.iweights * (self.endog - mu) ** 2 /
(mu ** power)).sum() / self.df_resid)
return (np.sum(self.iweights * ((self.endog - mu) ** 2 /
(scale * (mu ** power)) - 1) *
np.log(mu)) / self.freq_weights.sum())
power = brentq(psi_p, low, high, args=(mu))
else:
raise NotImplementedError('Only brentq can currently be used')
return power
[docs] def predict(self, params, exog=None, exposure=None, offset=None,
linear=False):
"""
Return predicted values for a design matrix
Parameters
----------
params : array-like
Parameters / coefficients of a GLM.
exog : array-like, optional
Design / exogenous data. Is exog is None, model exog is used.
exposure : array-like, optional
Exposure time values, only can be used with the log link
function. See notes for details.
offset : array-like, optional
Offset values. See notes for details.
linear : bool
If True, returns the linear predicted values. If False,
returns the value of the inverse of the model's link function at
the linear predicted values.
Returns
-------
An array of fitted values
Notes
-----
Any `exposure` and `offset` provided here take precedence over
the `exposure` and `offset` used in the model fit. If `exog`
is passed as an argument here, then any `exposure` and
`offset` values in the fit will be ignored.
Exposure values must be strictly positive.
"""
# Use fit offset if appropriate
if offset is None and exog is None and hasattr(self, 'offset'):
offset = self.offset
elif offset is None:
offset = 0.
if exposure is not None and not isinstance(self.family.link,
families.links.Log):
raise ValueError("exposure can only be used with the log link "
"function")
# Use fit exposure if appropriate
if exposure is None and exog is None and hasattr(self, 'exposure'):
# Already logged
exposure = self.exposure
elif exposure is None:
exposure = 0.
else:
exposure = np.log(exposure)
if exog is None:
exog = self.exog
linpred = np.dot(exog, params) + offset + exposure
if linear:
return linpred
else:
return self.family.fitted(linpred)
[docs] def get_distribution(self, params, scale=1, exog=None, exposure=None,
offset=None):
"""
Returns a random number generator for the predictive distribution.
Parameters
----------
params : array-like
The model parameters.
scale : scalar
The scale parameter.
exog : array-like
The predictor variable matrix.
Returns
-------
gen
Frozen random number generator object. Use the ``rvs`` method to
generate random values.
Notes
-----
Due to the behavior of ``scipy.stats.distributions objects``, the
returned random number generator must be called with ``gen.rvs(n)``
where ``n`` is the number of observations in the data set used
to fit the model. If any other value is used for ``n``, misleading
results will be produced.
"""
fit = self.predict(params, exog, exposure, offset, linear=False)
import scipy.stats.distributions as dist
if isinstance(self.family, families.Gaussian):
return dist.norm(loc=fit, scale=np.sqrt(scale))
elif isinstance(self.family, families.Binomial):
return dist.binom(n=1, p=fit)
elif isinstance(self.family, families.Poisson):
return dist.poisson(mu=fit)
elif isinstance(self.family, families.Gamma):
alpha = fit / float(scale)
return dist.gamma(alpha, scale=scale)
else:
raise ValueError("get_distribution not implemented for %s" %
self.family.name)
def _setup_binomial(self):
# this checks what kind of data is given for Binomial.
# family will need a reference to endog if this is to be removed from
# preprocessing
self.n_trials = np.ones((self.endog.shape[0])) # For binomial
if isinstance(self.family, families.Binomial):
tmp = self.family.initialize(self.endog, self.freq_weights)
self.endog = tmp[0]
self.n_trials = tmp[1]
self._init_keys.append('n_trials')
[docs] def fit(self, start_params=None, maxiter=100, method='IRLS', tol=1e-8,
scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None,
full_output=True, disp=False, max_start_irls=3, **kwargs):
"""
Fits a generalized linear model for a given family.
Parameters
----------
start_params : array-like, optional
Initial guess of the solution for the loglikelihood maximization.
