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AIM-PIbd-32-Kurbanova-A-A/aimenv/Lib/site-packages/statsmodels/discrete/discrete_model.py
ALINA_KURBANOVA f2ed44ff31 lab 1 is done
2024-10-02 22:15:59 +04:00

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"""
Limited dependent variable and qualitative variables.
Includes binary outcomes, count data, (ordered) ordinal data and limited
dependent variables.
General References
--------------------
A.C. Cameron and P.K. Trivedi. `Regression Analysis of Count Data`.
Cambridge, 1998
G.S. Madalla. `Limited-Dependent and Qualitative Variables in Econometrics`.
Cambridge, 1983.
W. Greene. `Econometric Analysis`. Prentice Hall, 5th. edition. 2003.
"""
__all__ = ["Poisson", "Logit", "Probit", "MNLogit", "NegativeBinomial",
"GeneralizedPoisson", "NegativeBinomialP", "CountModel"]
from statsmodels.compat.pandas import Appender
import warnings
import numpy as np
from pandas import MultiIndex, get_dummies
from scipy import special, stats
from scipy.special import digamma, gammaln, loggamma, polygamma
from scipy.stats import nbinom
from statsmodels.base.data import handle_data # for mnlogit
from statsmodels.base.l1_slsqp import fit_l1_slsqp
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
from statsmodels.base._constraints import fit_constrained_wrap
import statsmodels.base._parameter_inference as pinfer
from statsmodels.base import _prediction_inference as pred
from statsmodels.distributions import genpoisson_p
import statsmodels.regression.linear_model as lm
from statsmodels.tools import data as data_tools, tools
from statsmodels.tools.decorators import cache_readonly
from statsmodels.tools.numdiff import approx_fprime_cs
from statsmodels.tools.sm_exceptions import (
PerfectSeparationError,
PerfectSeparationWarning,
SpecificationWarning,
)
try:
import cvxopt # noqa:F401
have_cvxopt = True
except ImportError:
have_cvxopt = False
# TODO: When we eventually get user-settable precision, we need to change
# this
FLOAT_EPS = np.finfo(float).eps
# Limit for exponentials to avoid overflow
EXP_UPPER_LIMIT = np.log(np.finfo(np.float64).max) - 1.0
# TODO: add options for the parameter covariance/variance
# ie., OIM, EIM, and BHHH see Green 21.4
_discrete_models_docs = """
"""
_discrete_results_docs = """
%(one_line_description)s
Parameters
----------
model : A DiscreteModel instance
params : array_like
The parameters of a fitted model.
hessian : array_like
The hessian of the fitted model.
scale : float
A scale parameter for the covariance matrix.
Attributes
----------
df_resid : float
See model definition.
df_model : float
See model definition.
llf : float
Value of the loglikelihood
%(extra_attr)s"""
_l1_results_attr = """ nnz_params : int
The number of nonzero parameters in the model. Train with
trim_params == True or else numerical error will distort this.
trimmed : bool array
trimmed[i] == True if the ith parameter was trimmed from the model."""
_get_start_params_null_docs = """
Compute one-step moment estimator for null (constant-only) model
This is a preliminary estimator used as start_params.
Returns
-------
params : ndarray
parameter estimate based one one-step moment matching
"""
_check_rank_doc = """
check_rank : bool
Check exog rank to determine model degrees of freedom. Default is
True. Setting to False reduces model initialization time when
exog.shape[1] is large.
"""
# helper for MNLogit (will be generally useful later)
def _numpy_to_dummies(endog):
if endog.ndim == 2 and endog.dtype.kind not in ["S", "O"]:
endog_dummies = endog
ynames = range(endog.shape[1])
else:
dummies = get_dummies(endog, drop_first=False)
ynames = {i: dummies.columns[i] for i in range(dummies.shape[1])}
endog_dummies = np.asarray(dummies, dtype=float)
return endog_dummies, ynames
return endog_dummies, ynames
def _pandas_to_dummies(endog):
if endog.ndim == 2:
if endog.shape[1] == 1:
yname = endog.columns[0]
endog_dummies = get_dummies(endog.iloc[:, 0])
else: # assume already dummies
yname = 'y'
endog_dummies = endog
else:
yname = endog.name
if yname is None:
yname = 'y'
endog_dummies = get_dummies(endog)
ynames = endog_dummies.columns.tolist()
return endog_dummies, ynames, yname
def _validate_l1_method(method):
"""
As of 0.10.0, the supported values for `method` in `fit_regularized`
are "l1" and "l1_cvxopt_cp". If an invalid value is passed, raise
with a helpful error message
Parameters
----------
method : str
Raises
------
ValueError
"""
if method not in ['l1', 'l1_cvxopt_cp']:
raise ValueError('`method` = {method} is not supported, use either '
'"l1" or "l1_cvxopt_cp"'.format(method=method))
#### Private Model Classes ####
class DiscreteModel(base.LikelihoodModel):
"""
Abstract class for discrete choice models.
This class does not do anything itself but lays out the methods and
call signature expected of child classes in addition to those of
statsmodels.model.LikelihoodModel.
"""
def __init__(self, endog, exog, check_rank=True, **kwargs):
self._check_rank = check_rank
super().__init__(endog, exog, **kwargs)
self.raise_on_perfect_prediction = False # keep for backwards compat
self.k_extra = 0
def initialize(self):
"""
Initialize is called by
statsmodels.model.LikelihoodModel.__init__
and should contain any preprocessing that needs to be done for a model.
"""
if self._check_rank:
# assumes constant
rank = tools.matrix_rank(self.exog, method="qr")
else:
# If rank check is skipped, assume full
rank = self.exog.shape[1]
self.df_model = float(rank - 1)
self.df_resid = float(self.exog.shape[0] - rank)
def cdf(self, X):
"""
The cumulative distribution function of the model.
"""
raise NotImplementedError
def pdf(self, X):
"""
The probability density (mass) function of the model.
"""
raise NotImplementedError
def _check_perfect_pred(self, params, *args):
endog = self.endog
fittedvalues = self.predict(params)
if np.allclose(fittedvalues - endog, 0):
if self.raise_on_perfect_prediction:
# backwards compatibility for attr raise_on_perfect_prediction
msg = "Perfect separation detected, results not available"
raise PerfectSeparationError(msg)
else:
msg = ("Perfect separation or prediction detected, "
"parameter may not be identified")
warnings.warn(msg, category=PerfectSeparationWarning)
@Appender(base.LikelihoodModel.fit.__doc__)
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
"""
Fit the model using maximum likelihood.
The rest of the docstring is from
statsmodels.base.model.LikelihoodModel.fit
"""
if callback is None:
callback = self._check_perfect_pred
else:
pass # TODO: make a function factory to have multiple call-backs
mlefit = super().fit(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
**kwargs)
return mlefit # It is up to subclasses to wrap results
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=True,
callback=None, alpha=0, trim_mode='auto',
auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03,
qc_verbose=False, **kwargs):
"""
Fit the model using a regularized maximum likelihood.
The regularization method AND the solver used is determined by the
argument method.
Parameters
----------
start_params : array_like, optional
Initial guess of the solution for the loglikelihood maximization.
The default is an array of zeros.
method : 'l1' or 'l1_cvxopt_cp'
See notes for details.
maxiter : {int, 'defined_by_method'}
Maximum number of iterations to perform.
If 'defined_by_method', then use method defaults (see notes).
full_output : bool
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.
disp : bool
Set to True to print convergence messages.
fargs : tuple
Extra arguments passed to the likelihood function, i.e.,
loglike(x,*args).
callback : callable callback(xk)
Called after each iteration, as callback(xk), where xk is the
current parameter vector.
retall : bool
Set to True to return list of solutions at each iteration.
Available in Results object's mle_retvals attribute.
alpha : non-negative scalar or numpy array (same size as parameters)
The weight multiplying the l1 penalty term.
trim_mode : 'auto, 'size', or 'off'
If not 'off', trim (set to zero) parameters that would have been
zero if the solver reached the theoretical minimum.
If 'auto', trim params using the Theory above.
If 'size', trim params if they have very small absolute value.
size_trim_tol : float or 'auto' (default = 'auto')
Tolerance used when trim_mode == 'size'.
auto_trim_tol : float
Tolerance used when trim_mode == 'auto'.
qc_tol : float
Print warning and do not allow auto trim when (ii) (above) is
violated by this much.
qc_verbose : bool
If true, print out a full QC report upon failure.
**kwargs
Additional keyword arguments used when fitting the model.
Returns
-------
Results
A results instance.
Notes
-----
Using 'l1_cvxopt_cp' requires the cvxopt module.
Extra parameters are not penalized if alpha is given as a scalar.
An example is the shape parameter in NegativeBinomial `nb1` and `nb2`.
Optional arguments for the solvers (available in Results.mle_settings)::
'l1'
acc : float (default 1e-6)
Requested accuracy as used by slsqp
'l1_cvxopt_cp'
abstol : float
absolute accuracy (default: 1e-7).
reltol : float
relative accuracy (default: 1e-6).
feastol : float
tolerance for feasibility conditions (default: 1e-7).
refinement : int
number of iterative refinement steps when solving KKT
equations (default: 1).
Optimization methodology
With :math:`L` the negative log likelihood, we solve the convex but
non-smooth problem
.. math:: \\min_\\beta L(\\beta) + \\sum_k\\alpha_k |\\beta_k|
via the transformation to the smooth, convex, constrained problem
in twice as many variables (adding the "added variables" :math:`u_k`)
.. math:: \\min_{\\beta,u} L(\\beta) + \\sum_k\\alpha_k u_k,
subject to
.. math:: -u_k \\leq \\beta_k \\leq u_k.
With :math:`\\partial_k L` the derivative of :math:`L` in the
:math:`k^{th}` parameter direction, theory dictates that, at the
minimum, exactly one of two conditions holds:
(i) :math:`|\\partial_k L| = \\alpha_k` and :math:`\\beta_k \\neq 0`
(ii) :math:`|\\partial_k L| \\leq \\alpha_k` and :math:`\\beta_k = 0`
"""
_validate_l1_method(method)
# Set attributes based on method
cov_params_func = self.cov_params_func_l1
### Bundle up extra kwargs for the dictionary kwargs. These are
### passed through super(...).fit() as kwargs and unpacked at
### appropriate times
alpha = np.array(alpha)
assert alpha.min() >= 0
try:
kwargs['alpha'] = alpha
except TypeError:
kwargs = dict(alpha=alpha)
kwargs['alpha_rescaled'] = kwargs['alpha'] / float(self.endog.shape[0])
kwargs['trim_mode'] = trim_mode
kwargs['size_trim_tol'] = size_trim_tol
kwargs['auto_trim_tol'] = auto_trim_tol
kwargs['qc_tol'] = qc_tol
kwargs['qc_verbose'] = qc_verbose
### Define default keyword arguments to be passed to super(...).fit()
if maxiter == 'defined_by_method':
if method == 'l1':
maxiter = 1000
elif method == 'l1_cvxopt_cp':
maxiter = 70
## Parameters to pass to super(...).fit()
# For the 'extra' parameters, pass all that are available,
# even if we know (at this point) we will only use one.
extra_fit_funcs = {'l1': fit_l1_slsqp}
if have_cvxopt and method == 'l1_cvxopt_cp':
from statsmodels.base.l1_cvxopt import fit_l1_cvxopt_cp
extra_fit_funcs['l1_cvxopt_cp'] = fit_l1_cvxopt_cp
elif method.lower() == 'l1_cvxopt_cp':
raise ValueError("Cannot use l1_cvxopt_cp as cvxopt "
"was not found (install it, or use method='l1' instead)")
if callback is None:
callback = self._check_perfect_pred
else:
pass # make a function factory to have multiple call-backs
mlefit = super().fit(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
extra_fit_funcs=extra_fit_funcs,
cov_params_func=cov_params_func,
**kwargs)
return mlefit # up to subclasses to wrap results
def cov_params_func_l1(self, likelihood_model, xopt, retvals):
"""
Computes cov_params on a reduced parameter space
corresponding to the nonzero parameters resulting from the
l1 regularized fit.
Returns a full cov_params matrix, with entries corresponding
to zero'd values set to np.nan.
"""
H = likelihood_model.hessian(xopt)
trimmed = retvals['trimmed']
nz_idx = np.nonzero(~trimmed)[0]
nnz_params = (~trimmed).sum()
if nnz_params > 0:
H_restricted = H[nz_idx[:, None], nz_idx]
# Covariance estimate for the nonzero params
H_restricted_inv = np.linalg.inv(-H_restricted)
else:
H_restricted_inv = np.zeros(0)
cov_params = np.nan * np.ones(H.shape)
cov_params[nz_idx[:, None], nz_idx] = H_restricted_inv
return cov_params
def predict(self, params, exog=None, which="mean", linear=None):
"""
Predict response variable of a model given exogenous variables.
"""
raise NotImplementedError
def _derivative_exog(self, params, exog=None, dummy_idx=None,
count_idx=None):
"""
This should implement the derivative of the non-linear function
"""
raise NotImplementedError
def _derivative_exog_helper(self, margeff, params, exog, dummy_idx,
count_idx, transform):
"""
Helper for _derivative_exog to wrap results appropriately
"""
from .discrete_margins import _get_count_effects, _get_dummy_effects
if count_idx is not None:
margeff = _get_count_effects(margeff, exog, count_idx, transform,
self, params)
if dummy_idx is not None:
margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform,
self, params)
return margeff
class BinaryModel(DiscreteModel):
_continuous_ok = False
def __init__(self, endog, exog, offset=None, check_rank=True, **kwargs):
# unconditional check, requires no extra kwargs added by subclasses
self._check_kwargs(kwargs)
super().__init__(endog, exog, offset=offset, check_rank=check_rank,
**kwargs)
if not issubclass(self.__class__, MultinomialModel):
if not np.all((self.endog >= 0) & (self.endog <= 1)):
raise ValueError("endog must be in the unit interval.")
if offset is None:
delattr(self, 'offset')
if (not self._continuous_ok and
np.any(self.endog != np.round(self.endog))):
raise ValueError("endog must be binary, either 0 or 1")
def predict(self, params, exog=None, which="mean", linear=None,
offset=None):
"""
Predict response variable of a model given exogenous variables.
Parameters
----------
params : array_like
Fitted parameters of the model.
exog : array_like
1d or 2d array of exogenous values. If not supplied, the
whole exog attribute of the model is used.
which : {'mean', 'linear', 'var', 'prob'}, optional
Statistic to predict. Default is 'mean'.
- 'mean' returns the conditional expectation of endog E(y | x),
i.e. exp of linear predictor.
- 'linear' returns the linear predictor of the mean function.
- 'var' returns the estimated variance of endog implied by the
model.
.. versionadded: 0.14
``which`` replaces and extends the deprecated ``linear``
argument.
linear : bool
If True, returns the linear predicted values. If False or None,
then the statistic specified by ``which`` will be returned.
.. deprecated: 0.14
The ``linear` keyword is deprecated and will be removed,
use ``which`` keyword instead.
Returns
-------
array
Fitted values at exog.
"""
if linear is not None:
msg = 'linear keyword is deprecated, use which="linear"'
warnings.warn(msg, FutureWarning)
if linear is True:
which = "linear"
# 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 exog is None:
exog = self.exog
linpred = np.dot(exog, params) + offset
if which == "mean":
return self.cdf(linpred)
elif which == "linear":
return linpred
if which == "var":
mu = self.cdf(linpred)
var_ = mu * (1 - mu)
return var_
else:
raise ValueError('Only `which` is "mean", "linear" or "var" are'
' available.')
@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=1, callback=None,
alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
qc_tol=0.03, **kwargs):
_validate_l1_method(method)
bnryfit = super().fit_regularized(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
alpha=alpha,
trim_mode=trim_mode,
auto_trim_tol=auto_trim_tol,
size_trim_tol=size_trim_tol,
qc_tol=qc_tol,
**kwargs)
discretefit = L1BinaryResults(self, bnryfit)
return L1BinaryResultsWrapper(discretefit)
def fit_constrained(self, constraints, start_params=None, **fit_kwds):
res = fit_constrained_wrap(self, constraints, start_params=None,
**fit_kwds)
return res
fit_constrained.__doc__ = fit_constrained_wrap.__doc__
def _derivative_predict(self, params, exog=None, transform='dydx',
offset=None):
"""
For computing marginal effects standard errors.
This is used only in the case of discrete and count regressors to
get the variance-covariance of the marginal effects. It returns
[d F / d params] where F is the predict.
Transform can be 'dydx' or 'eydx'. Checking is done in margeff
computations for appropriate transform.
"""
if exog is None:
exog = self.exog
linpred = self.predict(params, exog, offset=offset, which="linear")
dF = self.pdf(linpred)[:,None] * exog
if 'ey' in transform:
dF /= self.predict(params, exog, offset=offset)[:,None]
return dF
def _derivative_exog(self, params, exog=None, transform='dydx',
dummy_idx=None, count_idx=None, offset=None):
"""
For computing marginal effects returns dF(XB) / dX where F(.) is
the predicted probabilities
transform can be 'dydx', 'dyex', 'eydx', or 'eyex'.
Not all of these make sense in the presence of discrete regressors,
but checks are done in the results in get_margeff.
"""
# Note: this form should be appropriate for
# group 1 probit, logit, logistic, cloglog, heckprob, xtprobit
if exog is None:
exog = self.exog
linpred = self.predict(params, exog, offset=offset, which="linear")
margeff = np.dot(self.pdf(linpred)[:,None],
params[None,:])
if 'ex' in transform:
margeff *= exog
if 'ey' in transform:
margeff /= self.predict(params, exog)[:, None]
return self._derivative_exog_helper(margeff, params, exog,
dummy_idx, count_idx, transform)
def _deriv_mean_dparams(self, params):
"""
Derivative of the expected endog with respect to the parameters.
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
The value of the derivative of the expected endog with respect
to the parameter vector.
"""
link = self.link
lin_pred = self.predict(params, which="linear")
idl = link.inverse_deriv(lin_pred)
dmat = self.exog * idl[:, None]
return dmat
def get_distribution(self, params, exog=None, offset=None):
"""Get frozen instance of distribution based on predicted parameters.
Parameters
----------
params : array_like
The parameters of the model.
exog : ndarray, optional
Explanatory variables for the main count model.
If ``exog`` is None, then the data from the model will be used.
offset : ndarray, optional
Offset is added to the linear predictor of the mean function with
coefficient equal to 1.
Default is zero if exog is not None, and the model offset if exog
is None.
exposure : ndarray, optional
Log(exposure) is added to the linear predictor of the mean
function with coefficient equal to 1. If exposure is specified,
then it will be logged by the method. The user does not need to
log it first.
Default is one if exog is is not None, and it is the model exposure
if exog is None.
Returns
-------
Instance of frozen scipy distribution.
"""
mu = self.predict(params, exog=exog, offset=offset)
# distr = stats.bernoulli(mu[:, None])
distr = stats.bernoulli(mu)
return distr
class MultinomialModel(BinaryModel):
def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
if data_tools._is_using_ndarray_type(endog, None):
endog_dummies, ynames = _numpy_to_dummies(endog)
yname = 'y'
elif data_tools._is_using_pandas(endog, None):
endog_dummies, ynames, yname = _pandas_to_dummies(endog)
else:
endog = np.asarray(endog)
endog_dummies, ynames = _numpy_to_dummies(endog)
yname = 'y'
if not isinstance(ynames, dict):
ynames = dict(zip(range(endog_dummies.shape[1]), ynames))
self._ynames_map = ynames
data = handle_data(endog_dummies, exog, missing, hasconst, **kwargs)
data.ynames = yname # overwrite this to single endog name
data.orig_endog = endog
self.wendog = data.endog
# repeating from upstream...
for key in kwargs:
if key in ['design_info', 'formula']: # leave attached to data
continue
try:
setattr(self, key, data.__dict__.pop(key))
except KeyError:
pass
return data
def initialize(self):
"""
Preprocesses the data for MNLogit.
"""
super().initialize()
# This is also a "whiten" method in other models (eg regression)
self.endog = self.endog.argmax(1) # turn it into an array of col idx
self.J = self.wendog.shape[1]
self.K = self.exog.shape[1]
self.df_model *= (self.J-1) # for each J - 1 equation.
self.df_resid = self.exog.shape[0] - self.df_model - (self.J-1)
def predict(self, params, exog=None, which="mean", linear=None):
"""
Predict response variable of a model given exogenous variables.
Parameters
----------
params : array_like
2d array of fitted parameters of the model. Should be in the
order returned from the model.
exog : array_like
1d or 2d array of exogenous values. If not supplied, the
whole exog attribute of the model is used. If a 1d array is given
it assumed to be 1 row of exogenous variables. If you only have
one regressor and would like to do prediction, you must provide
a 2d array with shape[1] == 1.
which : {'mean', 'linear', 'var', 'prob'}, optional
Statistic to predict. Default is 'mean'.
- 'mean' returns the conditional expectation of endog E(y | x),
i.e. exp of linear predictor.
- 'linear' returns the linear predictor of the mean function.
- 'var' returns the estimated variance of endog implied by the
model.
.. versionadded: 0.14
``which`` replaces and extends the deprecated ``linear``
argument.
linear : bool
If True, returns the linear predicted values. If False or None,
then the statistic specified by ``which`` will be returned.
.. deprecated: 0.14
The ``linear` keyword is deprecated and will be removed,
use ``which`` keyword instead.
Notes
-----
Column 0 is the base case, the rest conform to the rows of params
shifted up one for the base case.
"""
if linear is not None:
msg = 'linear keyword is deprecated, use which="linear"'
warnings.warn(msg, FutureWarning)
if linear is True:
which = "linear"
if exog is None: # do here to accommodate user-given exog
exog = self.exog
if exog.ndim == 1:
exog = exog[None]
pred = super().predict(params, exog, which=which)
if which == "linear":
pred = np.column_stack((np.zeros(len(exog)), pred))
return pred
@Appender(DiscreteModel.fit.__doc__)
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
if start_params is None:
start_params = np.zeros(self.K * (self.J-1))
else:
start_params = np.asarray(start_params)
if callback is None:
# placeholder until check_perfect_pred
callback = lambda x, *args : None
# skip calling super to handle results from LikelihoodModel
mnfit = base.LikelihoodModel.fit(self, start_params = start_params,
method=method, maxiter=maxiter, full_output=full_output,
disp=disp, callback=callback, **kwargs)
mnfit.params = mnfit.params.reshape(self.K, -1, order='F')
mnfit = MultinomialResults(self, mnfit)
return MultinomialResultsWrapper(mnfit)
@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=1, callback=None,
alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
qc_tol=0.03, **kwargs):
if start_params is None:
start_params = np.zeros(self.K * (self.J-1))
else:
start_params = np.asarray(start_params)
mnfit = DiscreteModel.fit_regularized(
self, start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=disp, callback=callback,
alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
mnfit.params = mnfit.params.reshape(self.K, -1, order='F')
mnfit = L1MultinomialResults(self, mnfit)
return L1MultinomialResultsWrapper(mnfit)
def _derivative_predict(self, params, exog=None, transform='dydx'):
"""
For computing marginal effects standard errors.
This is used only in the case of discrete and count regressors to
get the variance-covariance of the marginal effects. It returns
[d F / d params] where F is the predicted probabilities for each
choice. dFdparams is of shape nobs x (J*K) x (J-1)*K.
The zero derivatives for the base category are not included.
Transform can be 'dydx' or 'eydx'. Checking is done in margeff
computations for appropriate transform.
"""
if exog is None:
exog = self.exog
if params.ndim == 1: # will get flatted from approx_fprime
params = params.reshape(self.K, self.J-1, order='F')
eXB = np.exp(np.dot(exog, params))
sum_eXB = (1 + eXB.sum(1))[:,None]
J = int(self.J)
K = int(self.K)
repeat_eXB = np.repeat(eXB, J, axis=1)
X = np.tile(exog, J-1)
# this is the derivative wrt the base level
F0 = -repeat_eXB * X / sum_eXB ** 2
# this is the derivative wrt the other levels when
# dF_j / dParams_j (ie., own equation)
#NOTE: this computes too much, any easy way to cut down?
F1 = eXB.T[:,:,None]*X * (sum_eXB - repeat_eXB) / (sum_eXB**2)
F1 = F1.transpose((1,0,2)) # put the nobs index first
# other equation index
other_idx = ~np.kron(np.eye(J-1), np.ones(K)).astype(bool)
F1[:, other_idx] = (-eXB.T[:,:,None]*X*repeat_eXB / \
(sum_eXB**2)).transpose((1,0,2))[:, other_idx]
dFdX = np.concatenate((F0[:, None,:], F1), axis=1)
if 'ey' in transform:
dFdX /= self.predict(params, exog)[:, :, None]
return dFdX
def _derivative_exog(self, params, exog=None, transform='dydx',
dummy_idx=None, count_idx=None):
"""
For computing marginal effects returns dF(XB) / dX where F(.) is
the predicted probabilities
transform can be 'dydx', 'dyex', 'eydx', or 'eyex'.
Not all of these make sense in the presence of discrete regressors,
but checks are done in the results in get_margeff.
For Multinomial models the marginal effects are
P[j] * (params[j] - sum_k P[k]*params[k])
It is returned unshaped, so that each row contains each of the J
equations. This makes it easier to take derivatives of this for
standard errors. If you want average marginal effects you can do
margeff.reshape(nobs, K, J, order='F).mean(0) and the marginal effects
for choice J are in column J
"""
J = int(self.J) # number of alternative choices
K = int(self.K) # number of variables
# Note: this form should be appropriate for
# group 1 probit, logit, logistic, cloglog, heckprob, xtprobit
if exog is None:
exog = self.exog
if params.ndim == 1: # will get flatted from approx_fprime
params = params.reshape(K, J-1, order='F')
zeroparams = np.c_[np.zeros(K), params] # add base in
cdf = self.cdf(np.dot(exog, params))
# TODO: meaningful interpretation for `iterm`?
iterm = np.array([cdf[:, [i]] * zeroparams[:, i]
for i in range(int(J))]).sum(0)
margeff = np.array([cdf[:, [j]] * (zeroparams[:, j] - iterm)
for j in range(J)])
# swap the axes to make sure margeff are in order nobs, K, J
margeff = np.transpose(margeff, (1, 2, 0))
if 'ex' in transform:
margeff *= exog
if 'ey' in transform:
margeff /= self.predict(params, exog)[:,None,:]
margeff = self._derivative_exog_helper(margeff, params, exog,
dummy_idx, count_idx, transform)
return margeff.reshape(len(exog), -1, order='F')
def get_distribution(self, params, exog=None, offset=None):
"""get frozen instance of distribution
"""
raise NotImplementedError
class CountModel(DiscreteModel):
def __init__(self, endog, exog, offset=None, exposure=None, missing='none',
check_rank=True, **kwargs):
self._check_kwargs(kwargs)
super().__init__(endog, exog, check_rank, missing=missing,
offset=offset, exposure=exposure, **kwargs)
if exposure is not None:
self.exposure = np.asarray(self.exposure)
self.exposure = np.log(self.exposure)
if offset is not None:
self.offset = np.asarray(self.offset)
self._check_inputs(self.offset, self.exposure, self.endog)
if offset is None:
delattr(self, 'offset')
if exposure is None:
delattr(self, 'exposure')
# promote dtype to float64 if needed
dt = np.promote_types(self.endog.dtype, np.float64)
self.endog = np.asarray(self.endog, dt)
dt = np.promote_types(self.exog.dtype, np.float64)
self.exog = np.asarray(self.exog, dt)
def _check_inputs(self, offset, exposure, endog):
if offset is not None and offset.shape[0] != endog.shape[0]:
raise ValueError("offset is not the same length as endog")
if exposure is not None and exposure.shape[0] != endog.shape[0]:
raise ValueError("exposure is not the same length as endog")
def _get_init_kwds(self):
# this is a temporary fixup because exposure has been transformed
# see #1609
kwds = super()._get_init_kwds()
if 'exposure' in kwds and kwds['exposure'] is not None:
kwds['exposure'] = np.exp(kwds['exposure'])
return kwds
def _get_predict_arrays(self, exog=None, offset=None, exposure=None):
# convert extras if not None
if exposure is not None:
exposure = np.log(np.asarray(exposure))
if offset is not None:
offset = np.asarray(offset)
# get defaults
if exog is None:
# prediction is in-sample
exog = self.exog
if exposure is None:
exposure = getattr(self, 'exposure', 0)
if offset is None:
offset = getattr(self, 'offset', 0)
else:
# user specified
exog = np.asarray(exog)
if exposure is None:
exposure = 0
if offset is None:
offset = 0
return exog, offset, exposure
def predict(self, params, exog=None, exposure=None, offset=None,
which='mean', linear=None):
"""
Predict response variable of a count model given exogenous variables
Parameters
----------
params : array_like
Model parameters
exog : array_like, optional
Design / exogenous data. Is exog is None, model exog is used.
exposure : array_like, optional
Log(exposure) is added to the linear prediction with
coefficient equal to 1. If exposure is not provided and exog
is None, uses the model's exposure if present. If not, uses
0 as the default value.
offset : array_like, optional
Offset is added to the linear prediction with coefficient
equal to 1. If offset is not provided and exog
is None, uses the model's offset if present. If not, uses
0 as the default value.
which : 'mean', 'linear', 'var', 'prob' (optional)
Statitistic to predict. Default is 'mean'.
- 'mean' returns the conditional expectation of endog E(y | x),
i.e. exp of linear predictor.
- 'linear' returns the linear predictor of the mean function.
- 'var' variance of endog implied by the likelihood model
- 'prob' predicted probabilities for counts.
.. versionadded: 0.14
``which`` replaces and extends the deprecated ``linear``
argument.
linear : bool
If True, returns the linear predicted values. If False or None,
then the statistic specified by ``which`` will be returned.
.. deprecated: 0.14
The ``linear` keyword is deprecated and will be removed,
use ``which`` keyword instead.
Notes
-----
If exposure is specified, then it will be logged by the method.
The user does not need to log it first.
"""
if linear is not None:
msg = 'linear keyword is deprecated, use which="linear"'
warnings.warn(msg, FutureWarning)
if linear is True:
which = "linear"
# the following is copied from GLM predict (without family/link check)
# 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.
# 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
fitted = np.dot(exog, params[:exog.shape[1]])
linpred = fitted + exposure + offset
if which == "mean":
return np.exp(linpred)
elif which.startswith("lin"):
return linpred
else:
raise ValueError('keyword which has to be "mean" and "linear"')
def _derivative_predict(self, params, exog=None, transform='dydx'):
"""
For computing marginal effects standard errors.
This is used only in the case of discrete and count regressors to
get the variance-covariance of the marginal effects. It returns
[d F / d params] where F is the predict.
Transform can be 'dydx' or 'eydx'. Checking is done in margeff
computations for appropriate transform.
"""
if exog is None:
exog = self.exog
#NOTE: this handles offset and exposure
dF = self.predict(params, exog)[:,None] * exog
if 'ey' in transform:
dF /= self.predict(params, exog)[:,None]
return dF
def _derivative_exog(self, params, exog=None, transform="dydx",
dummy_idx=None, count_idx=None):
"""
For computing marginal effects. These are the marginal effects
d F(XB) / dX
For the Poisson model F(XB) is the predicted counts rather than
the probabilities.
transform can be 'dydx', 'dyex', 'eydx', or 'eyex'.
Not all of these make sense in the presence of discrete regressors,
but checks are done in the results in get_margeff.
"""
# group 3 poisson, nbreg, zip, zinb
if exog is None:
exog = self.exog
k_extra = getattr(self, 'k_extra', 0)
params_exog = params if k_extra == 0 else params[:-k_extra]
margeff = self.predict(params, exog)[:,None] * params_exog[None,:]
if 'ex' in transform:
margeff *= exog
if 'ey' in transform:
margeff /= self.predict(params, exog)[:,None]
return self._derivative_exog_helper(margeff, params, exog,
dummy_idx, count_idx, transform)
def _deriv_mean_dparams(self, params):
"""
Derivative of the expected endog with respect to the parameters.
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
The value of the derivative of the expected endog with respect
to the parameter vector.
"""
from statsmodels.genmod.families import links
link = links.Log()
lin_pred = self.predict(params, which="linear")
idl = link.inverse_deriv(lin_pred)
dmat = self.exog * idl[:, None]
if self.k_extra > 0:
dmat_extra = np.zeros((dmat.shape[0], self.k_extra))
dmat = np.column_stack((dmat, dmat_extra))
return dmat
@Appender(DiscreteModel.fit.__doc__)
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
cntfit = super().fit(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
**kwargs)
discretefit = CountResults(self, cntfit)
return CountResultsWrapper(discretefit)
@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=1, callback=None,
alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
qc_tol=0.03, **kwargs):
_validate_l1_method(method)
cntfit = super().fit_regularized(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
alpha=alpha,
trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
size_trim_tol=size_trim_tol,
qc_tol=qc_tol,
**kwargs)
discretefit = L1CountResults(self, cntfit)
return L1CountResultsWrapper(discretefit)
# Public Model Classes
class Poisson(CountModel):
__doc__ = """
Poisson Model
%(params)s
%(extra_params)s
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
""" % {'params': base._model_params_doc,
'extra_params':
"""offset : array_like
Offset is added to the linear prediction with coefficient equal to 1.
exposure : array_like
Log(exposure) is added to the linear prediction with coefficient
equal to 1.
""" + base._missing_param_doc + _check_rank_doc}
@cache_readonly
def family(self):
from statsmodels.genmod import families
return families.Poisson()
def cdf(self, X):
"""
Poisson model cumulative distribution function
Parameters
----------
X : array_like
`X` is the linear predictor of the model. See notes.
Returns
-------
The value of the Poisson CDF at each point.
Notes
-----
The CDF is defined as
.. math:: \\exp\\left(-\\lambda\\right)\\sum_{i=0}^{y}\\frac{\\lambda^{i}}{i!}
where :math:`\\lambda` assumes the loglinear model. I.e.,
.. math:: \\ln\\lambda_{i}=X\\beta
The parameter `X` is :math:`X\\beta` in the above formula.
"""
y = self.endog
return stats.poisson.cdf(y, np.exp(X))
def pdf(self, X):
"""
Poisson model probability mass function
Parameters
----------
X : array_like
`X` is the linear predictor of the model. See notes.
Returns
-------
pdf : ndarray
The value of the Poisson probability mass function, PMF, for each
point of X.
Notes
-----
The PMF is defined as
.. math:: \\frac{e^{-\\lambda_{i}}\\lambda_{i}^{y_{i}}}{y_{i}!}
where :math:`\\lambda` assumes the loglinear model. I.e.,
.. math:: \\ln\\lambda_{i}=x_{i}\\beta
The parameter `X` is :math:`x_{i}\\beta` in the above formula.
"""
y = self.endog
return np.exp(stats.poisson.logpmf(y, np.exp(X)))
def loglike(self, params):
"""
Loglikelihood of Poisson model
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : float
The log-likelihood function of the model evaluated at `params`.
See notes.