The default is family-specific and is given by the
``family.starting_mu(endog)``. If start_params is given then the
initial mean will be calculated as ``np.dot(exog, start_params)``.
maxiter : int, optional
Default is 100.
method : string
Default is 'IRLS' for iteratively reweighted least squares.
Otherwise gradient optimization is used.
tol : float
Convergence tolerance. Default is 1e-8.
scale : string or float, optional
`scale` can be 'X2', 'dev', or a float
The default value is None, which uses `X2` for Gamma, Gaussian,
and Inverse Gaussian.
`X2` is Pearson's chi-square divided by `df_resid`.
The default is 1 for the Binomial and Poisson families.
`dev` is the deviance divided by df_resid
cov_type : string
The type of parameter estimate covariance matrix to compute.
cov_kwds : dict-like
Extra arguments for calculating the covariance of the parameter
estimates.
use_t : bool
If True, the Student t-distribution is used for inference.
full_output : bool, optional
Set to True to have all available output in the Results object's
mle_retvals attribute. The output is dependent on the solver.
See LikelihoodModelResults notes section for more information.
Not used if methhod is IRLS.
disp : bool, optional
Set to True to print convergence messages. Not used if method is
IRLS.
max_start_irls : int
The number of IRLS iterations used to obtain starting
values for gradient optimization. Only relevant if
`method` is set to something other than 'IRLS'.
If IRLS fitting used, the following additional parameters are
available:
atol : float, optional
The absolute tolerance criterion that must be satisfied. Defaults
to ``tol``. Convergence is attained when:
:math:`rtol * prior + atol > abs(current - prior)`
rtol : float, optional
The relative tolerance criterion that must be satisfied. Defaults
to 0 which means ``rtol`` is not used. Convergence is attained
when:
:math:`rtol * prior + atol > abs(current - prior)`
tol_criterion : str, optional
Defaults to ``'deviance'``. Can optionally be ``'params'``.
wls_method : str, optional
options are 'lstsq', 'pinv' and 'qr'
specifies which linear algebra function to use for the irls
optimization. Default is `lstsq` which uses the same underlying
svd based approach as 'pinv', but is faster during iterations.
'lstsq' and 'pinv' regularize the estimate in singular and
near-singular cases by truncating small singular values based
on `rcond` of the respective numpy.linalg function. 'qr' is
only valied for cases that are not singular nor near-singular.
If a scipy optimizer is used, the following additional parameter is
available:
optim_hessian : {'eim', 'oim'}, optional
When 'oim', the default, the observed Hessian is used in fitting.
'eim' is the expected Hessian. This may provide more stable fits,
but adds assumption that the Hessian is correctly specified.
Notes
-----
If method is 'IRLS', then an additional keyword 'attach_wls' is
available. This is currently for internal use only and might change
in future versions. If attach_wls' is true, then the final WLS
instance of the IRLS iteration is attached to the results instance
as `results_wls` attribute.
"""
self.scaletype = scale
if method.lower() == "irls":
return self._fit_irls(start_params=start_params, maxiter=maxiter,
tol=tol, scale=scale, cov_type=cov_type,
cov_kwds=cov_kwds, use_t=use_t, **kwargs)
else:
self._optim_hessian = kwargs.get('optim_hessian')
fit_ = self._fit_gradient(start_params=start_params,
method=method,
maxiter=maxiter,
tol=tol, scale=scale,
full_output=full_output,
disp=disp, cov_type=cov_type,
cov_kwds=cov_kwds, use_t=use_t,
max_start_irls=max_start_irls,
**kwargs)
self._optim_hessian = None
return fit_
def _fit_gradient(self, start_params=None, method="newton",
maxiter=100, tol=1e-8, full_output=True,
disp=True, scale=None, cov_type='nonrobust',
cov_kwds=None, use_t=None, max_start_irls=3,
**kwargs):
"""
Fits a generalized linear model for a given family iteratively
using the scipy gradient optimizers.