Notes
-----
.. math:: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right]
"""
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
XB = np.dot(self.exog, params) + offset + exposure
endog = self.endog
return np.sum(
-np.exp(np.clip(XB, None, EXP_UPPER_LIMIT))
+ endog * XB
- gammaln(endog + 1)
)
def loglikeobs(self, params):
"""
Loglikelihood for observations of Poisson model
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : array_like
The log likelihood for each observation of the model evaluated
at `params`. See Notes
Notes
-----
.. math:: \\ln L_{i}=\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right]
for observations :math:`i=1,...,n`
"""
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
XB = np.dot(self.exog, params) + offset + exposure
endog = self.endog
#np.sum(stats.poisson.logpmf(endog, np.exp(XB)))
return -np.exp(XB) + endog*XB - gammaln(endog+1)
@Appender(_get_start_params_null_docs)
def _get_start_params_null(self):
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
const = (self.endog / np.exp(offset + exposure)).mean()
params = [np.log(const)]
return params
@Appender(DiscreteModel.fit.__doc__)
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
if start_params is None and self.data.const_idx is not None:
# k_params or k_exog not available?
start_params = 0.001 * np.ones(self.exog.shape[1])
start_params[self.data.const_idx] = self._get_start_params_null()[0]
kwds = {}
if kwargs.get('cov_type') is not None:
kwds['cov_type'] = kwargs.get('cov_type')
kwds['cov_kwds'] = kwargs.get('cov_kwds', {})
cntfit = super(CountModel, self).fit(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
**kwargs)
discretefit = PoissonResults(self, cntfit, **kwds)
return PoissonResultsWrapper(discretefit)
@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=1, callback=None,
alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
qc_tol=0.03, **kwargs):
_validate_l1_method(method)
cntfit = super(CountModel, self).fit_regularized(
start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=disp, callback=callback,
alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
discretefit = L1PoissonResults(self, cntfit)
return L1PoissonResultsWrapper(discretefit)
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
"""
#constraints = (R, q)
# TODO: temporary trailing underscore to not overwrite the monkey
# patched version
# TODO: decide whether to move the imports
from patsy import DesignInfo
from statsmodels.base._constraints import (fit_constrained,
LinearConstraints)
# 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(maxiter=0, method='nm', disp=0,
warn_convergence=False) # we get a wrapper back
res.mle_retvals['fcall'] = res_constr.mle_retvals.get('fcall', np.nan)
res.mle_retvals['iterations'] = res_constr.mle_retvals.get(
'iterations', np.nan)
res.mle_retvals['converged'] = res_constr.mle_retvals['converged']
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 # assume scale=1
else:
res._results.normalized_cov_params = None
k_constr = len(q)
res._results.df_resid += k_constr
res._results.df_model -= k_constr
res._results.constraints = LinearConstraints.from_patsy(lc)
res._results.k_constr = k_constr
res._results.results_constrained = res_constr
return res
def score(self, params):
"""
Poisson model score (gradient) vector of the log-likelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score : ndarray, 1-D
The score vector of the model, i.e. the first derivative of the
loglikelihood function, evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left(y_{i}-\\lambda_{i}\\right)x_{i}
where the loglinear model is assumed
.. math:: \\ln\\lambda_{i}=x_{i}\\beta
"""
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
X = self.exog
L = np.exp(np.dot(X,params) + offset + exposure)
return np.dot(self.endog - L, X)
def score_obs(self, params):
"""
Poisson model Jacobian of the log-likelihood for each observation
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score : array_like
The score vector (nobs, k_vars) of the model evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\lambda_{i}\\right)x_{i}
for observations :math:`i=1,...,n`
where the loglinear model is assumed
.. math:: \\ln\\lambda_{i}=x_{i}\\beta
"""
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
X = self.exog
L = np.exp(np.dot(X,params) + offset + exposure)
return (self.endog - L)[:,None] * X
def score_factor(self, params):
"""
Poisson model score_factor for each observation
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score : array_like
The score factor (nobs, ) of the model evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\lambda_{i}\\right)
for observations :math:`i=1,...,n`
where the loglinear model is assumed
.. math:: \\ln\\lambda_{i}=x_{i}\\beta
"""
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
X = self.exog
L = np.exp(np.dot(X,params) + offset + exposure)
return (self.endog - L)
def hessian(self, params):
"""
Poisson model Hessian matrix of the loglikelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hess : ndarray, (k_vars, k_vars)
The Hessian, second derivative of loglikelihood function,
evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i=1}^{n}\\lambda_{i}x_{i}x_{i}^{\\prime}
where the loglinear model is assumed
.. math:: \\ln\\lambda_{i}=x_{i}\\beta
"""
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
X = self.exog
L = np.exp(np.dot(X,params) + exposure + offset)
return -np.dot(L*X.T, X)
def hessian_factor(self, params):
"""
Poisson model Hessian factor
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hess : ndarray, (nobs,)
The Hessian factor, second derivative of loglikelihood function
with respect to the linear predictor evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i=1}^{n}\\lambda_{i}
where the loglinear model is assumed
.. math:: \\ln\\lambda_{i}=x_{i}\\beta
"""
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
X = self.exog
L = np.exp(np.dot(X,params) + exposure + offset)
return -L
def _deriv_score_obs_dendog(self, params, scale=None):
"""derivative of score_obs w.r.t. endog
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
-------
derivative : ndarray_2d
The derivative of the score_obs with respect to endog. This
can is given by `score_factor0[:, None] * exog` where
`score_factor0` is the score_factor without the residual.
"""
return self.exog
def predict(self, params, exog=None, exposure=None, offset=None,
which='mean', linear=None, y_values=None):
"""
Predict response variable of a model given exogenous variables.
Parameters
----------
params : array_like
2d array of fitted parameters of the model. Should be in the
order returned from the model.
exog : array_like, optional
1d or 2d array of exogenous values. If not supplied, then the
exog attribute of the model is used. If a 1d array is given
it assumed to be 1 row of exogenous variables. If you only have
one regressor and would like to do prediction, you must provide
a 2d array with shape[1] == 1.
offset : array_like, optional
Offset is added to the linear predictor with coefficient equal
to 1.
Default is zero if exog is not None, and the model offset if exog
is None.
exposure : array_like, optional
Log(exposure) is added to the linear prediction with coefficient
equal to 1.
Default is one if exog is is not None, and is the model exposure
if exog is None.
which : 'mean', 'linear', 'var', 'prob' (optional)
Statitistic to predict. Default is 'mean'.
- 'mean' returns the conditional expectation of endog E(y | x),
i.e. exp of linear predictor.
- 'linear' returns the linear predictor of the mean function.
- 'var' returns the estimated variance of endog implied by the
model.
- 'prob' return probabilities for counts from 0 to max(endog) or
for y_values if those are provided.
.. versionadded: 0.14
``which`` replaces and extends the deprecated ``linear``
argument.
linear : bool
The ``linear` keyword is deprecated and will be removed,
use ``which`` keyword instead.
If True, returns the linear predicted values. If False or None,
then the statistic specified by ``which`` will be returned.
.. deprecated: 0.14
The ``linear` keyword is deprecated and will be removed,
use ``which`` keyword instead.
y_values : array_like
Values of the random variable endog at which pmf is evaluated.
Only used if ``which="prob"``
"""
# Note docstring is reused by other count models
if linear is not None:
msg = 'linear keyword is deprecated, use which="linear"'
warnings.warn(msg, FutureWarning)
if linear is True:
which = "linear"
if which.startswith("lin"):
which = "linear"
if which in ["mean", "linear"]:
return super().predict(params, exog=exog, exposure=exposure,
offset=offset,
which=which, linear=linear)
# TODO: add full set of which
elif which == "var":
mu = self.predict(params, exog=exog,
exposure=exposure, offset=offset,
)
return mu
elif which == "prob":
if y_values is not None:
y_values = np.atleast_2d(y_values)
else:
y_values = np.atleast_2d(
np.arange(0, np.max(self.endog) + 1))
mu = self.predict(params, exog=exog,
exposure=exposure, offset=offset,
)[:, None]
# uses broadcasting
return stats.poisson._pmf(y_values, mu)
else:
raise ValueError('Value of the `which` option is not recognized')
def _prob_nonzero(self, mu, params=None):
"""Probability that count is not zero
internal use in Censored model, will be refactored or removed
"""
prob_nz = - np.expm1(-mu)
return prob_nz
def _var(self, mu, params=None):
"""variance implied by the distribution
internal use, will be refactored or removed
"""
return mu
def get_distribution(self, params, exog=None, exposure=None, offset=None):
"""Get frozen instance of distribution based on predicted parameters.
Parameters
----------
params : array_like
The parameters of the model.
exog : ndarray, optional
Explanatory variables for the main count model.
If ``exog`` is None, then the data from the model will be used.
offset : ndarray, optional
Offset is added to the linear predictor of the mean function with
coefficient equal to 1.
Default is zero if exog is not None, and the model offset if exog
is None.
exposure : ndarray, optional
Log(exposure) is added to the linear predictor of the mean
function with coefficient equal to 1. If exposure is specified,
then it will be logged by the method. The user does not need to
log it first.
Default is one if exog is is not None, and it is the model exposure
if exog is None.
Returns
-------
Instance of frozen scipy distribution subclass.
"""
mu = self.predict(params, exog=exog, exposure=exposure, offset=offset)
distr = stats.poisson(mu)
return distr
class GeneralizedPoisson(CountModel):
__doc__ = """
Generalized Poisson Model
%(params)s
%(extra_params)s
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
""" % {'params': base._model_params_doc,
'extra_params':
"""
p : scalar
P denotes parameterizations for GP regression. p=1 for GP-1 and
p=2 for GP-2. Default is p=1.
offset : array_like
Offset is added to the linear prediction with coefficient equal to 1.
exposure : array_like
Log(exposure) is added to the linear prediction with coefficient
equal to 1.""" + base._missing_param_doc + _check_rank_doc}
def __init__(self, endog, exog, p=1, offset=None,
exposure=None, missing='none', check_rank=True, **kwargs):
super().__init__(endog,
exog,
offset=offset,
exposure=exposure,
missing=missing,
check_rank=check_rank,
**kwargs)
self.parameterization = p - 1
self.exog_names.append('alpha')
self.k_extra = 1
self._transparams = False
def _get_init_kwds(self):
kwds = super()._get_init_kwds()
kwds['p'] = self.parameterization + 1
return kwds
def _get_exogs(self):
return (self.exog, None)
def loglike(self, params):
"""
Loglikelihood of Generalized Poisson model
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : float
The log-likelihood function of the model evaluated at `params`.
See notes.
Notes
-----
.. math:: \\ln L=\\sum_{i=1}^{n}\\left[\\mu_{i}+(y_{i}-1)*ln(\\mu_{i}+
\\alpha*\\mu_{i}^{p-1}*y_{i})-y_{i}*ln(1+\\alpha*\\mu_{i}^{p-1})-
ln(y_{i}!)-\\frac{\\mu_{i}+\\alpha*\\mu_{i}^{p-1}*y_{i}}{1+\\alpha*
\\mu_{i}^{p-1}}\\right]
"""
return np.sum(self.loglikeobs(params))
def loglikeobs(self, params):
"""
Loglikelihood for observations of Generalized Poisson model
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : ndarray
The log likelihood for each observation of the model evaluated
at `params`. See Notes
Notes
-----
.. math:: \\ln L=\\sum_{i=1}^{n}\\left[\\mu_{i}+(y_{i}-1)*ln(\\mu_{i}+
\\alpha*\\mu_{i}^{p-1}*y_{i})-y_{i}*ln(1+\\alpha*\\mu_{i}^{p-1})-
ln(y_{i}!)-\\frac{\\mu_{i}+\\alpha*\\mu_{i}^{p-1}*y_{i}}{1+\\alpha*
\\mu_{i}^{p-1}}\\right]
for observations :math:`i=1,...,n`
"""
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = self.parameterization
endog = self.endog
mu = self.predict(params)
mu_p = np.power(mu, p)
a1 = 1 + alpha * mu_p
a2 = mu + (a1 - 1) * endog
a1 = np.maximum(1e-20, a1)
a2 = np.maximum(1e-20, a2)
return (np.log(mu) + (endog - 1) * np.log(a2) - endog *
np.log(a1) - gammaln(endog + 1) - a2 / a1)
@Appender(_get_start_params_null_docs)
def _get_start_params_null(self):
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
const = (self.endog / np.exp(offset + exposure)).mean()
params = [np.log(const)]
mu = const * np.exp(offset + exposure)
resid = self.endog - mu
a = self._estimate_dispersion(mu, resid, df_resid=resid.shape[0] - 1)
params.append(a)
return np.array(params)
def _estimate_dispersion(self, mu, resid, df_resid=None):
q = self.parameterization
if df_resid is None:
df_resid = resid.shape[0]
a = ((np.abs(resid) / np.sqrt(mu) - 1) * mu**(-q)).sum() / df_resid
return a
@Appender(
"""
use_transparams : bool
This parameter enable internal transformation to impose
non-negativity. True to enable. Default is False.
use_transparams=True imposes the no underdispersion (alpha > 0)
constraint. In case use_transparams=True and method="newton" or
"ncg" transformation is ignored.
""")
@Appender(DiscreteModel.fit.__doc__)
def fit(self, start_params=None, method='bfgs', maxiter=35,
full_output=1, disp=1, callback=None, use_transparams=False,
cov_type='nonrobust', cov_kwds=None, use_t=None, optim_kwds_prelim=None,
**kwargs):
if use_transparams and method not in ['newton', 'ncg']:
self._transparams = True
else:
if use_transparams:
warnings.warn('Parameter "use_transparams" is ignored',
RuntimeWarning)
self._transparams = False
if start_params is None:
offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
if np.size(offset) == 1 and offset == 0:
offset = None
kwds_prelim = {'disp': 0, 'skip_hessian': True,
'warn_convergence': False}
if optim_kwds_prelim is not None:
kwds_prelim.update(optim_kwds_prelim)
mod_poi = Poisson(self.endog, self.exog, offset=offset)
with warnings.catch_warnings():
warnings.simplefilter("always")
res_poi = mod_poi.fit(**kwds_prelim)
start_params = res_poi.params
a = self._estimate_dispersion(res_poi.predict(), res_poi.resid,
df_resid=res_poi.df_resid)
start_params = np.append(start_params, max(-0.1, a))
if callback is None:
# work around perfect separation callback #3895
callback = lambda *x: x
mlefit = super().fit(start_params=start_params,
maxiter=maxiter,
method=method,
disp=disp,
full_output=full_output,
callback=callback,
**kwargs)
if optim_kwds_prelim is not None:
mlefit.mle_settings["optim_kwds_prelim"] = optim_kwds_prelim
if use_transparams and method not in ["newton", "ncg"]:
self._transparams = False
mlefit._results.params[-1] = np.exp(mlefit._results.params[-1])
gpfit = GeneralizedPoissonResults(self, mlefit._results)
result = GeneralizedPoissonResultsWrapper(gpfit)
if cov_kwds is None:
cov_kwds = {}
result._get_robustcov_results(cov_type=cov_type,
use_self=True, use_t=use_t, **cov_kwds)
return result
@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=1, callback=None,
alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
qc_tol=0.03, **kwargs):
_validate_l1_method(method)
if np.size(alpha) == 1 and alpha != 0:
k_params = self.exog.shape[1] + self.k_extra
alpha = alpha * np.ones(k_params)
alpha[-1] = 0
alpha_p = alpha[:-1] if (self.k_extra and np.size(alpha) > 1) else alpha
self._transparams = False
if start_params is None:
offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
if np.size(offset) == 1 and offset == 0:
offset = None
mod_poi = Poisson(self.endog, self.exog, offset=offset)
with warnings.catch_warnings():
warnings.simplefilter("always")
start_params = mod_poi.fit_regularized(
start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=0, callback=callback,
alpha=alpha_p, trim_mode=trim_mode,
auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol,
qc_tol=qc_tol, **kwargs).params
start_params = np.append(start_params, 0.1)
cntfit = super(CountModel, self).fit_regularized(
start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=disp, callback=callback,
alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
discretefit = L1GeneralizedPoissonResults(self, cntfit)
return L1GeneralizedPoissonResultsWrapper(discretefit)
def score_obs(self, params):
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = self.parameterization
exog = self.exog
y = self.endog[:,None]
mu = self.predict(params)[:,None]
mu_p = np.power(mu, p)
a1 = 1 + alpha * mu_p
a2 = mu + alpha * mu_p * y
a3 = alpha * p * mu ** (p - 1)
a4 = a3 * y
dmudb = mu * exog
dalpha = (mu_p * (y * ((y - 1) / a2 - 2 / a1) + a2 / a1**2))
dparams = dmudb * (-a4 / a1 +
a3 * a2 / (a1 ** 2) +
(1 + a4) * ((y - 1) / a2 - 1 / a1) +
1 / mu)
return np.concatenate((dparams, np.atleast_2d(dalpha)),
axis=1)
def score(self, params):
score = np.sum(self.score_obs(params), axis=0)
if self._transparams:
score[-1] == score[-1] ** 2
return score
else:
return score
def score_factor(self, params, endog=None):
params = np.asarray(params)
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = self.parameterization
y = self.endog if endog is None else endog
mu = self.predict(params)
mu_p = np.power(mu, p)
a1 = 1 + alpha * mu_p
a2 = mu + alpha * mu_p * y
a3 = alpha * p * mu ** (p - 1)
a4 = a3 * y
dmudb = mu
dalpha = (mu_p * (y * ((y - 1) / a2 - 2 / a1) + a2 / a1**2))
dparams = dmudb * (-a4 / a1 +
a3 * a2 / (a1 ** 2) +
(1 + a4) * ((y - 1) / a2 - 1 / a1) +
1 / mu)
return dparams, dalpha
def _score_p(self, params):
"""
Generalized Poisson model derivative of the log-likelihood by p-parameter
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
dldp : float
dldp is first derivative of the loglikelihood function,
evaluated at `p-parameter`.
"""
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = self.parameterization
y = self.endog[:,None]
mu = self.predict(params)[:,None]
mu_p = np.power(mu, p)
a1 = 1 + alpha * mu_p
a2 = mu + alpha * mu_p * y
dp = np.sum(np.log(mu) * ((a2 - mu) * ((y - 1) / a2 - 2 / a1) +
(a1 - 1) * a2 / a1 ** 2))
return dp
def hessian(self, params):
"""
Generalized Poisson model Hessian matrix of the loglikelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hess : ndarray, (k_vars, k_vars)
The Hessian, second derivative of loglikelihood function,
evaluated at `params`
"""
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = self.parameterization
exog = self.exog
y = self.endog[:,None]
mu = self.predict(params)[:,None]
mu_p = np.power(mu, p)
a1 = 1 + alpha * mu_p
a2 = mu + alpha * mu_p * y
a3 = alpha * p * mu ** (p - 1)
a4 = a3 * y
a5 = p * mu ** (p - 1)
dmudb = mu * exog
# for dl/dparams dparams
dim = exog.shape[1]
hess_arr = np.empty((dim+1,dim+1))
for i in range(dim):
for j in range(i + 1):
hess_val = np.sum(mu * exog[:,i,None] * exog[:,j,None] *
(mu * (a3 * a4 / a1**2 -
2 * a3**2 * a2 / a1**3 +
2 * a3 * (a4 + 1) / a1**2 -
a4 * p / (mu * a1) +
a3 * p * a2 / (mu * a1**2) +
(y - 1) * a4 * (p - 1) / (a2 * mu) -
(y - 1) * (1 + a4)**2 / a2**2 -
a4 * (p - 1) / (a1 * mu)) +
((y - 1) * (1 + a4) / a2 -
(1 + a4) / a1)), axis=0)
hess_arr[i, j] = np.squeeze(hess_val)
tri_idx = np.triu_indices(dim, k=1)
hess_arr[tri_idx] = hess_arr.T[tri_idx]
# for dl/dparams dalpha
dldpda = np.sum((2 * a4 * mu_p / a1**2 -
2 * a3 * mu_p * a2 / a1**3 -
mu_p * y * (y - 1) * (1 + a4) / a2**2 +
mu_p * (1 + a4) / a1**2 +
a5 * y * (y - 1) / a2 -
2 * a5 * y / a1 +
a5 * a2 / a1**2) * dmudb,
axis=0)
hess_arr[-1,:-1] = dldpda
hess_arr[:-1,-1] = dldpda
# for dl/dalpha dalpha
dldada = mu_p**2 * (3 * y / a1**2 -
(y / a2)**2. * (y - 1) -
2 * a2 / a1**3)
hess_arr[-1,-1] = dldada.sum()
return hess_arr
def hessian_factor(self, params):
"""
Generalized Poisson model Hessian matrix of the loglikelihood
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
hess : ndarray, (nobs, 3)
The Hessian factor, second derivative of loglikelihood function
with respect to linear predictor and dispersion parameter
evaluated at `params`
The first column contains the second derivative w.r.t. linpred,
the second column contains the cross derivative, and the
third column contains the second derivative w.r.t. the dispersion
parameter.
"""
params = np.asarray(params)
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = self.parameterization
y = self.endog
mu = self.predict(params)
mu_p = np.power(mu, p)
a1 = 1 + alpha * mu_p
a2 = mu + alpha * mu_p * y
a3 = alpha * p * mu ** (p - 1)
a4 = a3 * y
a5 = p * mu ** (p - 1)
dmudb = mu
dbb = mu * (
mu * (a3 * a4 / a1**2 -
2 * a3**2 * a2 / a1**3 +
2 * a3 * (a4 + 1) / a1**2 -
a4 * p / (mu * a1) +
a3 * p * a2 / (mu * a1**2) +
a4 / (mu * a1) -
a3 * a2 / (mu * a1**2) +
(y - 1) * a4 * (p - 1) / (a2 * mu) -
(y - 1) * (1 + a4)**2 / a2**2 -
a4 * (p - 1) / (a1 * mu) -
1 / mu**2) +
(-a4 / a1 +
a3 * a2 / a1**2 +
(y - 1) * (1 + a4) / a2 -
(1 + a4) / a1 +
1 / mu))
# for dl/dlinpred dalpha
dba = ((2 * a4 * mu_p / a1**2 -
2 * a3 * mu_p * a2 / a1**3 -
mu_p * y * (y - 1) * (1 + a4) / a2**2 +
mu_p * (1 + a4) / a1**2 +
a5 * y * (y - 1) / a2 -
2 * a5 * y / a1 +
a5 * a2 / a1**2) * dmudb)
# for dl/dalpha dalpha
daa = mu_p**2 * (3 * y / a1**2 -
(y / a2)**2. * (y - 1) -
2 * a2 / a1**3)
return dbb, dba, daa
@Appender(Poisson.predict.__doc__)
def predict(self, params, exog=None, exposure=None, offset=None,
which='mean', y_values=None):
if exog is None:
exog = self.exog
if exposure is None:
exposure = getattr(self, 'exposure', 0)
elif exposure != 0:
exposure = np.log(exposure)
if offset is None:
offset = getattr(self, 'offset', 0)
fitted = np.dot(exog, params[:exog.shape[1]])
linpred = fitted + exposure + offset
if which == 'mean':
return np.exp(linpred)
elif which == 'linear':
return linpred
elif which == 'var':
mean = np.exp(linpred)
alpha = params[-1]
pm1 = self.parameterization # `p - 1` in GPP
var_ = mean * (1 + alpha * mean**pm1)**2
return var_
elif which == 'prob':
if y_values is None:
y_values = np.atleast_2d(np.arange(0, np.max(self.endog)+1))
mu = self.predict(params, exog=exog, exposure=exposure,
offset=offset)[:, None]
return genpoisson_p.pmf(y_values, mu, params[-1],
self.parameterization + 1)
else:
raise ValueError('keyword \'which\' not recognized')
def _deriv_score_obs_dendog(self, params):
"""derivative of score_obs w.r.t. endog
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
derivative : ndarray_2d
The derivative of the score_obs with respect to endog.
"""
# code duplication with NegativeBinomialP
from statsmodels.tools.numdiff import _approx_fprime_cs_scalar
def f(y):
if y.ndim == 2 and y.shape[1] == 1:
y = y[:, 0]
sf = self.score_factor(params, endog=y)
return np.column_stack(sf)
dsf = _approx_fprime_cs_scalar(self.endog[:, None], f)
# deriv is 2d vector
d1 = dsf[:, :1] * self.exog
d2 = dsf[:, 1:2]
return np.column_stack((d1, d2))
def _var(self, mu, params=None):
"""variance implied by the distribution
internal use, will be refactored or removed
"""
alpha = params[-1]
pm1 = self.parameterization # `p-1` in GPP
var_ = mu * (1 + alpha * mu**pm1)**2
return var_
def _prob_nonzero(self, mu, params):
"""Probability that count is not zero
internal use in Censored model, will be refactored or removed
"""
alpha = params[-1]
pm1 = self.parameterization # p-1 in GPP
prob_zero = np.exp(- mu / (1 + alpha * mu**pm1))
prob_nz = 1 - prob_zero
return prob_nz
@Appender(Poisson.get_distribution.__doc__)
def get_distribution(self, params, exog=None, exposure=None, offset=None):
"""get frozen instance of distribution
"""
mu = self.predict(params, exog=exog, exposure=exposure, offset=offset)
p = self.parameterization + 1
# distr = genpoisson_p(mu[:, None], params[-1], p)
distr = genpoisson_p(mu, params[-1], p)
return distr
class Logit(BinaryModel):
__doc__ = """
Logit Model
{params}
offset : array_like
Offset is added to the linear prediction with coefficient equal to 1.
{extra_params}
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
""".format(params=base._model_params_doc,
extra_params=base._missing_param_doc + _check_rank_doc)
_continuous_ok = True
@cache_readonly
def link(self):
from statsmodels.genmod.families import links
link = links.Logit()
return link
def cdf(self, X):
"""
The logistic cumulative distribution function
Parameters
----------
X : array_like
`X` is the linear predictor of the logit model. See notes.
Returns
-------
1/(1 + exp(-X))
Notes
-----
In the logit model,
.. math:: \\Lambda\\left(x^{\\prime}\\beta\\right)=
\\text{Prob}\\left(Y=1|x\\right)=
\\frac{e^{x^{\\prime}\\beta}}{1+e^{x^{\\prime}\\beta}}
"""
X = np.asarray(X)
return 1/(1+np.exp(-X))
def pdf(self, X):
"""
The logistic probability density function
Parameters
----------
X : array_like
`X` is the linear predictor of the logit model. See notes.
Returns
-------
pdf : ndarray
The value of the Logit probability mass function, PMF, for each
point of X. ``np.exp(-x)/(1+np.exp(-X))**2``
Notes
-----
In the logit model,
.. math:: \\lambda\\left(x^{\\prime}\\beta\\right)=\\frac{e^{-x^{\\prime}\\beta}}{\\left(1+e^{-x^{\\prime}\\beta}\\right)^{2}}
"""
X = np.asarray(X)
return np.exp(-X)/(1+np.exp(-X))**2
@cache_readonly
def family(self):
from statsmodels.genmod import families
return families.Binomial()
def loglike(self, params):
"""
Log-likelihood of logit model.
Parameters
----------
params : array_like
The parameters of the logit model.
Returns
-------
loglike : float
The log-likelihood function of the model evaluated at `params`.
See notes.
Notes
-----
.. math::
\\ln L=\\sum_{i}\\ln\\Lambda
\\left(q_{i}x_{i}^{\\prime}\\beta\\right)
Where :math:`q=2y-1`. This simplification comes from the fact that the
logistic distribution is symmetric.
"""
q = 2*self.endog - 1
linpred = self.predict(params, which="linear")
return np.sum(np.log(self.cdf(q * linpred)))
def loglikeobs(self, params):
"""
Log-likelihood of logit model for each observation.
Parameters
----------
params : array_like
The parameters of the logit model.
Returns
-------
loglike : ndarray
The log likelihood for each observation of the model evaluated
at `params`. See Notes
Notes
-----
.. math::
\\ln L=\\sum_{i}\\ln\\Lambda
\\left(q_{i}x_{i}^{\\prime}\\beta\\right)
for observations :math:`i=1,...,n`
where :math:`q=2y-1`. This simplification comes from the fact that the
logistic distribution is symmetric.
"""
q = 2*self.endog - 1
linpred = self.predict(params, which="linear")
return np.log(self.cdf(q * linpred))
def score(self, params):
"""
Logit model score (gradient) vector of the log-likelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score : ndarray, 1-D
The score vector of the model, i.e. the first derivative of the
loglikelihood function, evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left(y_{i}-\\Lambda_{i}\\right)x_{i}
"""
y = self.endog
X = self.exog
fitted = self.predict(params)
return np.dot(y - fitted, X)
def score_obs(self, params):
"""
Logit model Jacobian of the log-likelihood for each observation
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
jac : array_like
The derivative of the loglikelihood for each observation evaluated
at `params`.
Notes
-----
.. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\Lambda_{i}\\right)x_{i}
for observations :math:`i=1,...,n`
"""
y = self.endog
X = self.exog
fitted = self.predict(params)
return (y - fitted)[:,None] * X
def score_factor(self, params):
"""
Logit model derivative of the log-likelihood with respect to linpred.
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score_factor : array_like
The derivative of the loglikelihood for each observation evaluated
at `params`.
Notes
-----
.. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\lambda_{i}\\right)
for observations :math:`i=1,...,n`
where the loglinear model is assumed
.. math:: \\ln\\lambda_{i}=x_{i}\\beta
"""
y = self.endog
fitted = self.predict(params)
return (y - fitted)
def hessian(self, params):
"""
Logit model Hessian matrix of the log-likelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hess : ndarray, (k_vars, k_vars)
The Hessian, second derivative of loglikelihood function,
evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i}\\Lambda_{i}\\left(1-\\Lambda_{i}\\right)x_{i}x_{i}^{\\prime}
"""
X = self.exog
L = self.predict(params)
return -np.dot(L*(1-L)*X.T,X)
def hessian_factor(self, params):
"""
Logit model Hessian factor
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hess : ndarray, (nobs,)
The Hessian factor, second derivative of loglikelihood function
with respect to the linear predictor evaluated at `params`
"""
L = self.predict(params)
return -L * (1 - L)
@Appender(DiscreteModel.fit.__doc__)
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
bnryfit = super().fit(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
**kwargs)
discretefit = LogitResults(self, bnryfit)
return BinaryResultsWrapper(discretefit)
def _deriv_score_obs_dendog(self, params):
"""derivative of score_obs w.r.t. endog
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
derivative : ndarray_2d
The derivative of the score_obs with respect to endog. This
can is given by `score_factor0[:, None] * exog` where
`score_factor0` is the score_factor without the residual.
"""
return self.exog
class Probit(BinaryModel):
__doc__ = """
Probit Model
{params}
offset : array_like
Offset is added to the linear prediction with coefficient equal to 1.
{extra_params}
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
""".format(params=base._model_params_doc,
extra_params=base._missing_param_doc + _check_rank_doc)
@cache_readonly
def link(self):
from statsmodels.genmod.families import links
link = links.Probit()
return link
def cdf(self, X):
"""
Probit (Normal) cumulative distribution function
Parameters
----------
X : array_like
The linear predictor of the model (XB).
Returns
-------
cdf : ndarray
The cdf evaluated at `X`.
Notes
-----
This function is just an alias for scipy.stats.norm.cdf
"""
return stats.norm._cdf(X)
def pdf(self, X):
"""
Probit (Normal) probability density function
Parameters
----------
X : array_like
The linear predictor of the model (XB).
Returns
-------
pdf : ndarray
The value of the normal density function for each point of X.
Notes
-----
This function is just an alias for scipy.stats.norm.pdf
"""
X = np.asarray(X)
return stats.norm._pdf(X)
def loglike(self, params):
"""
Log-likelihood of probit model (i.e., the normal distribution).
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : float
The log-likelihood function of the model evaluated at `params`.
See notes.
Notes
-----
.. math:: \\ln L=\\sum_{i}\\ln\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)
Where :math:`q=2y-1`. This simplification comes from the fact that the
normal distribution is symmetric.
"""
q = 2*self.endog - 1
linpred = self.predict(params, which="linear")
return np.sum(np.log(np.clip(self.cdf(q * linpred), FLOAT_EPS, 1)))
def loglikeobs(self, params):
"""
Log-likelihood of probit model for each observation
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : array_like
The log likelihood for each observation of the model evaluated
at `params`. See Notes
Notes
-----
.. math:: \\ln L_{i}=\\ln\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)
for observations :math:`i=1,...,n`
where :math:`q=2y-1`. This simplification comes from the fact that the
normal distribution is symmetric.
"""
q = 2*self.endog - 1
linpred = self.predict(params, which="linear")
return np.log(np.clip(self.cdf(q*linpred), FLOAT_EPS, 1))
def score(self, params):
"""
Probit model score (gradient) vector
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score : ndarray, 1-D
The score vector of the model, i.e. the first derivative of the
loglikelihood function, evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left[\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}\\right]x_{i}
Where :math:`q=2y-1`. This simplification comes from the fact that the
normal distribution is symmetric.
"""
y = self.endog
X = self.exog
XB = self.predict(params, which="linear")
q = 2*y - 1
# clip to get rid of invalid divide complaint
L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS)
return np.dot(L,X)
def score_obs(self, params):
"""
Probit model Jacobian for each observation
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
jac : array_like
The derivative of the loglikelihood for each observation evaluated
at `params`.
Notes
-----
.. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left[\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}\\right]x_{i}
for observations :math:`i=1,...,n`
Where :math:`q=2y-1`. This simplification comes from the fact that the
normal distribution is symmetric.
"""
y = self.endog
X = self.exog
XB = self.predict(params, which="linear")
q = 2*y - 1
# clip to get rid of invalid divide complaint
L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS)
return L[:,None] * X
def score_factor(self, params):
"""
Probit model Jacobian for each observation
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
score_factor : array_like (nobs,)
The derivative of the loglikelihood function for each observation
with respect to linear predictor evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left[\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}\\right]x_{i}
for observations :math:`i=1,...,n`
Where :math:`q=2y-1`. This simplification comes from the fact that the
normal distribution is symmetric.
"""
y = self.endog
XB = self.predict(params, which="linear")
q = 2*y - 1
# clip to get rid of invalid divide complaint
L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS)
return L
def hessian(self, params):
"""
Probit model Hessian matrix of the log-likelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hess : ndarray, (k_vars, k_vars)
The Hessian, second derivative of loglikelihood function,
evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\lambda_{i}\\left(\\lambda_{i}+x_{i}^{\\prime}\\beta\\right)x_{i}x_{i}^{\\prime}
where
.. math:: \\lambda_{i}=\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}
and :math:`q=2y-1`
"""
X = self.exog
XB = self.predict(params, which="linear")
q = 2*self.endog - 1
L = q*self.pdf(q*XB)/self.cdf(q*XB)
return np.dot(-L*(L+XB)*X.T,X)
def hessian_factor(self, params):
"""
Probit model Hessian factor of the log-likelihood
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
hess : ndarray, (nobs,)
The Hessian factor, second derivative of loglikelihood function
with respect to linear predictor evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\lambda_{i}\\left(\\lambda_{i}+x_{i}^{\\prime}\\beta\\right)x_{i}x_{i}^{\\prime}
where
.. math:: \\lambda_{i}=\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}
and :math:`q=2y-1`
"""
XB = self.predict(params, which="linear")
q = 2 * self.endog - 1
L = q * self.pdf(q * XB) / self.cdf(q * XB)
return -L * (L + XB)
@Appender(DiscreteModel.fit.__doc__)
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
bnryfit = super().fit(start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
callback=callback,
**kwargs)
discretefit = ProbitResults(self, bnryfit)
return BinaryResultsWrapper(discretefit)
def _deriv_score_obs_dendog(self, params):
"""derivative of score_obs w.r.t. endog
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
derivative : ndarray_2d
The derivative of the score_obs with respect to endog. This
can is given by `score_factor0[:, None] * exog` where
`score_factor0` is the score_factor without the residual.
"""
linpred = self.predict(params, which="linear")
pdf_ = self.pdf(linpred)
# clip to get rid of invalid divide complaint
cdf_ = np.clip(self.cdf(linpred), FLOAT_EPS, 1 - FLOAT_EPS)
deriv = pdf_ / cdf_ / (1 - cdf_) # deriv factor
return deriv[:, None] * self.exog
class MNLogit(MultinomialModel):
__doc__ = """
Multinomial Logit Model
Parameters
----------
endog : array_like
`endog` is an 1-d vector of the endogenous response. `endog` can
contain strings, ints, or floats or may be a pandas Categorical Series.
Note that if it contains strings, every distinct string will be a
category. No stripping of whitespace is done.
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. See `statsmodels.tools.add_constant`.
{extra_params}
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
J : float
The number of choices for the endogenous variable. Note that this
is zero-indexed.
K : float
The actual number of parameters for the exogenous design. Includes
the constant if the design has one.
names : dict
A dictionary mapping the column number in `wendog` to the variables
in `endog`.
wendog : ndarray
An n x j array where j is the number of unique categories in `endog`.
Each column of j is a dummy variable indicating the category of
each observation. See `names` for a dictionary mapping each column to
its category.
Notes
-----
See developer notes for further information on `MNLogit` internals.
""".format(extra_params=base._missing_param_doc + _check_rank_doc)
def __init__(self, endog, exog, check_rank=True, **kwargs):
super().__init__(endog, exog, check_rank=check_rank, **kwargs)
# Override cov_names since multivariate model
yname = self.endog_names
ynames = self._ynames_map
ynames = MultinomialResults._maybe_convert_ynames_int(ynames)
# use range below to ensure sortedness
ynames = [ynames[key] for key in range(int(self.J))]
idx = MultiIndex.from_product((ynames[1:], self.data.xnames),
names=(yname, None))
self.data.cov_names = idx
def pdf(self, eXB):
"""
NotImplemented
"""
raise NotImplementedError
def cdf(self, X):
"""
Multinomial logit cumulative distribution function.
Parameters
----------
X : ndarray
The linear predictor of the model XB.