"""
# fix scale during optimization, see #4616
scaletype = self.scaletype
self.scaletype = 1.
if (max_start_irls > 0) and (start_params is None):
irls_rslt = self._fit_irls(start_params=start_params,
maxiter=max_start_irls,
tol=tol, scale=1., cov_type='nonrobust',
cov_kwds=None, use_t=None,
**kwargs)
start_params = irls_rslt.params
del irls_rslt
rslt = super(GLM, self).fit(start_params=start_params, tol=tol,
maxiter=maxiter, full_output=full_output,
method=method, disp=disp, **kwargs)
# reset scaletype to original
self.scaletype = scaletype
mu = self.predict(rslt.params)
scale = self.estimate_scale(mu)
if rslt.normalized_cov_params is None:
cov_p = None
else:
cov_p = rslt.normalized_cov_params / scale
glm_results = GLMResults(self, rslt.params,
cov_p,
scale,
cov_type=cov_type, cov_kwds=cov_kwds,
use_t=use_t)
# TODO: iteration count is not always available
history = {'iteration': 0}
if full_output:
glm_results.mle_retvals = rslt.mle_retvals
if 'iterations' in rslt.mle_retvals:
history['iteration'] = rslt.mle_retvals['iterations']
glm_results.method = method
glm_results.fit_history = history
return GLMResultsWrapper(glm_results)
def _fit_irls(self, start_params=None, maxiter=100, tol=1e-8,
scale=None, cov_type='nonrobust', cov_kwds=None,
use_t=None, **kwargs):
"""
Fits a generalized linear model for a given family using
iteratively reweighted least squares (IRLS).
"""
attach_wls = kwargs.pop('attach_wls', False)
atol = kwargs.get('atol')
rtol = kwargs.get('rtol', 0.)
tol_criterion = kwargs.get('tol_criterion', 'deviance')
wls_method = kwargs.get('wls_method', 'lstsq')
atol = tol if atol is None else atol
endog = self.endog
wlsexog = self.exog
if start_params is None:
start_params = np.zeros(self.exog.shape[1], np.float)
mu = self.family.starting_mu(self.endog)
lin_pred = self.family.predict(mu)
else:
lin_pred = np.dot(wlsexog, start_params) + self._offset_exposure
mu = self.family.fitted(lin_pred)
self.scale = self.estimate_scale(mu)
dev = self.family.deviance(self.endog, mu, self.var_weights,
self.freq_weights, self.scale)
if np.isnan(dev):
raise ValueError("The first guess on the deviance function "
"returned a nan. This could be a boundary "
" problem and should be reported.")
# first guess on the deviance is assumed to be scaled by 1.
# params are none to start, so they line up with the deviance
history = dict(params=[np.inf, start_params], deviance=[np.inf, dev])
converged = False
criterion = history[tol_criterion]
# 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):
self.weights = (self.iweights * self.n_trials *
self.family.weights(mu))
wlsendog = (lin_pred + self.family.link.deriv(mu) * (self.endog-mu)
- self._offset_exposure)
wls_results = reg_tools._MinimalWLS(
wlsendog,
wlsexog,
self.weights).fit(method=wls_method)
lin_pred = np.dot(self.exog, wls_results.params)
lin_pred += self._offset_exposure
mu = self.family.fitted(lin_pred)
history = self._update_history(wls_results, mu, history)
self.scale = self.estimate_scale(mu)
if endog.squeeze().ndim == 1 and np.allclose(mu - endog, 0):
msg = "Perfect separation detected, results not available"
raise PerfectSeparationError(msg)
converged = _check_convergence(criterion, iteration + 1, atol,
rtol)
if converged:
break
self.mu = mu
if maxiter > 0: # Only if iterative used
wls_method2 = 'pinv' if wls_method == 'lstsq' else wls_method
wls_model = lm.WLS(wlsendog, wlsexog, self.weights)
wls_results = wls_model.fit(method=wls_method2)
glm_results = GLMResults(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 = "IRLS"
glm_results.mle_settings = {}
glm_results.mle_settings['wls_method'] = wls_method
glm_results.mle_settings['optimizer'] = glm_results.method
if (maxiter > 0) and (attach_wls is True):
glm_results.results_wls = wls_results
history['iteration'] = iteration + 1
glm_results.fit_history = history
glm_results.converged = converged
return GLMResultsWrapper(glm_results)
[docs] def fit_regularized(self, method="elastic_net", alpha=0.,
start_params=None, refit=False, **kwargs):
"""
Return a regularized fit to a linear regression model.