Returns
-------
cdf : ndarray
The cdf evaluated at `X`.
Notes
-----
In the multinomial logit model.
.. math:: \\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}
"""
eXB = np.column_stack((np.ones(len(X)), np.exp(X)))
return eXB/eXB.sum(1)[:,None]
def loglike(self, params):
"""
Log-likelihood of the multinomial logit model.
Parameters
----------
params : array_like
The parameters of the multinomial logit model.
Returns
-------
loglike : float
The log-likelihood function of the model evaluated at `params`.
See notes.
Notes
-----
.. math::
\\ln L=\\sum_{i=1}^{n}\\sum_{j=0}^{J}d_{ij}\\ln
\\left(\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}
{\\sum_{k=0}^{J}
\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)
where :math:`d_{ij}=1` if individual `i` chose alternative `j` and 0
if not.
"""
params = params.reshape(self.K, -1, order='F')
d = self.wendog
logprob = np.log(self.cdf(np.dot(self.exog,params)))
return np.sum(d * logprob)
def loglikeobs(self, params):
"""
Log-likelihood of the multinomial logit model for each observation.
Parameters
----------
params : array_like
The parameters of the multinomial logit model.
Returns
-------
loglike : array_like
The log likelihood for each observation of the model evaluated
at `params`. See Notes
Notes
-----
.. math::
\\ln L_{i}=\\sum_{j=0}^{J}d_{ij}\\ln
\\left(\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}
{\\sum_{k=0}^{J}
\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)
for observations :math:`i=1,...,n`
where :math:`d_{ij}=1` if individual `i` chose alternative `j` and 0
if not.
"""
params = params.reshape(self.K, -1, order='F')
d = self.wendog
logprob = np.log(self.cdf(np.dot(self.exog,params)))
return d * logprob
def score(self, params):
"""
Score matrix for multinomial logit model log-likelihood
Parameters
----------
params : ndarray
The parameters of the multinomial logit model.
Returns
-------
score : ndarray, (K * (J-1),)
The 2-d score vector, i.e. the first derivative of the
loglikelihood function, of the multinomial logit model evaluated at
`params`.
Notes
-----
.. math:: \\frac{\\partial\\ln L}{\\partial\\beta_{j}}=\\sum_{i}\\left(d_{ij}-\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)x_{i}
for :math:`j=1,...,J`
In the multinomial model the score matrix is K x J-1 but is returned
as a flattened array to work with the solvers.
"""
params = params.reshape(self.K, -1, order='F')
firstterm = self.wendog[:,1:] - self.cdf(np.dot(self.exog,
params))[:,1:]
#NOTE: might need to switch terms if params is reshaped
return np.dot(firstterm.T, self.exog).flatten()
def loglike_and_score(self, params):
"""
Returns log likelihood and score, efficiently reusing calculations.
Note that both of these returned quantities will need to be negated
before being minimized by the maximum likelihood fitting machinery.
"""
params = params.reshape(self.K, -1, order='F')
cdf_dot_exog_params = self.cdf(np.dot(self.exog, params))
loglike_value = np.sum(self.wendog * np.log(cdf_dot_exog_params))
firstterm = self.wendog[:, 1:] - cdf_dot_exog_params[:, 1:]
score_array = np.dot(firstterm.T, self.exog).flatten()
return loglike_value, score_array
def score_obs(self, params):
"""
Jacobian matrix for multinomial logit model log-likelihood
Parameters
----------
params : ndarray
The parameters of the multinomial logit model.
Returns
-------
jac : array_like
The derivative of the loglikelihood for each observation evaluated
at `params` .
Notes
-----
.. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta_{j}}=\\left(d_{ij}-\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)x_{i}
for :math:`j=1,...,J`, for observations :math:`i=1,...,n`
In the multinomial model the score vector is K x (J-1) but is returned
as a flattened array. The Jacobian has the observations in rows and
the flattened array of derivatives in columns.
"""
params = params.reshape(self.K, -1, order='F')
firstterm = self.wendog[:,1:] - self.cdf(np.dot(self.exog,
params))[:,1:]
#NOTE: might need to switch terms if params is reshaped
return (firstterm[:,:,None] * self.exog[:,None,:]).reshape(self.exog.shape[0], -1)
def hessian(self, params):
"""
Multinomial logit Hessian matrix of the log-likelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hess : ndarray, (J*K, J*K)
The Hessian, second derivative of loglikelihood function with
respect to the flattened parameters, evaluated at `params`
Notes
-----
.. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta_{j}\\partial\\beta_{l}}=-\\sum_{i=1}^{n}\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\left[\\boldsymbol{1}\\left(j=l\\right)-\\frac{\\exp\\left(\\beta_{l}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right]x_{i}x_{l}^{\\prime}
where
:math:`\\boldsymbol{1}\\left(j=l\\right)` equals 1 if `j` = `l` and 0
otherwise.
The actual Hessian matrix has J**2 * K x K elements. Our Hessian
is reshaped to be square (J*K, J*K) so that the solvers can use it.
This implementation does not take advantage of the symmetry of
the Hessian and could probably be refactored for speed.
"""
params = params.reshape(self.K, -1, order='F')
X = self.exog
pr = self.cdf(np.dot(X,params))
partials = []
J = self.J
K = self.K
for i in range(J-1):
for j in range(J-1): # this loop assumes we drop the first col.
if i == j:
partials.append(\
-np.dot(((pr[:,i+1]*(1-pr[:,j+1]))[:,None]*X).T,X))
else:
partials.append(-np.dot(((pr[:,i+1]*-pr[:,j+1])[:,None]*X).T,X))
H = np.array(partials)
# the developer's notes on multinomial should clear this math up
H = np.transpose(H.reshape(J-1, J-1, K, K), (0, 2, 1, 3)).reshape((J-1)*K, (J-1)*K)
return H
#TODO: Weibull can replaced by a survival analsysis function
# like stat's streg (The cox model as well)
#class Weibull(DiscreteModel):
# """
# Binary choice Weibull model
#
# Notes
# ------
# This is unfinished and untested.
# """
##TODO: add analytic hessian for Weibull
# def initialize(self):
# pass
#
# def cdf(self, X):
# """
# Gumbell (Log Weibull) cumulative distribution function
# """
## return np.exp(-np.exp(-X))
# return stats.gumbel_r.cdf(X)
# # these two are equivalent.
# # Greene table and discussion is incorrect.
#
# def pdf(self, X):
# """
# Gumbell (LogWeibull) probability distribution function
# """
# return stats.gumbel_r.pdf(X)
#
# def loglike(self, params):
# """
# Loglikelihood of Weibull distribution
# """
# X = self.exog
# cdf = self.cdf(np.dot(X,params))
# y = self.endog
# return np.sum(y*np.log(cdf) + (1-y)*np.log(1-cdf))
#
# def score(self, params):
# y = self.endog
# X = self.exog
# F = self.cdf(np.dot(X,params))
# f = self.pdf(np.dot(X,params))
# term = (y*f/F + (1 - y)*-f/(1-F))
# return np.dot(term,X)
#
# def hessian(self, params):
# hess = nd.Jacobian(self.score)
# return hess(params)
#
# def fit(self, start_params=None, method='newton', maxiter=35, tol=1e-08):
## The example had problems with all zero start values, Hessian = 0
# if start_params is None:
# start_params = OLS(self.endog, self.exog).fit().params
# mlefit = super(Weibull, self).fit(start_params=start_params,
# method=method, maxiter=maxiter, tol=tol)
# return mlefit
#
class NegativeBinomial(CountModel):
__doc__ = """
Negative Binomial Model
%(params)s
%(extra_params)s
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
References
----------
Greene, W. 2008. "Functional forms for the negative binomial model
for count data". Economics Letters. Volume 99, Number 3, pp.585-590.
Hilbe, J.M. 2011. "Negative binomial regression". Cambridge University
Press.
""" % {'params': base._model_params_doc,
'extra_params':
"""loglike_method : str
Log-likelihood type. 'nb2','nb1', or 'geometric'.
Fitted value :math:`\\mu`
Heterogeneity parameter :math:`\\alpha`
- nb2: Variance equal to :math:`\\mu + \\alpha\\mu^2` (most common)
- nb1: Variance equal to :math:`\\mu + \\alpha\\mu`
- geometric: Variance equal to :math:`\\mu + \\mu^2`
offset : array_like
Offset is added to the linear prediction with coefficient equal to 1.
exposure : array_like
Log(exposure) is added to the linear prediction with coefficient
equal to 1.
""" + base._missing_param_doc + _check_rank_doc}
def __init__(self, endog, exog, loglike_method='nb2', offset=None,
exposure=None, missing='none', check_rank=True, **kwargs):
super().__init__(endog,
exog,
offset=offset,
exposure=exposure,
missing=missing,
check_rank=check_rank,
**kwargs)
self.loglike_method = loglike_method
self._initialize()
if loglike_method in ['nb2', 'nb1']:
self.exog_names.append('alpha')
self.k_extra = 1
else:
self.k_extra = 0
# store keys for extras if we need to recreate model instance
# we need to append keys that do not go to super
self._init_keys.append('loglike_method')
def _initialize(self):
if self.loglike_method == 'nb2':
self.hessian = self._hessian_nb2
self.score = self._score_nbin
self.loglikeobs = self._ll_nb2
self._transparams = True # transform lnalpha -> alpha in fit
elif self.loglike_method == 'nb1':
self.hessian = self._hessian_nb1
self.score = self._score_nb1
self.loglikeobs = self._ll_nb1
self._transparams = True # transform lnalpha -> alpha in fit
elif self.loglike_method == 'geometric':
self.hessian = self._hessian_geom
self.score = self._score_geom
self.loglikeobs = self._ll_geometric
else:
raise ValueError('Likelihood type must "nb1", "nb2" '
'or "geometric"')
# Workaround to pickle instance methods
def __getstate__(self):
odict = self.__dict__.copy() # copy the dict since we change it
del odict['hessian']
del odict['score']
del odict['loglikeobs']
return odict
def __setstate__(self, indict):
self.__dict__.update(indict)
self._initialize()
def _ll_nbin(self, params, alpha, Q=0):
if np.any(np.iscomplex(params)) or np.iscomplex(alpha):
gamma_ln = loggamma
else:
gamma_ln = gammaln
endog = self.endog
mu = self.predict(params)
size = 1/alpha * mu**Q
prob = size/(size+mu)
coeff = (gamma_ln(size+endog) - gamma_ln(endog+1) -
gamma_ln(size))
llf = coeff + size*np.log(prob) + endog*np.log(1-prob)
return llf
def _ll_nb2(self, params):
if self._transparams: # got lnalpha during fit
alpha = np.exp(params[-1])
else:
alpha = params[-1]
return self._ll_nbin(params[:-1], alpha, Q=0)
def _ll_nb1(self, params):
if self._transparams: # got lnalpha during fit
alpha = np.exp(params[-1])
else:
alpha = params[-1]
return self._ll_nbin(params[:-1], alpha, Q=1)
def _ll_geometric(self, params):
# we give alpha of 1 because it's actually log(alpha) where alpha=0
return self._ll_nbin(params, 1, 0)
def loglike(self, params):
r"""
Loglikelihood for negative binomial model
Parameters
----------
params : array_like
The parameters of the model. If `loglike_method` is nb1 or
nb2, then the ancillary parameter is expected to be the
last element.
Returns
-------
llf : float
The loglikelihood value at `params`
Notes
-----
Following notation in Greene (2008), with negative binomial
heterogeneity parameter :math:`\alpha`:
.. math::
\lambda_i &= exp(X\beta) \\
\theta &= 1 / \alpha \\
g_i &= \theta \lambda_i^Q \\
w_i &= g_i/(g_i + \lambda_i) \\
r_i &= \theta / (\theta+\lambda_i) \\
ln \mathcal{L}_i &= ln \Gamma(y_i+g_i) - ln \Gamma(1+y_i) + g_iln (r_i) + y_i ln(1-r_i)
where :math`Q=0` for NB2 and geometric and :math:`Q=1` for NB1.
For the geometric, :math:`\alpha=0` as well.
"""
llf = np.sum(self.loglikeobs(params))
return llf
def _score_geom(self, params):
exog = self.exog
y = self.endog[:, None]
mu = self.predict(params)[:, None]
dparams = exog * (y-mu)/(mu+1)
return dparams.sum(0)
def _score_nbin(self, params, Q=0):
"""
Score vector for NB2 model
"""
if self._transparams: # lnalpha came in during fit
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
exog = self.exog
y = self.endog[:,None]
mu = self.predict(params)[:,None]
a1 = 1/alpha * mu**Q
prob = a1 / (a1 + mu) # a1 aka "size" in _ll_nbin
if Q == 1: # nb1
# Q == 1 --> a1 = mu / alpha --> prob = 1 / (alpha + 1)
dgpart = digamma(y + a1) - digamma(a1)
dparams = exog * a1 * (np.log(prob) +
dgpart)
dalpha = ((alpha * (y - mu * np.log(prob) -
mu*(dgpart + 1)) -
mu * (np.log(prob) +
dgpart))/
(alpha**2*(alpha + 1))).sum()
elif Q == 0: # nb2
dgpart = digamma(y + a1) - digamma(a1)
dparams = exog*a1 * (y-mu)/(mu+a1)
da1 = -alpha**-2
dalpha = (dgpart + np.log(a1)
- np.log(a1+mu) - (y-mu)/(a1+mu)).sum() * da1
#multiply above by constant outside sum to reduce rounding error
if self._transparams:
return np.r_[dparams.sum(0), dalpha*alpha]
else:
return np.r_[dparams.sum(0), dalpha]
def _score_nb1(self, params):
return self._score_nbin(params, Q=1)
def _hessian_geom(self, params):
exog = self.exog
y = self.endog[:,None]
mu = self.predict(params)[:,None]
# for dl/dparams dparams
dim = exog.shape[1]
hess_arr = np.empty((dim, dim))
const_arr = mu*(1+y)/(mu+1)**2
for i in range(dim):
for j in range(dim):
if j > i:
continue
hess_arr[i,j] = np.squeeze(
np.sum(-exog[:,i,None] * exog[:,j,None] * const_arr,
axis=0
)
)
tri_idx = np.triu_indices(dim, k=1)
hess_arr[tri_idx] = hess_arr.T[tri_idx]
return hess_arr
def _hessian_nb1(self, params):
"""
Hessian of NB1 model.
"""
if self._transparams: # lnalpha came in during fit
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
exog = self.exog
y = self.endog[:,None]
mu = self.predict(params)[:,None]
a1 = mu/alpha
dgpart = digamma(y + a1) - digamma(a1)
prob = 1 / (1 + alpha) # equiv: a1 / (a1 + mu)
# for dl/dparams dparams
dim = exog.shape[1]
hess_arr = np.empty((dim+1,dim+1))
#const_arr = a1*mu*(a1+y)/(mu+a1)**2
# not all of dparams
dparams = exog / alpha * (np.log(prob) +
dgpart)
dmudb = exog*mu
xmu_alpha = exog * a1
trigamma = (special.polygamma(1, a1 + y) -
special.polygamma(1, a1))
for i in range(dim):
for j in range(dim):
if j > i:
continue
hess_arr[i,j] = np.squeeze(
np.sum(
dparams[:,i,None] * dmudb[:,j,None] +
xmu_alpha[:,i,None] * xmu_alpha[:,j,None] * trigamma,
axis=0
)
)
tri_idx = np.triu_indices(dim, k=1)
hess_arr[tri_idx] = hess_arr.T[tri_idx]
# for dl/dparams dalpha
# da1 = -alpha**-2
dldpda = np.sum(-a1 * dparams + exog * a1 *
(-trigamma*mu/alpha**2 - prob), axis=0)
hess_arr[-1,:-1] = dldpda
hess_arr[:-1,-1] = dldpda
log_alpha = np.log(prob)
alpha3 = alpha**3
alpha2 = alpha**2
mu2 = mu**2
dada = ((alpha3*mu*(2*log_alpha + 2*dgpart + 3) -
2*alpha3*y +
4*alpha2*mu*(log_alpha + dgpart) +
alpha2 * (2*mu - y) +
2*alpha*mu2*trigamma + mu2 * trigamma + alpha2 * mu2 * trigamma +
2*alpha*mu*(log_alpha + dgpart)
)/(alpha**4*(alpha2 + 2*alpha + 1)))
hess_arr[-1,-1] = dada.sum()
return hess_arr
def _hessian_nb2(self, params):
"""
Hessian of NB2 model.
"""
if self._transparams: # lnalpha came in during fit
alpha = np.exp(params[-1])
else:
alpha = params[-1]
a1 = 1/alpha
params = params[:-1]
exog = self.exog
y = self.endog[:,None]
mu = self.predict(params)[:,None]
prob = a1 / (a1 + mu)
dgpart = digamma(a1 + y) - digamma(a1)
# for dl/dparams dparams
dim = exog.shape[1]
hess_arr = np.empty((dim+1,dim+1))
const_arr = a1*mu*(a1+y)/(mu+a1)**2
for i in range(dim):
for j in range(dim):
if j > i:
continue
hess_arr[i,j] = np.sum(-exog[:,i,None] * exog[:,j,None] *
const_arr, axis=0).squeeze()
tri_idx = np.triu_indices(dim, k=1)
hess_arr[tri_idx] = hess_arr.T[tri_idx]
# for dl/dparams dalpha
da1 = -alpha**-2
dldpda = -np.sum(mu*exog*(y-mu)*a1**2/(mu+a1)**2 , axis=0)
hess_arr[-1,:-1] = dldpda
hess_arr[:-1,-1] = dldpda
# for dl/dalpha dalpha
#NOTE: polygamma(1,x) is the trigamma function
da2 = 2*alpha**-3
dalpha = da1 * (dgpart +
np.log(prob) - (y - mu)/(a1+mu))
dada = (da2 * dalpha/da1 + da1**2 * (special.polygamma(1, a1+y) -
special.polygamma(1, a1) + 1/a1 - 1/(a1 + mu) +
(y - mu)/(mu + a1)**2)).sum()
hess_arr[-1,-1] = dada
return hess_arr
#TODO: replace this with analytic where is it used?
def score_obs(self, params):
sc = approx_fprime_cs(params, self.loglikeobs)
return sc
@Appender(Poisson.predict.__doc__)
def predict(self, params, exog=None, exposure=None, offset=None,
which='mean', linear=None, y_values=None):
if linear is not None:
msg = 'linear keyword is deprecated, use which="linear"'
warnings.warn(msg, FutureWarning)
if linear is True:
which = "linear"
# avoid duplicate computation for get-distribution
if which == "prob":
distr = self.get_distribution(
params,
exog=exog,
exposure=exposure,
offset=offset
)
if y_values is None:
y_values = np.arange(0, np.max(self.endog) + 1)
else:
y_values = np.asarray(y_values)
assert y_values.ndim == 1
y_values = y_values[..., None]
return distr.pmf(y_values).T
exog, offset, exposure = self._get_predict_arrays(
exog=exog,
offset=offset,
exposure=exposure
)
fitted = np.dot(exog, params[:exog.shape[1]])
linpred = fitted + exposure + offset
if which == "mean":
return np.exp(linpred)
elif which.startswith("lin"):
return linpred
elif which == "var":
mu = np.exp(linpred)
if self.loglike_method == 'geometric':
var_ = mu * (1 + mu)
else:
if self.loglike_method == 'nb2':
p = 2
elif self.loglike_method == 'nb1':
p = 1
alpha = params[-1]
var_ = mu * (1 + alpha * mu**(p - 1))
return var_
else:
raise ValueError('keyword which has to be "mean" and "linear"')
@Appender(_get_start_params_null_docs)
def _get_start_params_null(self):
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
const = (self.endog / np.exp(offset + exposure)).mean()
params = [np.log(const)]
mu = const * np.exp(offset + exposure)
resid = self.endog - mu
a = self._estimate_dispersion(mu, resid, df_resid=resid.shape[0] - 1)
params.append(a)
return np.array(params)
def _estimate_dispersion(self, mu, resid, df_resid=None):
if df_resid is None:
df_resid = resid.shape[0]
if self.loglike_method == 'nb2':
#params.append(np.linalg.pinv(mu[:,None]).dot(resid**2 / mu - 1))
a = ((resid**2 / mu - 1) / mu).sum() / df_resid
else: #self.loglike_method == 'nb1':
a = (resid**2 / mu - 1).sum() / df_resid
return a
def fit(self, start_params=None, method='bfgs', maxiter=35,
full_output=1, disp=1, callback=None,
cov_type='nonrobust', cov_kwds=None, use_t=None,
optim_kwds_prelim=None, **kwargs):
# Note: do not let super handle robust covariance because it has
# transformed params
self._transparams = False # always define attribute
if self.loglike_method.startswith('nb') and method not in ['newton',
'ncg']:
self._transparams = True # in case same Model instance is refit
elif self.loglike_method.startswith('nb'): # method is newton/ncg
self._transparams = False # because we need to step in alpha space
if start_params is None:
# Use poisson fit as first guess.
#TODO, Warning: this assumes exposure is logged
offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
if np.size(offset) == 1 and offset == 0:
offset = None
kwds_prelim = {'disp': 0, 'skip_hessian': True, 'warn_convergence': False}
if optim_kwds_prelim is not None:
kwds_prelim.update(optim_kwds_prelim)
mod_poi = Poisson(self.endog, self.exog, offset=offset)
with warnings.catch_warnings():
warnings.simplefilter("always")
res_poi = mod_poi.fit(**kwds_prelim)
start_params = res_poi.params
if self.loglike_method.startswith('nb'):
a = self._estimate_dispersion(res_poi.predict(), res_poi.resid,
df_resid=res_poi.df_resid)
start_params = np.append(start_params, max(0.05, a))
else:
if self._transparams is True:
# transform user provided start_params dispersion, see #3918
start_params = np.array(start_params, copy=True)
start_params[-1] = np.log(start_params[-1])
if callback is None:
# work around perfect separation callback #3895
callback = lambda *x: x
mlefit = super().fit(start_params=start_params,
maxiter=maxiter, method=method, disp=disp,
full_output=full_output, callback=callback,
**kwargs)
if optim_kwds_prelim is not None:
mlefit.mle_settings["optim_kwds_prelim"] = optim_kwds_prelim
# TODO: Fix NBin _check_perfect_pred
if self.loglike_method.startswith('nb'):
# mlefit is a wrapped counts results
self._transparams = False # do not need to transform anymore now
# change from lnalpha to alpha
if method not in ["newton", "ncg"]:
mlefit._results.params[-1] = np.exp(mlefit._results.params[-1])
nbinfit = NegativeBinomialResults(self, mlefit._results)
result = NegativeBinomialResultsWrapper(nbinfit)
else:
result = mlefit
if cov_kwds is None:
cov_kwds = {} #TODO: make this unnecessary ?
result._get_robustcov_results(cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds)
return result
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=1, callback=None,
alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
qc_tol=0.03, **kwargs):
_validate_l1_method(method)
if self.loglike_method.startswith('nb') and (np.size(alpha) == 1 and
alpha != 0):
# do not penalize alpha if alpha is scalar
k_params = self.exog.shape[1] + self.k_extra
alpha = alpha * np.ones(k_params)
alpha[-1] = 0
# alpha for regularized poisson to get starting values
alpha_p = alpha[:-1] if (self.k_extra and np.size(alpha) > 1) else alpha
self._transparams = False
if start_params is None:
# Use poisson fit as first guess.
#TODO, Warning: this assumes exposure is logged
offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
if np.size(offset) == 1 and offset == 0:
offset = None
mod_poi = Poisson(self.endog, self.exog, offset=offset)
with warnings.catch_warnings():
warnings.simplefilter("always")
start_params = mod_poi.fit_regularized(
start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=0, callback=callback,
alpha=alpha_p, trim_mode=trim_mode,
auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol,
qc_tol=qc_tol, **kwargs).params
if self.loglike_method.startswith('nb'):
start_params = np.append(start_params, 0.1)
cntfit = super(CountModel, self).fit_regularized(
start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=disp, callback=callback,
alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
discretefit = L1NegativeBinomialResults(self, cntfit)
return L1NegativeBinomialResultsWrapper(discretefit)
@Appender(Poisson.get_distribution.__doc__)
def get_distribution(self, params, exog=None, exposure=None, offset=None):
"""get frozen instance of distribution
"""
mu = self.predict(params, exog=exog, exposure=exposure, offset=offset)
if self.loglike_method == 'geometric':
# distr = stats.geom(1 / (1 + mu[:, None]), loc=-1)
distr = stats.geom(1 / (1 + mu), loc=-1)
else:
if self.loglike_method == 'nb2':
p = 2
elif self.loglike_method == 'nb1':
p = 1
alpha = params[-1]
q = 2 - p
size = 1. / alpha * mu**q
prob = size / (size + mu)
# distr = nbinom(size[:, None], prob[:, None])
distr = nbinom(size, prob)
return distr
class NegativeBinomialP(CountModel):
__doc__ = """
Generalized Negative Binomial (NB-P) Model
%(params)s
%(extra_params)s
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
p : scalar
P denotes parameterizations for NB-P regression. p=1 for NB-1 and
p=2 for NB-2. Default is p=1.
""" % {'params': base._model_params_doc,
'extra_params':
"""p : scalar
P denotes parameterizations for NB regression. p=1 for NB-1 and
p=2 for NB-2. Default is p=2.
offset : array_like
Offset is added to the linear prediction with coefficient equal to 1.
exposure : array_like
Log(exposure) is added to the linear prediction with coefficient
equal to 1.
""" + base._missing_param_doc + _check_rank_doc}
def __init__(self, endog, exog, p=2, offset=None,
exposure=None, missing='none', check_rank=True,
**kwargs):
super().__init__(endog,
exog,
offset=offset,
exposure=exposure,
missing=missing,
check_rank=check_rank,
**kwargs)
self.parameterization = p
self.exog_names.append('alpha')
self.k_extra = 1
self._transparams = False
def _get_init_kwds(self):
kwds = super()._get_init_kwds()
kwds['p'] = self.parameterization
return kwds
def _get_exogs(self):
return (self.exog, None)
def loglike(self, params):
"""
Loglikelihood of Generalized Negative Binomial (NB-P) model
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : float
The log-likelihood function of the model evaluated at `params`.
See notes.
"""
return np.sum(self.loglikeobs(params))
def loglikeobs(self, params):
"""
Loglikelihood for observations of Generalized Negative Binomial (NB-P) model
Parameters
----------
params : array_like
The parameters of the model.
Returns
-------
loglike : ndarray
The log likelihood for each observation of the model evaluated
at `params`. See Notes
"""
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = self.parameterization
y = self.endog
mu = self.predict(params)
mu_p = mu**(2 - p)
a1 = mu_p / alpha
a2 = mu + a1
llf = (gammaln(y + a1) - gammaln(y + 1) - gammaln(a1) +
a1 * np.log(a1) + y * np.log(mu) -
(y + a1) * np.log(a2))
return llf
def score_obs(self, params):
"""
Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations.
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score : ndarray, 1-D
The score vector of the model, i.e. the first derivative of the
loglikelihood function, evaluated at `params`
"""
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = 2 - self.parameterization
y = self.endog
mu = self.predict(params)
mu_p = mu**p
a1 = mu_p / alpha
a2 = mu + a1
a3 = y + a1
a4 = p * a1 / mu
dgpart = digamma(a3) - digamma(a1)
dgterm = dgpart + np.log(a1 / a2) + 1 - a3 / a2
# TODO: better name/interpretation for dgterm?
dparams = (a4 * dgterm -
a3 / a2 +
y / mu)
dparams = (self.exog.T * mu * dparams).T
dalpha = -a1 / alpha * dgterm
return np.concatenate((dparams, np.atleast_2d(dalpha).T),
axis=1)
def score(self, params):
"""
Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
score : ndarray, 1-D
The score vector of the model, i.e. the first derivative of the
loglikelihood function, evaluated at `params`
"""
score = np.sum(self.score_obs(params), axis=0)
if self._transparams:
score[-1] == score[-1] ** 2
return score
else:
return score
def score_factor(self, params, endog=None):
"""
Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations.
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
score : ndarray, 1-D
The score vector of the model, i.e. the first derivative of the
loglikelihood function, evaluated at `params`
"""
params = np.asarray(params)
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = 2 - self.parameterization
y = self.endog if endog is None else endog
mu = self.predict(params)
mu_p = mu**p
a1 = mu_p / alpha
a2 = mu + a1
a3 = y + a1
a4 = p * a1 / mu
dgpart = digamma(a3) - digamma(a1)
dparams = ((a4 * dgpart -
a3 / a2) +
y / mu + a4 * (1 - a3 / a2 + np.log(a1 / a2)))
dparams = (mu * dparams).T
dalpha = (-a1 / alpha * (dgpart +
np.log(a1 / a2) +
1 - a3 / a2))
return dparams, dalpha
def hessian(self, params):
"""
Generalized Negative Binomial (NB-P) model hessian maxtrix of the log-likelihood
Parameters
----------
params : array_like
The parameters of the model
Returns
-------
hessian : ndarray, 2-D
The hessian matrix of the model.
"""
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = 2 - self.parameterization
y = self.endog
exog = self.exog
mu = self.predict(params)
mu_p = mu**p
a1 = mu_p / alpha
a2 = mu + a1
a3 = y + a1
a4 = p * a1 / mu
prob = a1 / a2
lprob = np.log(prob)
dgpart = digamma(a3) - digamma(a1)
pgpart = polygamma(1, a3) - polygamma(1, a1)
dim = exog.shape[1]
hess_arr = np.zeros((dim + 1, dim + 1))
coeff = mu**2 * (((1 + a4)**2 * a3 / a2**2 -
a3 / a2 * (p - 1) * a4 / mu -
y / mu**2 -
2 * a4 * (1 + a4) / a2 +
p * a4 / mu * (lprob + dgpart + 2) -
a4 / mu * (lprob + dgpart + 1) +
a4**2 * pgpart) +
(-(1 + a4) * a3 / a2 +
y / mu +
a4 * (lprob + dgpart + 1)) / mu)
for i in range(dim):
hess_arr[i, :-1] = np.sum(self.exog[:, :].T * self.exog[:, i] * coeff, axis=1)
hess_arr[-1,:-1] = (self.exog[:, :].T * mu * a1 *
((1 + a4) * (1 - a3 / a2) / a2 -
p * (lprob + dgpart + 2) / mu +
p / mu * (a3 + p * a1) / a2 -
a4 * pgpart) / alpha).sum(axis=1)
da2 = (a1 * (2 * lprob +
2 * dgpart + 3 -
2 * a3 / a2
+ a1 * pgpart
- 2 * prob +
prob * a3 / a2) / alpha**2)
hess_arr[-1, -1] = da2.sum()
tri_idx = np.triu_indices(dim + 1, k=1)
hess_arr[tri_idx] = hess_arr.T[tri_idx]
return hess_arr
def hessian_factor(self, params):
"""
Generalized Negative Binomial (NB-P) model hessian maxtrix of the log-likelihood
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
hessian : ndarray, 2-D
The hessian matrix of the model.
"""
params = np.asarray(params)
if self._transparams:
alpha = np.exp(params[-1])
else:
alpha = params[-1]
params = params[:-1]
p = 2 - self.parameterization
y = self.endog
mu = self.predict(params)
mu_p = mu**p
a1 = mu_p / alpha
a2 = mu + a1
a3 = y + a1
a4 = p * a1 / mu
a5 = a4 * p / mu
dgpart = digamma(a3) - digamma(a1)
coeff = mu**2 * (((1 + a4)**2 * a3 / a2**2 -
a3 * (a5 - a4 / mu) / a2 -
y / mu**2 -
2 * a4 * (1 + a4) / a2 +
a5 * (np.log(a1) - np.log(a2) + dgpart + 2) -
a4 * (np.log(a1) - np.log(a2) + dgpart + 1) / mu -
a4**2 * (polygamma(1, a1) - polygamma(1, a3))) +
(-(1 + a4) * a3 / a2 +
y / mu +
a4 * (np.log(a1) - np.log(a2) + dgpart + 1)) / mu)
hfbb = coeff
hfba = (mu * a1 *
((1 + a4) * (1 - a3 / a2) / a2 -
p * (np.log(a1 / a2) + dgpart + 2) / mu +
p * (a3 / mu + a4) / a2 +
a4 * (polygamma(1, a1) - polygamma(1, a3))) / alpha)
hfaa = (a1 * (2 * np.log(a1 / a2) +
2 * dgpart + 3 -
2 * a3 / a2 - a1 * polygamma(1, a1) +
a1 * polygamma(1, a3) - 2 * a1 / a2 +
a1 * a3 / a2**2) / alpha**2)
return hfbb, hfba, hfaa
@Appender(_get_start_params_null_docs)
def _get_start_params_null(self):
offset = getattr(self, "offset", 0)
exposure = getattr(self, "exposure", 0)
const = (self.endog / np.exp(offset + exposure)).mean()
params = [np.log(const)]
mu = const * np.exp(offset + exposure)
resid = self.endog - mu
a = self._estimate_dispersion(mu, resid, df_resid=resid.shape[0] - 1)
params.append(a)
return np.array(params)
def _estimate_dispersion(self, mu, resid, df_resid=None):
q = self.parameterization - 1
if df_resid is None:
df_resid = resid.shape[0]
a = ((resid**2 / mu - 1) * mu**(-q)).sum() / df_resid
return a
@Appender(DiscreteModel.fit.__doc__)
def fit(self, start_params=None, method='bfgs', maxiter=35,
full_output=1, disp=1, callback=None, use_transparams=False,
cov_type='nonrobust', cov_kwds=None, use_t=None,
optim_kwds_prelim=None, **kwargs):
# TODO: Fix doc string
"""
use_transparams : bool
This parameter enable internal transformation to impose
non-negativity. True to enable. Default is False.
use_transparams=True imposes the no underdispersion (alpha > 0)
constraint. In case use_transparams=True and method="newton" or
"ncg" transformation is ignored.
"""
if use_transparams and method not in ['newton', 'ncg']:
self._transparams = True
else:
if use_transparams:
warnings.warn('Parameter "use_transparams" is ignored',
RuntimeWarning)
self._transparams = False
if start_params is None:
offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
if np.size(offset) == 1 and offset == 0:
offset = None
kwds_prelim = {'disp': 0, 'skip_hessian': True, 'warn_convergence': False}
if optim_kwds_prelim is not None:
kwds_prelim.update(optim_kwds_prelim)
mod_poi = Poisson(self.endog, self.exog, offset=offset)
with warnings.catch_warnings():
warnings.simplefilter("always")
res_poi = mod_poi.fit(**kwds_prelim)
start_params = res_poi.params
a = self._estimate_dispersion(res_poi.predict(), res_poi.resid,
df_resid=res_poi.df_resid)
start_params = np.append(start_params, max(0.05, a))
if callback is None:
# work around perfect separation callback #3895
callback = lambda *x: x
mlefit = super().fit(start_params=start_params,
maxiter=maxiter, method=method, disp=disp,
full_output=full_output, callback=callback,
**kwargs)
if optim_kwds_prelim is not None:
mlefit.mle_settings["optim_kwds_prelim"] = optim_kwds_prelim
if use_transparams and method not in ["newton", "ncg"]:
self._transparams = False
mlefit._results.params[-1] = np.exp(mlefit._results.params[-1])
nbinfit = NegativeBinomialPResults(self, mlefit._results)
result = NegativeBinomialPResultsWrapper(nbinfit)
if cov_kwds is None:
cov_kwds = {}
result._get_robustcov_results(cov_type=cov_type,
use_self=True, use_t=use_t, **cov_kwds)
return result
@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1',
maxiter='defined_by_method', full_output=1, disp=1, callback=None,
alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
qc_tol=0.03, **kwargs):
_validate_l1_method(method)
if np.size(alpha) == 1 and alpha != 0:
k_params = self.exog.shape[1] + self.k_extra
alpha = alpha * np.ones(k_params)
alpha[-1] = 0
alpha_p = alpha[:-1] if (self.k_extra and np.size(alpha) > 1) else alpha
self._transparams = False
if start_params is None:
offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
if np.size(offset) == 1 and offset == 0:
offset = None
mod_poi = Poisson(self.endog, self.exog, offset=offset)
with warnings.catch_warnings():
warnings.simplefilter("always")
start_params = mod_poi.fit_regularized(
start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=0, callback=callback,
alpha=alpha_p, trim_mode=trim_mode,
auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol,
qc_tol=qc_tol, **kwargs).params
start_params = np.append(start_params, 0.1)
cntfit = super(CountModel, self).fit_regularized(
start_params=start_params, method=method, maxiter=maxiter,
full_output=full_output, disp=disp, callback=callback,
alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
discretefit = L1NegativeBinomialResults(self, cntfit)
return L1NegativeBinomialResultsWrapper(discretefit)
@Appender(Poisson.predict.__doc__)
def predict(self, params, exog=None, exposure=None, offset=None,
which='mean', y_values=None):
if exog is None:
exog = self.exog
if exposure is None:
exposure = getattr(self, 'exposure', 0)
elif exposure != 0:
exposure = np.log(exposure)
if offset is None:
offset = getattr(self, 'offset', 0)
fitted = np.dot(exog, params[:exog.shape[1]])
linpred = fitted + exposure + offset
if which == 'mean':
return np.exp(linpred)
elif which == 'linear':
return linpred
elif which == 'var':
mean = np.exp(linpred)
alpha = params[-1]
p = self.parameterization # no `-1` as in GPP
var_ = mean * (1 + alpha * mean**(p - 1))
return var_
elif which == 'prob':
if y_values is None:
y_values = np.atleast_2d(np.arange(0, np.max(self.endog)+1))
mu = self.predict(params, exog, exposure, offset)
size, prob = self.convert_params(params, mu)
return nbinom.pmf(y_values, size[:, None], prob[:, None])
else:
raise ValueError('keyword "which" = %s not recognized' % which)
def convert_params(self, params, mu):
alpha = params[-1]
p = 2 - self.parameterization
size = 1. / alpha * mu**p
prob = size / (size + mu)
return (size, prob)
def _deriv_score_obs_dendog(self, params):
"""derivative of score_obs w.r.t. endog
Parameters
----------
params : ndarray
parameter at which score is evaluated
Returns
-------
derivative : ndarray_2d
The derivative of the score_obs with respect to endog.