Parameters
----------
method :
Only the `elastic_net` approach is currently implemented.
alpha : scalar or array-like
The penalty weight. If a scalar, the same penalty weight
applies to all variables in the model. If a vector, it
must have the same length as `params`, and contains a
penalty weight for each coefficient.
start_params : array-like
Starting values for `params`.
refit : bool
If True, the model is refit using only the variables that
have non-zero coefficients in the regularized fit. The
refitted model is not regularized.
Returns
-------
An array, or a GLMResults object of the same type returned by `fit`.
Notes
-----
The penalty is the ``elastic net`` penalty, which is a
combination of L1 and L2 penalties.
The function that is minimized is:
.. math::
-loglike/n + alpha*((1-L1\_wt)*|params|_2^2/2 + L1\_wt*|params|_1)
where :math:`|*|_1` and :math:`|*|_2` are the L1 and L2 norms.
Post-estimation results are based on the same data used to
select variables, hence may be subject to overfitting biases.
The elastic_net method uses the following keyword arguments:
maxiter : int
Maximum number of iterations
L1_wt : float
Must be in [0, 1]. The L1 penalty has weight L1_wt and the
L2 penalty has weight 1 - L1_wt.
cnvrg_tol : float
Convergence threshold for line searches
zero_tol : float
Coefficients below this threshold are treated as zero.
"""
from statsmodels.base.elastic_net import fit_elasticnet
if method != "elastic_net":
raise ValueError("method for fit_regularied must be elastic_net")
defaults = {"maxiter": 50, "L1_wt": 1, "cnvrg_tol": 1e-10,
"zero_tol": 1e-10}
defaults.update(kwargs)
result = fit_elasticnet(self, method=method,
alpha=alpha,
start_params=start_params,
refit=refit,
**defaults)
self.mu = self.predict(result.params)
self.scale = self.estimate_scale(self.mu)
return result
[docs] def fit_constrained(self, constraints, start_params=None, **fit_kwds):
"""fit the model subject to linear equality constraints
The constraints are of the form `R params = q`
where R is the constraint_matrix and q is the vector of
constraint_values.
The estimation creates a new model with transformed design matrix,
exog, and converts the results back to the original parameterization.
Parameters
----------
constraints : formula expression or tuple
If it is a tuple, then the constraint needs to be given by two
arrays (constraint_matrix, constraint_value), i.e. (R, q).
Otherwise, the constraints can be given as strings or list of
strings.
see t_test for details
start_params : None or array_like
starting values for the optimization. `start_params` needs to be
given in the original parameter space and are internally
transformed.
**fit_kwds : keyword arguments
fit_kwds are used in the optimization of the transformed model.
Returns
-------
results : Results instance
"""
from patsy import DesignInfo
from statsmodels.base._constraints import fit_constrained
# same pattern as in base.LikelihoodModel.t_test
lc = DesignInfo(self.exog_names).linear_constraint(constraints)
R, q = lc.coefs, lc.constants
# TODO: add start_params option, need access to tranformation
# fit_constrained needs to do the transformation
params, cov, res_constr = fit_constrained(self, R, q,
start_params=start_params,
fit_kwds=fit_kwds)
# create dummy results Instance, TODO: wire up properly
res = self.fit(start_params=params, maxiter=0) # we get a wrapper back
res._results.params = params
res._results.cov_params_default = cov
cov_type = fit_kwds.get('cov_type', 'nonrobust')
if cov_type != 'nonrobust':
res._results.normalized_cov_params = cov / res_constr.scale
else:
res._results.normalized_cov_params = None
res._results.scale = res_constr.scale
k_constr = len(q)
res._results.df_resid += k_constr
res._results.df_model -= k_constr
res._results.constraints = lc
res._results.k_constr = k_constr
res._results.results_constrained = res_constr
# TODO: the next is not the best. history should bin in results
res._results.model.history = res_constr.model.history
return res
[docs]class GLMResults(base.LikelihoodModelResults):
"""
Class to contain GLM results.