"""
from statsmodels.tools.numdiff import _approx_fprime_cs_scalar
def f(y):
if y.ndim == 2 and y.shape[1] == 1:
y = y[:, 0]
sf = self.score_factor(params, endog=y)
return np.column_stack(sf)
dsf = _approx_fprime_cs_scalar(self.endog[:, None], f)
# deriv is 2d vector
d1 = dsf[:, :1] * self.exog
d2 = dsf[:, 1:2]
return np.column_stack((d1, d2))
def _var(self, mu, params=None):
"""variance implied by the distribution
internal use, will be refactored or removed
"""
alpha = params[-1]
p = self.parameterization # no `-1` as in GPP
var_ = mu * (1 + alpha * mu**(p - 1))
return var_
def _prob_nonzero(self, mu, params):
"""Probability that count is not zero
internal use in Censored model, will be refactored or removed
"""
alpha = params[-1]
p = self.parameterization
prob_nz = 1 - (1 + alpha * mu**(p-1))**(- 1 / alpha)
return prob_nz
@Appender(Poisson.get_distribution.__doc__)
def get_distribution(self, params, exog=None, exposure=None, offset=None):
"""get frozen instance of distribution
"""
mu = self.predict(params, exog=exog, exposure=exposure, offset=offset)
size, prob = self.convert_params(params, mu)
# distr = nbinom(size[:, None], prob[:, None])
distr = nbinom(size, prob)
return distr
### Results Class ###
class DiscreteResults(base.LikelihoodModelResults):
__doc__ = _discrete_results_docs % {"one_line_description" :
"A results class for the discrete dependent variable models.",
"extra_attr" : ""}
def __init__(self, model, mlefit, cov_type='nonrobust', cov_kwds=None,
use_t=None):
#super(DiscreteResults, self).__init__(model, params,
# np.linalg.inv(-hessian), scale=1.)
self.model = model
self.method = "MLE"
self.df_model = model.df_model
self.df_resid = model.df_resid
self._cache = {}
self.nobs = model.exog.shape[0]
self.__dict__.update(mlefit.__dict__)
self.converged = mlefit.mle_retvals["converged"]
if not hasattr(self, 'cov_type'):
# do this only if super, i.e. mlefit did not already add cov_type
# robust covariance
if use_t is not None:
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 = {}
from statsmodels.base.covtype import get_robustcov_results
get_robustcov_results(self, cov_type=cov_type, use_self=True,
**cov_kwds)
def __getstate__(self):
# remove unpicklable methods
mle_settings = getattr(self, 'mle_settings', None)
if mle_settings is not None:
if 'callback' in mle_settings:
mle_settings['callback'] = None
if 'cov_params_func' in mle_settings:
mle_settings['cov_params_func'] = None
return self.__dict__
@cache_readonly
def prsquared(self):
"""
McFadden's pseudo-R-squared. `1 - (llf / llnull)`
"""
return 1 - self.llf/self.llnull
@cache_readonly
def llr(self):
"""
Likelihood ratio chi-squared statistic; `-2*(llnull - llf)`
"""
return -2*(self.llnull - self.llf)
@cache_readonly
def llr_pvalue(self):
"""
The chi-squared probability of getting a log-likelihood ratio
statistic greater than llr. llr has a chi-squared distribution
with degrees of freedom `df_model`.
"""
return stats.distributions.chi2.sf(self.llr, self.df_model)
def set_null_options(self, llnull=None, attach_results=True, **kwargs):
"""
Set the fit options for the Null (constant-only) model.
This resets the cache for related attributes which is potentially
fragile. This only sets the option, the null model is estimated
when llnull is accessed, if llnull is not yet in cache.
Parameters
----------
llnull : {None, float}
If llnull is not None, then the value will be directly assigned to
the cached attribute "llnull".
attach_results : bool
Sets an internal flag whether the results instance of the null
model should be attached. By default without calling this method,
thenull model results are not attached and only the loglikelihood
value llnull is stored.
**kwargs
Additional keyword arguments used as fit keyword arguments for the
null model. The override and model default values.
Notes
-----
Modifies attributes of this instance, and so has no return.
"""
# reset cache, note we need to add here anything that depends on
# llnullor the null model. If something is missing, then the attribute
# might be incorrect.
self._cache.pop('llnull', None)
self._cache.pop('llr', None)
self._cache.pop('llr_pvalue', None)
self._cache.pop('prsquared', None)
if hasattr(self, 'res_null'):
del self.res_null
if llnull is not None:
self._cache['llnull'] = llnull
self._attach_nullmodel = attach_results
self._optim_kwds_null = kwargs
@cache_readonly
def llnull(self):
"""
Value of the constant-only loglikelihood
"""
model = self.model
kwds = model._get_init_kwds().copy()
for key in getattr(model, '_null_drop_keys', []):
del kwds[key]
# TODO: what parameters to pass to fit?
mod_null = model.__class__(model.endog, np.ones(self.nobs), **kwds)
# TODO: consider catching and warning on convergence failure?
# in the meantime, try hard to converge. see
# TestPoissonConstrained1a.test_smoke
optim_kwds = getattr(self, '_optim_kwds_null', {}).copy()
if 'start_params' in optim_kwds:
# user provided
sp_null = optim_kwds.pop('start_params')
elif hasattr(model, '_get_start_params_null'):
# get moment estimates if available
sp_null = model._get_start_params_null()
else:
sp_null = None
opt_kwds = dict(method='bfgs', warn_convergence=False, maxiter=10000,
disp=0)
opt_kwds.update(optim_kwds)
if optim_kwds:
res_null = mod_null.fit(start_params=sp_null, **opt_kwds)
else:
# this should be a reasonably method case across versions
res_null = mod_null.fit(start_params=sp_null, method='nm',
warn_convergence=False,
maxiter=10000, disp=0)
res_null = mod_null.fit(start_params=res_null.params, method='bfgs',
warn_convergence=False,
maxiter=10000, disp=0)
if getattr(self, '_attach_nullmodel', False) is not False:
self.res_null = res_null
return res_null.llf
@cache_readonly
def fittedvalues(self):
"""
Linear predictor XB.
"""
return np.dot(self.model.exog, self.params[:self.model.exog.shape[1]])
@cache_readonly
def resid_response(self):
"""
Respnose residuals. The response residuals are defined as
`endog - fittedvalues`
"""
return self.model.endog - self.predict()
@cache_readonly
def resid_pearson(self):
"""
Pearson residuals defined as response residuals divided by standard
deviation implied by the model.
"""
var_ = self.predict(which="var")
return self.resid_response / np.sqrt(var_)
@cache_readonly
def aic(self):
"""
Akaike information criterion. `-2*(llf - p)` where `p` is the number
of regressors including the intercept.
"""
k_extra = getattr(self.model, 'k_extra', 0)
return -2*(self.llf - (self.df_model + 1 + k_extra))
@cache_readonly
def bic(self):
"""
Bayesian information criterion. `-2*llf + ln(nobs)*p` where `p` is the
number of regressors including the intercept.
"""
k_extra = getattr(self.model, 'k_extra', 0)
return -2*self.llf + np.log(self.nobs)*(self.df_model + 1 + k_extra)
@cache_readonly
def im_ratio(self):
return pinfer.im_ratio(self)
def info_criteria(self, crit, dk_params=0):
"""Return an information criterion for the model.
Parameters
----------
crit : string
One of 'aic', 'bic', 'tic' or 'gbic'.
dk_params : int or float
Correction to the number of parameters used in the information
criterion.
Returns
-------
Value of information criterion.
Notes
-----
Tic and gbic
References
----------
Burnham KP, Anderson KR (2002). Model Selection and Multimodel
Inference; Springer New York.
"""
crit = crit.lower()
k_extra = getattr(self.model, 'k_extra', 0)
k_params = self.df_model + 1 + k_extra + dk_params
if crit == "aic":
return -2 * self.llf + 2 * k_params
elif crit == "bic":
nobs = self.df_model + self.df_resid + 1
bic = -2*self.llf + k_params*np.log(nobs)
return bic
elif crit == "tic":
return pinfer.tic(self)
elif crit == "gbic":
return pinfer.gbic(self)
else:
raise ValueError("Name of information criterion not recognized.")
def score_test(self, exog_extra=None, params_constrained=None,
hypothesis='joint', cov_type=None, cov_kwds=None,
k_constraints=None, observed=True):
res = pinfer.score_test(self, exog_extra=exog_extra,
params_constrained=params_constrained,
hypothesis=hypothesis,
cov_type=cov_type, cov_kwds=cov_kwds,
k_constraints=k_constraints,
observed=observed)
return res
score_test.__doc__ = pinfer.score_test.__doc__
def get_prediction(self, exog=None,
transform=True, which="mean", linear=None,
row_labels=None, average=False,
agg_weights=None, y_values=None,
**kwargs):
"""
Compute prediction results when endpoint transformation is valid.
Parameters
----------
exog : array_like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a formula, do you want to pass
exog through the formula. Default is True. E.g., if you fit
a model y ~ log(x1) + log(x2), and transform is True, then
you can pass a data structure that contains x1 and x2 in
their original form. Otherwise, you'd need to log the data
first.
which : str
Which statistic is to be predicted. Default is "mean".
The available statistics and options depend on the model.
see the model.predict docstring
linear : bool
Linear has been replaced by the `which` keyword and will be
deprecated.
If linear is True, then `which` is ignored and the linear
prediction is returned.
row_labels : list of str or None
If row_lables are provided, then they will replace the generated
labels.
average : bool
If average is True, then the mean prediction is computed, that is,
predictions are computed for individual exog and then the average
over observation is used.
If average is False, then the results are the predictions for all
observations, i.e. same length as ``exog``.
agg_weights : ndarray, optional
Aggregation weights, only used if average is True.
The weights are not normalized.
y_values : None or nd_array
Some predictive statistics like which="prob" are computed at
values of the response variable. If y_values is not None, then
it will be used instead of the default set of y_values.
**Warning:** ``which="prob"`` for count models currently computes
the pmf for all y=k up to max(endog). This can be a large array if
the observed endog values are large.
This will likely change so that the set of y_values will be chosen
to limit the array size.
**kwargs :
Some models can take additional keyword arguments, such as offset,
exposure or additional exog in multi-part models like zero inflated
models.
See the predict method of the model for the details.
Returns
-------
prediction_results : PredictionResults
The prediction results instance contains prediction and prediction
variance and can on demand calculate confidence intervals and
summary dataframe for the prediction.
Notes
-----
Status: new in 0.14, experimental
"""
if linear is True:
# compatibility with old keyword
which = "linear"
pred_kwds = kwargs
# y_values is explicit so we can add it to the docstring
if y_values is not None:
pred_kwds["y_values"] = y_values
res = pred.get_prediction(
self,
exog=exog,
which=which,
transform=transform,
row_labels=row_labels,
average=average,
agg_weights=agg_weights,
pred_kwds=pred_kwds
)
return res
def get_distribution(self, exog=None, transform=True, **kwargs):
exog, _ = self._transform_predict_exog(exog, transform=transform)
if exog is not None:
exog = np.asarray(exog)
distr = self.model.get_distribution(self.params,
exog=exog,
**kwargs
)
return distr
def _get_endog_name(self, yname, yname_list):
if yname is None:
yname = self.model.endog_names
if yname_list is None:
yname_list = self.model.endog_names
return yname, yname_list
def get_margeff(self, at='overall', method='dydx', atexog=None,
dummy=False, count=False):
"""Get marginal effects of the fitted model.
Parameters
----------
at : str, optional
Options are:
- 'overall', The average of the marginal effects at each
observation.
- 'mean', The marginal effects at the mean of each regressor.
- 'median', The marginal effects at the median of each regressor.
- 'zero', The marginal effects at zero for each regressor.
- 'all', The marginal effects at each observation. If `at` is all
only margeff will be available from the returned object.
Note that if `exog` is specified, then marginal effects for all
variables not specified by `exog` are calculated using the `at`
option.
method : str, optional
Options are:
- 'dydx' - dy/dx - No transformation is made and marginal effects
are returned. This is the default.
- 'eyex' - estimate elasticities of variables in `exog` --
d(lny)/d(lnx)
- 'dyex' - estimate semi-elasticity -- dy/d(lnx)
- 'eydx' - estimate semi-elasticity -- d(lny)/dx
Note that tranformations are done after each observation is
calculated. Semi-elasticities for binary variables are computed
using the midpoint method. 'dyex' and 'eyex' do not make sense
for discrete variables. For interpretations of these methods
see notes below.
atexog : array_like, optional
Optionally, you can provide the exogenous variables over which to
get the marginal effects. This should be a dictionary with the key
as the zero-indexed column number and the value of the dictionary.
Default is None for all independent variables less the constant.
dummy : bool, optional
If False, treats binary variables (if present) as continuous. This
is the default. Else if True, treats binary variables as
changing from 0 to 1. Note that any variable that is either 0 or 1
is treated as binary. Each binary variable is treated separately
for now.
count : bool, optional
If False, treats count variables (if present) as continuous. This
is the default. Else if True, the marginal effect is the
change in probabilities when each observation is increased by one.
Returns
-------
DiscreteMargins : marginal effects instance
Returns an object that holds the marginal effects, standard
errors, confidence intervals, etc. See
`statsmodels.discrete.discrete_margins.DiscreteMargins` for more
information.
Notes
-----
Interpretations of methods:
- 'dydx' - change in `endog` for a change in `exog`.
- 'eyex' - proportional change in `endog` for a proportional change
in `exog`.
- 'dyex' - change in `endog` for a proportional change in `exog`.
- 'eydx' - proportional change in `endog` for a change in `exog`.
When using after Poisson, returns the expected number of events per
period, assuming that the model is loglinear.
"""
if getattr(self.model, "offset", None) is not None:
raise NotImplementedError("Margins with offset are not available.")
from statsmodels.discrete.discrete_margins import DiscreteMargins
return DiscreteMargins(self, (at, method, atexog, dummy, count))
def get_influence(self):
"""
Get an instance of MLEInfluence with influence and outlier measures
Returns
-------
infl : MLEInfluence instance
The instance has methods to calculate the main influence and
outlier measures as attributes.
See Also
--------
statsmodels.stats.outliers_influence.MLEInfluence
"""
from statsmodels.stats.outliers_influence import MLEInfluence
return MLEInfluence(self)
def summary(self, yname=None, xname=None, title=None, alpha=.05,
yname_list=None):
"""
Summarize the Regression Results.
Parameters
----------
yname : str, optional
The name of the endog variable in the tables. The default is `y`.
xname : list[str], optional
The names for the exogenous variables, default is "var_xx".
Must match the number of parameters in the model.
title : str, optional
Title for the top table. If not None, then this replaces the
default title.
alpha : float
The significance level for the confidence intervals.
Returns
-------
Summary
Class that holds the summary tables and text, which can be printed
or converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary : Class that hold summary results.
"""
top_left = [('Dep. Variable:', None),
('Model:', [self.model.__class__.__name__]),
('Method:', [self.method]),
('Date:', None),
('Time:', None),
('converged:', ["%s" % self.mle_retvals['converged']]),
]
top_right = [('No. Observations:', None),
('Df Residuals:', None),
('Df Model:', None),
('Pseudo R-squ.:', ["%#6.4g" % self.prsquared]),
('Log-Likelihood:', None),
('LL-Null:', ["%#8.5g" % self.llnull]),
('LLR p-value:', ["%#6.4g" % self.llr_pvalue])
]
if hasattr(self, 'cov_type'):
top_left.append(('Covariance Type:', [self.cov_type]))
if title is None:
title = self.model.__class__.__name__ + ' ' + "Regression Results"
# boiler plate
from statsmodels.iolib.summary import Summary
smry = Summary()
yname, yname_list = self._get_endog_name(yname, yname_list)
# for top of table
smry.add_table_2cols(self, gleft=top_left, gright=top_right,
yname=yname, xname=xname, title=title)
# for parameters, etc
smry.add_table_params(self, yname=yname_list, 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.'])
return smry
def summary2(self, yname=None, xname=None, title=None, alpha=.05,
float_format="%.4f"):
"""
Experimental function to summarize regression results.
Parameters
----------
yname : str
Name of the dependent variable (optional).
xname : list[str], optional
List of strings of length equal to the number of parameters
Names of the independent variables (optional).
title : str, optional
Title for the top table. If not None, then this replaces the
default title.
alpha : float
The significance level for the confidence intervals.
float_format : str
The print format for floats in parameters summary.
Returns
-------
Summary
Instance that contains the summary tables and text, which can be
printed or converted to various output formats.
See Also
--------
statsmodels.iolib.summary2.Summary : Class that holds summary results.
"""
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 CountResults(DiscreteResults):
__doc__ = _discrete_results_docs % {
"one_line_description": "A results class for count data",
"extra_attr": ""}
@cache_readonly
def resid(self):
"""
Residuals
Notes
-----
The residuals for Count models are defined as
.. math:: y - p
where :math:`p = \\exp(X\\beta)`. Any exposure and offset variables
are also handled.
"""
return self.model.endog - self.predict()
def get_diagnostic(self, y_max=None):
"""
Get instance of class with specification and diagnostic methods.
experimental, API of Diagnostic classes will change
Returns
-------
CountDiagnostic instance
The instance has methods to perform specification and diagnostic
tesst and plots
See Also
--------
statsmodels.statsmodels.discrete.diagnostic.CountDiagnostic
"""
from statsmodels.discrete.diagnostic import CountDiagnostic
return CountDiagnostic(self, y_max=y_max)
class NegativeBinomialResults(CountResults):
__doc__ = _discrete_results_docs % {
"one_line_description": "A results class for NegativeBinomial 1 and 2",
"extra_attr": ""}
@cache_readonly
def lnalpha(self):
"""Natural log of alpha"""
return np.log(self.params[-1])
@cache_readonly
def lnalpha_std_err(self):
"""Natural log of standardized error"""
return self.bse[-1] / self.params[-1]
@cache_readonly
def aic(self):
# + 1 because we estimate alpha
k_extra = getattr(self.model, 'k_extra', 0)
return -2*(self.llf - (self.df_model + self.k_constant + k_extra))
@cache_readonly
def bic(self):
# + 1 because we estimate alpha
k_extra = getattr(self.model, 'k_extra', 0)
return -2*self.llf + np.log(self.nobs)*(self.df_model +
self.k_constant + k_extra)
class NegativeBinomialPResults(NegativeBinomialResults):
__doc__ = _discrete_results_docs % {
"one_line_description": "A results class for NegativeBinomialP",
"extra_attr": ""}
class GeneralizedPoissonResults(NegativeBinomialResults):
__doc__ = _discrete_results_docs % {
"one_line_description": "A results class for Generalized Poisson",
"extra_attr": ""}
@cache_readonly
def _dispersion_factor(self):
p = getattr(self.model, 'parameterization', 0)
mu = self.predict()
return (1 + self.params[-1] * mu**p)**2
class L1CountResults(DiscreteResults):
__doc__ = _discrete_results_docs % {"one_line_description" :
"A results class for count data fit by l1 regularization",
"extra_attr" : _l1_results_attr}
def __init__(self, model, cntfit):
super().__init__(model, cntfit)
# self.trimmed is a boolean array with T/F telling whether or not that
# entry in params has been set zero'd out.
self.trimmed = cntfit.mle_retvals['trimmed']
self.nnz_params = (~self.trimmed).sum()
# Set degrees of freedom. In doing so,
# adjust for extra parameter in NegativeBinomial nb1 and nb2
# extra parameter is not included in df_model
k_extra = getattr(self.model, 'k_extra', 0)
self.df_model = self.nnz_params - 1 - k_extra
self.df_resid = float(self.model.endog.shape[0] - self.nnz_params) + k_extra
class PoissonResults(CountResults):
def predict_prob(self, n=None, exog=None, exposure=None, offset=None,
transform=True):
"""
Return predicted probability of each count level for each observation
Parameters
----------
n : array_like or int
The counts for which you want the probabilities. If n is None
then the probabilities for each count from 0 to max(y) are
given.
Returns
-------
ndarray
A nobs x n array where len(`n`) columns are indexed by the count
n. If n is None, then column 0 is the probability that each
observation is 0, column 1 is the probability that each
observation is 1, etc.
"""
if n is not None:
counts = np.atleast_2d(n)
else:
counts = np.atleast_2d(np.arange(0, np.max(self.model.endog)+1))
mu = self.predict(exog=exog, exposure=exposure, offset=offset,
transform=transform, which="mean")[:,None]
# uses broadcasting
return stats.poisson.pmf(counts, mu)
@property
def resid_pearson(self):
"""
Pearson residuals
Notes
-----
Pearson residuals are defined to be
.. math:: r_j = \\frac{(y - M_jp_j)}{\\sqrt{M_jp_j(1-p_j)}}
where :math:`p_j=cdf(X\\beta)` and :math:`M_j` is the total number of
observations sharing the covariate pattern :math:`j`.
For now :math:`M_j` is always set to 1.
"""
# Pearson residuals
p = self.predict() # fittedvalues is still linear
return (self.model.endog - p)/np.sqrt(p)
def get_influence(self):
"""
Get an instance of MLEInfluence with influence and outlier measures
Returns
-------
infl : MLEInfluence instance
The instance has methods to calculate the main influence and
outlier measures as attributes.
See Also
--------
statsmodels.stats.outliers_influence.MLEInfluence
"""
from statsmodels.stats.outliers_influence import MLEInfluence
return MLEInfluence(self)
def get_diagnostic(self, y_max=None):
"""
Get instance of class with specification and diagnostic methods
experimental, API of Diagnostic classes will change
Returns
-------
PoissonDiagnostic instance
The instance has methods to perform specification and diagnostic
tesst and plots
See Also
--------
statsmodels.statsmodels.discrete.diagnostic.PoissonDiagnostic
"""
from statsmodels.discrete.diagnostic import (
PoissonDiagnostic)
return PoissonDiagnostic(self, y_max=y_max)
class L1PoissonResults(L1CountResults, PoissonResults):
pass
class L1NegativeBinomialResults(L1CountResults, NegativeBinomialResults):
pass
class L1GeneralizedPoissonResults(L1CountResults, GeneralizedPoissonResults):
pass
class OrderedResults(DiscreteResults):
__doc__ = _discrete_results_docs % {"one_line_description" : "A results class for ordered discrete data." , "extra_attr" : ""}
pass
class BinaryResults(DiscreteResults):
__doc__ = _discrete_results_docs % {"one_line_description" : "A results class for binary data", "extra_attr" : ""}
def pred_table(self, threshold=.5):
"""
Prediction table
Parameters
----------
threshold : scalar
Number between 0 and 1. Threshold above which a prediction is
considered 1 and below which a prediction is considered 0.
Notes
-----
pred_table[i,j] refers to the number of times "i" was observed and
the model predicted "j". Correct predictions are along the diagonal.
"""
model = self.model
actual = model.endog
pred = np.array(self.predict() > threshold, dtype=float)
bins = np.array([0, 0.5, 1])
return np.histogram2d(actual, pred, bins=bins)[0]
@Appender(DiscreteResults.summary.__doc__)
def summary(self, yname=None, xname=None, title=None, alpha=.05,
yname_list=None):
smry = super().summary(yname, xname, title, alpha,
yname_list)
fittedvalues = self.model.cdf(self.fittedvalues)
absprederror = np.abs(self.model.endog - fittedvalues)
predclose_sum = (absprederror < 1e-4).sum()
predclose_frac = predclose_sum / len(fittedvalues)
# add warnings/notes
etext = []
if predclose_sum == len(fittedvalues): # TODO: nobs?
wstr = "Complete Separation: The results show that there is"
wstr += "complete separation or perfect prediction.\n"
wstr += "In this case the Maximum Likelihood Estimator does "
wstr += "not exist and the parameters\n"
wstr += "are not identified."
etext.append(wstr)
elif predclose_frac > 0.1: # TODO: get better diagnosis
wstr = "Possibly complete quasi-separation: A fraction "
wstr += "%4.2f of observations can be\n" % predclose_frac
wstr += "perfectly predicted. This might indicate that there "
wstr += "is complete\nquasi-separation. In this case some "
wstr += "parameters will not be identified."
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
@cache_readonly
def resid_dev(self):
"""
Deviance residuals
Notes
-----
Deviance residuals are defined
.. math:: d_j = \\pm\\left(2\\left[Y_j\\ln\\left(\\frac{Y_j}{M_jp_j}\\right) + (M_j - Y_j\\ln\\left(\\frac{M_j-Y_j}{M_j(1-p_j)} \\right) \\right] \\right)^{1/2}
where
:math:`p_j = cdf(X\\beta)` and :math:`M_j` is the total number of
observations sharing the covariate pattern :math:`j`.
For now :math:`M_j` is always set to 1.
"""
#These are the deviance residuals
#model = self.model
endog = self.model.endog
#exog = model.exog
# M = # of individuals that share a covariate pattern
# so M[i] = 2 for i = two share a covariate pattern
M = 1
p = self.predict()
#Y_0 = np.where(exog == 0)
#Y_M = np.where(exog == M)
#NOTE: Common covariate patterns are not yet handled
res = -(1-endog)*np.sqrt(2*M*np.abs(np.log(1-p))) + \
endog*np.sqrt(2*M*np.abs(np.log(p)))
return res
@cache_readonly
def resid_pearson(self):
"""
Pearson residuals
Notes
-----
Pearson residuals are defined to be
.. math:: r_j = \\frac{(y - M_jp_j)}{\\sqrt{M_jp_j(1-p_j)}}
where :math:`p_j=cdf(X\\beta)` and :math:`M_j` is the total number of
observations sharing the covariate pattern :math:`j`.
For now :math:`M_j` is always set to 1.
"""
# Pearson residuals
#model = self.model
endog = self.model.endog
#exog = model.exog
# M = # of individuals that share a covariate pattern
# so M[i] = 2 for i = two share a covariate pattern
# use unique row pattern?
M = 1
p = self.predict()
return (endog - M*p)/np.sqrt(M*p*(1-p))
@cache_readonly
def resid_response(self):
"""
The response residuals
Notes
-----
Response residuals are defined to be
.. math:: y - p
where :math:`p=cdf(X\\beta)`.
"""
return self.model.endog - self.predict()
class LogitResults(BinaryResults):
__doc__ = _discrete_results_docs % {
"one_line_description": "A results class for Logit Model",
"extra_attr": ""}
@cache_readonly
def resid_generalized(self):
"""
Generalized residuals
Notes
-----
The generalized residuals for the Logit model are defined
.. math:: y - p
where :math:`p=cdf(X\\beta)`. This is the same as the `resid_response`
for the Logit model.
"""
# Generalized residuals
return self.model.endog - self.predict()
def get_influence(self):
"""
Get an instance of MLEInfluence with influence and outlier measures
Returns
-------
infl : MLEInfluence instance
The instance has methods to calculate the main influence and
outlier measures as attributes.
See Also
--------
statsmodels.stats.outliers_influence.MLEInfluence
"""
from statsmodels.stats.outliers_influence import MLEInfluence
return MLEInfluence(self)
class ProbitResults(BinaryResults):
__doc__ = _discrete_results_docs % {
"one_line_description": "A results class for Probit Model",
"extra_attr": ""}
@cache_readonly
def resid_generalized(self):
"""
Generalized residuals
Notes
-----
The generalized residuals for the Probit model are defined
.. math:: y\\frac{\\phi(X\\beta)}{\\Phi(X\\beta)}-(1-y)\\frac{\\phi(X\\beta)}{1-\\Phi(X\\beta)}
"""
# generalized residuals
model = self.model
endog = model.endog
XB = self.predict(which="linear")
pdf = model.pdf(XB)
cdf = model.cdf(XB)
return endog * pdf/cdf - (1-endog)*pdf/(1-cdf)
class L1BinaryResults(BinaryResults):
__doc__ = _discrete_results_docs % {"one_line_description" :
"Results instance for binary data fit by l1 regularization",
"extra_attr" : _l1_results_attr}
def __init__(self, model, bnryfit):
super().__init__(model, bnryfit)
# self.trimmed is a boolean array with T/F telling whether or not that
# entry in params has been set zero'd out.
self.trimmed = bnryfit.mle_retvals['trimmed']
self.nnz_params = (~self.trimmed).sum()
self.df_model = self.nnz_params - 1
self.df_resid = float(self.model.endog.shape[0] - self.nnz_params)
class MultinomialResults(DiscreteResults):
__doc__ = _discrete_results_docs % {"one_line_description" :
"A results class for multinomial data", "extra_attr" : ""}
def __init__(self, model, mlefit):
super().__init__(model, mlefit)
self.J = model.J
self.K = model.K
@staticmethod
def _maybe_convert_ynames_int(ynames):
# see if they're integers
issue_warning = False
msg = ('endog contains values are that not int-like. Uses string '
'representation of value. Use integer-valued endog to '
'suppress this warning.')
for i in ynames:
try:
if ynames[i] % 1 == 0:
ynames[i] = str(int(ynames[i]))
else:
issue_warning = True
ynames[i] = str(ynames[i])
except TypeError:
ynames[i] = str(ynames[i])
if issue_warning:
warnings.warn(msg, SpecificationWarning)
return ynames
def _get_endog_name(self, yname, yname_list, all=False):
"""
If all is False, the first variable name is dropped
"""
model = self.model
if yname is None:
yname = model.endog_names
if yname_list is None:
ynames = model._ynames_map
ynames = self._maybe_convert_ynames_int(ynames)
# use range below to ensure sortedness
ynames = [ynames[key] for key in range(int(model.J))]
ynames = ['='.join([yname, name]) for name in ynames]
if not all:
yname_list = ynames[1:] # assumes first variable is dropped
else:
yname_list = ynames
return yname, yname_list
def pred_table(self):
"""
Returns the J x J prediction table.
Notes
-----
pred_table[i,j] refers to the number of times "i" was observed and
the model predicted "j". Correct predictions are along the diagonal.
"""
ju = self.model.J - 1 # highest index
# these are the actual, predicted indices
#idx = lzip(self.model.endog, self.predict().argmax(1))
bins = np.concatenate(([0], np.linspace(0.5, ju - 0.5, ju), [ju]))
return np.histogram2d(self.model.endog, self.predict().argmax(1),
bins=bins)[0]
@cache_readonly
def bse(self):
bse = np.sqrt(np.diag(self.cov_params()))
return bse.reshape(self.params.shape, order='F')
@cache_readonly
def aic(self):
return -2*(self.llf - (self.df_model+self.model.J-1))
@cache_readonly
def bic(self):
return -2*self.llf + np.log(self.nobs)*(self.df_model+self.model.J-1)
def conf_int(self, alpha=.05, cols=None):
confint = super(DiscreteResults, self).conf_int(alpha=alpha,
cols=cols)
return confint.transpose(2,0,1)
def get_prediction(self):
"""Not implemented for Multinomial
"""
raise NotImplementedError
def margeff(self):
raise NotImplementedError("Use get_margeff instead")
@cache_readonly
def resid_misclassified(self):
"""
Residuals indicating which observations are misclassified.
Notes
-----
The residuals for the multinomial model are defined as
.. math:: argmax(y_i) \\neq argmax(p_i)
where :math:`argmax(y_i)` is the index of the category for the
endogenous variable and :math:`argmax(p_i)` is the index of the
predicted probabilities for each category. That is, the residual
is a binary indicator that is 0 if the category with the highest
predicted probability is the same as that of the observed variable
and 1 otherwise.
"""
# it's 0 or 1 - 0 for correct prediction and 1 for a missed one
return (self.model.wendog.argmax(1) !=
self.predict().argmax(1)).astype(float)
def summary2(self, alpha=0.05, float_format="%.4f"):
"""Experimental function to summarize regression results
Parameters
----------
alpha : float
significance level for the confidence intervals
float_format : str
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
"""
from statsmodels.iolib import summary2
smry = summary2.Summary()
smry.add_dict(summary2.summary_model(self))
# One data frame per value of endog
eqn = self.params.shape[1]
confint = self.conf_int(alpha)
for i in range(eqn):
coefs = summary2.summary_params((self, self.params[:, i],
self.bse[:, i],
self.tvalues[:, i],
self.pvalues[:, i],
confint[i]),
alpha=alpha)
# Header must show value of endog
level_str = self.model.endog_names + ' = ' + str(i)
coefs[level_str] = coefs.index
coefs = coefs.iloc[:, [-1, 0, 1, 2, 3, 4, 5]]
smry.add_df(coefs, index=False, header=True,
float_format=float_format)
smry.add_title(results=self)
return smry
class L1MultinomialResults(MultinomialResults):
__doc__ = _discrete_results_docs % {"one_line_description" :
"A results class for multinomial data fit by l1 regularization",
"extra_attr" : _l1_results_attr}
def __init__(self, model, mlefit):
super().__init__(model, mlefit)
# self.trimmed is a boolean array with T/F telling whether or not that
# entry in params has been set zero'd out.
self.trimmed = mlefit.mle_retvals['trimmed']
self.nnz_params = (~self.trimmed).sum()
# Note: J-1 constants
self.df_model = self.nnz_params - (self.model.J - 1)
self.df_resid = float(self.model.endog.shape[0] - self.nnz_params)
#### Results Wrappers ####
class OrderedResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(OrderedResultsWrapper, OrderedResults)
class CountResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(CountResultsWrapper, CountResults)
class NegativeBinomialResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(NegativeBinomialResultsWrapper,
NegativeBinomialResults)
class NegativeBinomialPResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(NegativeBinomialPResultsWrapper,
NegativeBinomialPResults)
class GeneralizedPoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(GeneralizedPoissonResultsWrapper,
GeneralizedPoissonResults)
class PoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(PoissonResultsWrapper, PoissonResults)
class L1CountResultsWrapper(lm.RegressionResultsWrapper):
pass
class L1PoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(L1PoissonResultsWrapper, L1PoissonResults)
class L1NegativeBinomialResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(L1NegativeBinomialResultsWrapper,
L1NegativeBinomialResults)
class L1GeneralizedPoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(L1GeneralizedPoissonResultsWrapper,
L1GeneralizedPoissonResults)
class BinaryResultsWrapper(lm.RegressionResultsWrapper):
_attrs = {"resid_dev": "rows",
"resid_generalized": "rows",
"resid_pearson": "rows",
"resid_response": "rows"
}
_wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs,
_attrs)
wrap.populate_wrapper(BinaryResultsWrapper, BinaryResults)
class L1BinaryResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(L1BinaryResultsWrapper, L1BinaryResults)
class MultinomialResultsWrapper(lm.RegressionResultsWrapper):
_attrs = {"resid_misclassified": "rows"}
_wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs,
_attrs)
_methods = {'conf_int': 'multivariate_confint'}
_wrap_methods = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(MultinomialResultsWrapper, MultinomialResults)
class L1MultinomialResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(L1MultinomialResultsWrapper, L1MultinomialResults)
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