GLMResults inherits from statsmodels.LikelihoodModelResults
Parameters
----------
See statsmodels.LikelihoodModelReesults
Returns
-------
**Attributes**
aic : float
Akaike Information Criterion
-2 * `llf` + 2*(`df_model` + 1)
bic : float
Bayes Information Criterion
`deviance` - `df_resid` * log(`nobs`)
deviance : float
See statsmodels.families.family for the distribution-specific deviance
functions.
df_model : float
See GLM.df_model
df_resid : float
See GLM.df_resid
fit_history : dict
Contains information about the iterations. Its keys are `iterations`,
`deviance` and `params`.
fittedvalues : array
Linear predicted values for the fitted model.
dot(exog, params)
llf : float
Value of the loglikelihood function evalued at params.
See statsmodels.families.family for distribution-specific
loglikelihoods.
model : class instance
Pointer to GLM model instance that called fit.
mu : array
See GLM docstring.
nobs : float
The number of observations n.
normalized_cov_params : array
See GLM docstring
null_deviance : float
The value of the deviance function for the model fit with a constant
as the only regressor.
params : array
The coefficients of the fitted model. Note that interpretation
of the coefficients often depends on the distribution family and the
data.
pearson_chi2 : array
Pearson's Chi-Squared statistic is defined as the sum of the squares
of the Pearson residuals.
pvalues : array
The two-tailed p-values for the parameters.
resid_anscombe : array
Anscombe residuals. See statsmodels.families.family for distribution-
specific Anscombe residuals. Currently, the unscaled residuals are
provided. In a future version, the scaled residuals will be provided.
resid_anscombe_scaled : array
Scaled Anscombe residuals. See statsmodels.families.family for
distribution-specific Anscombe residuals.
resid_anscombe_unscaled : array
Unscaled Anscombe residuals. See statsmodels.families.family for
distribution-specific Anscombe residuals.
resid_deviance : array
Deviance residuals. See statsmodels.families.family for distribution-
specific deviance residuals.
resid_pearson : array
Pearson residuals. The Pearson residuals are defined as
(`endog` - `mu`)/sqrt(VAR(`mu`)) where VAR is the distribution
specific variance function. See statsmodels.families.family and
statsmodels.families.varfuncs for more information.
resid_response : array
Respnose residuals. The response residuals are defined as
`endog` - `fittedvalues`
resid_working : array
Working residuals. The working residuals are defined as
`resid_response`/link'(`mu`). See statsmodels.family.links for the
derivatives of the link functions. They are defined analytically.
scale : float
The estimate of the scale / dispersion for the model fit.
See GLM.fit and GLM.estimate_scale for more information.
stand_errors : array
The standard errors of the fitted GLM. #TODO still named bse
See Also
--------
statsmodels.base.model.LikelihoodModelResults
"""
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._freq_weights = model.freq_weights
self._var_weights = model.var_weights
self._iweights = model.iweights
if isinstance(self.family, families.Binomial):
self._n_trials = self.model.n_trials
else:
self._n_trials = 1
self.df_resid = model.df_resid
self.df_model = model.df_model
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', '_freq_weights',
'_var_weights', '_iweights'])
self.data_in_cache = getattr(self, 'data_in_cache', [])
self.data_in_cache.extend(['null', 'mu'])
self._data_attr_model = getattr(self, '_data_attr_model', [])
self._data_attr_model.append('mu')
# 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
# temporary warning
ct = (cov_type == 'nonrobust') or (cov_type.upper().startswith('HC'))
if self.model._has_freq_weights and not ct:
import warnings
from statsmodels.tools.sm_exceptions import SpecificationWarning
warnings.warn('cov_type not fully supported with freq_weights',
SpecificationWarning)
if self.model._has_var_weights and not ct:
import warnings
from statsmodels.tools.sm_exceptions import SpecificationWarning
warnings.warn('cov_type not fully supported with var_weights',
SpecificationWarning)
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)
[docs] @cache_readonly
def resid_response(self):
return self._n_trials * (self._endog-self.mu)
[docs] @cache_readonly
def resid_pearson(self):
return (np.sqrt(self._n_trials) * (self._endog-self.mu) *
np.sqrt(self._var_weights) /
np.sqrt(self.family.variance(self.mu)))
[docs] @cache_readonly
def resid_working(self):
# Isn't self.resid_response is already adjusted by _n_trials?
val = (self.resid_response * self.family.link.deriv(self.mu))
val *= self._n_trials
return val
[docs] @cache_readonly
def resid_anscombe(self):
import warnings
warnings.warn('Anscombe residuals currently unscaled. In a future '
'release, they will be scaled.', category=FutureWarning)
return self.family.resid_anscombe(self._endog, self.fittedvalues,
var_weights=self._var_weights,
scale=1.)
[docs] @cache_readonly
def resid_anscombe_scaled(self):
return self.family.resid_anscombe(self._endog, self.fittedvalues,
var_weights=self._var_weights,
scale=self.scale)
[docs] @cache_readonly
def resid_anscombe_unscaled(self):
return self.family.resid_anscombe(self._endog, self.fittedvalues,
var_weights=self._var_weights,
scale=1.)
[docs] @cache_readonly
def resid_deviance(self):
dev = self.family.resid_dev(self._endog, self.fittedvalues,
var_weights=self._var_weights,
scale=1.)
return dev
[docs] @cache_readonly
def pearson_chi2(self):
chisq = (self._endog - self.mu)**2 / self.family.variance(self.mu)
chisq *= self._iweights * self._n_trials
chisqsum = np.sum(chisq)
return chisqsum
[docs] @cache_readonly
def fittedvalues(self):
return self.mu
[docs] @cache_readonly
def mu(self):
return self.model.predict(self.params)
[docs] @cache_readonly
def null(self):
endog = self._endog
model = self.model
exog = np.ones((len(endog), 1))
kwargs = model._get_init_kwds()
kwargs.pop('family')
if hasattr(self, '_offset_exposure'):
return GLM(endog, exog, family=self.family,
**kwargs).fit().fittedvalues
else:
# correct if fitted is identical across observations
wls_model = lm.WLS(endog, exog,
weights=self._iweights * self._n_trials)
return wls_model.fit().fittedvalues
[docs] @cache_readonly
def deviance(self):
return self.family.deviance(self._endog, self.mu, self._var_weights,
self._freq_weights)
[docs] @cache_readonly
def null_deviance(self):
return self.family.deviance(self._endog, self.null, self._var_weights,
self._freq_weights)
[docs] @cache_readonly
def llnull(self):
return self.family.loglike(self._endog, self.null,
var_weights=self._var_weights,
freq_weights=self._freq_weights,
scale=self.scale)
[docs] @cache_readonly
def llf(self):
_modelfamily = self.family
if (isinstance(self.family, families.Gaussian) and
isinstance(self.family.link, families.links.Power) and
(self.family.link.power == 1.)):
scale = (np.power(self._endog - self.mu, 2) * self._iweights).sum()
scale /= self.model.wnobs
else:
scale = self.scale
val = _modelfamily.loglike(self._endog, self.mu,
var_weights=self._var_weights,
freq_weights=self._freq_weights,
scale=scale)
return val
[docs] @cache_readonly
def aic(self):
return -2 * self.llf + 2 * (self.df_model + 1)
[docs] @cache_readonly
def bic(self):
return (self.deviance -
(self.model.wnobs - self.df_model - 1) *
np.log(self.model.wnobs))
[docs] def get_prediction(self, exog=None, exposure=None, offset=None,
transform=True, linear=False,
row_labels=None):
import statsmodels.regression._prediction as linpred
pred_kwds = {'exposure': exposure, 'offset': offset, 'linear': True}
# two calls to a get_prediction duplicates exog generation if patsy
res_linpred = linpred.get_prediction(self, exog=exog,
transform=transform,
row_labels=row_labels,
pred_kwds=pred_kwds)
pred_kwds['linear'] = False
res = pred.get_prediction_glm(self, exog=exog, transform=transform,
row_labels=row_labels,
linpred=res_linpred,
link=self.model.family.link,
pred_kwds=pred_kwds)
return res
get_prediction.__doc__ = pred.get_prediction_glm.__doc__
[docs] def remove_data(self):
# GLM has alias/reference in result instance
self._data_attr.extend([i for i in self.model._data_attr
if '_data.' not in i])
super(self.__class__, self).remove_data()
# TODO: what are these in results?
self._endog = None
self._freq_weights = None
self._var_weights = None
self._iweights = None
self._n_trials = None
remove_data.__doc__ = base.LikelihoodModelResults.remove_data.__doc__
[docs] def plot_added_variable(self, focus_exog, resid_type=None,
use_glm_weights=True, fit_kwargs=None,
ax=None):
# Docstring attached below
from statsmodels.graphics.regressionplots import plot_added_variable
fig = plot_added_variable(self, focus_exog,
resid_type=resid_type,
use_glm_weights=use_glm_weights,
fit_kwargs=fit_kwargs, ax=ax)
return fig
plot_added_variable.__doc__ = _plot_added_variable_doc % {
'extra_params_doc': ''}
[docs] def plot_partial_residuals(self, focus_exog, ax=None):
# Docstring attached below
from statsmodels.graphics.regressionplots import plot_partial_residuals
return plot_partial_residuals(self, focus_exog, ax=ax)
plot_partial_residuals.__doc__ = _plot_partial_residuals_doc % {
'extra_params_doc': ''}
[docs] def plot_ceres_residuals(self, focus_exog, frac=0.66, cond_means=None,
ax=None):
# Docstring attached below
from statsmodels.graphics.regressionplots import plot_ceres_residuals
return plot_ceres_residuals(self, focus_exog, frac,
cond_means=cond_means, ax=ax)
plot_ceres_residuals.__doc__ = _plot_ceres_residuals_doc % {
'extra_params_doc': ''}
[docs] def summary(self, yname=None, xname=None, title=None, alpha=.05):
"""
Summarize the Regression Results
Parameters
-----------
yname : string, optional
Default is `y`
xname : list of strings, optional
Default is `var_##` for ## in p the number of regressors
title : string, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary : class to hold summary
results
"""
top_left = [('Dep. Variable:', None),
('Model:', None),
('Model Family:', [self.family.__class__.__name__]),
('Link Function:', [self.family.link.__class__.__name__]),
('Method:', [self.method]),
('Date:', None),
('Time:', None),
('No. Iterations:',
["%d" % self.fit_history['iteration']]),
]
top_right = [('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None),
('Scale:', ["%#8.5g" % self.scale]),
('Log-Likelihood:', None),
('Deviance:', ["%#8.5g" % self.deviance]),
('Pearson chi2:', ["%#6.3g" % self.pearson_chi2])
]
if hasattr(self, 'cov_type'):
top_right.append(('Covariance Type:', [self.cov_type]))
if title is None:
title = "Generalized Linear Model Regression Results"
# create summary tables
from statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(self, gleft=top_left, gright=top_right, # [],
yname=yname, xname=xname, title=title)
smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
use_t=self.use_t)
if hasattr(self, 'constraints'):
smry.add_extra_txt(['Model has been estimated subject to linear '
'equality constraints.'])
# diagnostic table is not used yet:
# smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
# yname=yname, xname=xname,
# title="")
return smry
[docs] def summary2(self, yname=None, xname=None, title=None, alpha=.05,
float_format="%.4f"):
"""Experimental summary for regression Results
Parameters
-----------
yname : string
Name of the dependent variable (optional)
xname : List of strings of length equal to the number of parameters
Names of the independent variables (optional)
title : string, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
significance level for the confidence intervals
float_format: string
print format for floats in parameters summary
Returns
-------
smry : Summary instance
this holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary2.Summary : class to hold summary
results
"""
self.method = 'IRLS'
from statsmodels.iolib import summary2
smry = summary2.Summary()
smry.add_base(results=self, alpha=alpha, float_format=float_format,
xname=xname, yname=yname, title=title)
if hasattr(self, 'constraints'):
smry.add_text('Model has been estimated subject to linear '
'equality constraints.')
return smry
class GLMResultsWrapper(lm.RegressionResultsWrapper):
_attrs = {
'resid_anscombe': 'rows',
'resid_deviance': 'rows',
'resid_pearson': 'rows',
'resid_response': 'rows',
'resid_working': 'rows'
}
_wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs,
_attrs)
wrap.populate_wrapper(GLMResultsWrapper, GLMResults)
if __name__ == "__main__":
import statsmodels.api as sm
data = sm.datasets.longley.load()
# data.exog = add_constant(data.exog)
GLMmod = GLM(data.endog, data.exog).fit()
GLMT = GLMmod.summary(returns='tables')
# GLMT[0].extend_right(GLMT[1])
# print(GLMT[0])
# print(GLMT[2])
GLMTp = GLMmod.summary(title='Test GLM')
"""
From Stata
. webuse beetle
. glm r i.beetle ldose, family(binomial n) link(cloglog)
Iteration 0: log likelihood = -79.012269
Iteration 1: log likelihood = -76.94951
Iteration 2: log likelihood = -76.945645
Iteration 3: log likelihood = -76.945645
Generalized linear models No. of obs = 24
Optimization : ML Residual df = 20
Scale parameter = 1
Deviance = 73.76505595 (1/df) Deviance = 3.688253
Pearson = 71.8901173 (1/df) Pearson = 3.594506
Variance function: V(u) = u*(1-u/n) [Binomial]
Link function : g(u) = ln(-ln(1-u/n)) [Complementary log-log]
AIC = 6.74547
Log likelihood = -76.94564525 BIC = 10.20398
------------------------------------------------------------------------------
| OIM
r | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
beetle |
2 | -.0910396 .1076132 -0.85 0.398 -.3019576 .1198783
3 | -1.836058 .1307125 -14.05 0.000 -2.09225 -1.579867
|
ldose | 19.41558 .9954265 19.50 0.000 17.46458 21.36658
_cons | -34.84602 1.79333 -19.43 0.000 -38.36089 -31.33116
------------------------------------------------------------------------------
"""
# NOTE: wfs dataset has been removed due to a licensing issue
# example of using offset
# data = sm.datasets.wfs.load()
# get offset
# offset = np.log(data.exog[:,-1])
# exog = data.exog[:,:-1]
# convert dur to dummy
# exog = sm.tools.categorical(exog, col=0, drop=True)
# drop reference category
# convert res to dummy
# exog = sm.tools.categorical(exog, col=0, drop=True)
# convert edu to dummy
# exog = sm.tools.categorical(exog, col=0, drop=True)
# drop reference categories and add intercept
# exog = sm.add_constant(exog[:,[1,2,3,4,5,7,8,10,11,12]])
# endog = np.round(data.endog)
# mod = sm.GLM(endog, exog, family=sm.families.Poisson()).fit()
# res1 = GLM(endog, exog, family=sm.families.Poisson(),
# offset=offset).fit(tol=1e-12, maxiter=250)
# exposuremod = GLM(endog, exog, family=sm.families.Poisson(),
# exposure = data.exog[:,-1]).fit(tol=1e-12,
# maxiter=250)
# assert(np.all(res1.params == exposuremod.params))
