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discrete_model.py
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discrete_model.py
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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"]
import numpy as np
from scipy.special import gammaln
from scipy import stats, special, optimize # opt just for nbin
import statsmodels.tools.tools as tools
from statsmodels.tools.decorators import (resettable_cache,
cache_readonly)
from statsmodels.regression.linear_model import OLS
from scipy import stats, special, optimize # opt just for nbin
from statsmodels.tools.sm_exceptions import PerfectSeparationError
import statsmodels.base.model as base
import statsmodels.regression.linear_model as lm
import statsmodels.base.wrapper as wrap
#TODO: add options for the parameter covariance/variance
# ie., OIM, EIM, and BHHH see Green 21.4
#### margeff helper functions ####
#NOTE: todo marginal effects for group 2
# group 2 oprobit, ologit, gologit, mlogit, biprobit
def _check_margeff_args(at, method):
"""
Checks valid options for margeff
"""
if at not in ['overall','mean','median','zero','all']:
raise ValueError("%s not a valid option for `at`." % at)
if method not in ['dydx','eyex','dyex','eydx']:
raise ValueError("method is not understood. Got %s" % method)
def _check_discrete_args(at, method):
"""
Checks the arguments for margeff if the exogenous variables are discrete.
"""
if method in ['dyex','eyex']:
raise ValueError("%s not allowed for discrete variables" % method)
if at in ['median', 'zero']:
raise ValueError("%s not allowed for discrete variables" % at)
def _isdummy(X):
"""
Given an array X, returns a boolean column index for the dummy variables.
Parameters
----------
X : array-like
A 1d or 2d array of numbers
Examples
--------
>>> X = np.random.randint(0, 2, size=(15,5)).astype(float)
>>> X[:,1:3] = np.random.randn(15,2)
>>> ind = _isdummy(X)
>>> ind
array([ True, False, False, True, True], dtype=bool)
"""
X = np.asarray(X)
if X.ndim > 1:
ind = np.zeros(X.shape[1]).astype(bool)
max = (np.max(X, axis=0) == 1)
min = (np.min(X, axis=0) == 0)
remainder = np.all(X % 1. == 0, axis=0)
ind = min & max & remainder
if X.ndim == 1:
ind = np.asarray([ind])
return ind
def _iscount(X):
"""
Given an array X, returns a boolean column index for count variables.
Parameters
----------
X : array-like
A 1d or 2d array of numbers
Examples
--------
>>> X = np.random.randint(0, 10, size=(15,5)).astype(float)
>>> X[:,1:3] = np.random.randn(15,2)
>>> ind = _iscount(X)
>>> ind
array([ True, False, False, True, True], dtype=bool)
"""
X = np.asarray(X)
remainder = np.logical_and(np.all(X % 1. == 0, axis = 0),
X.var(0) != 0)
dummy = _isdummy(X)
remainder -= dummy
return remainder
def _get_margeff_exog(exog, at, atexog, ind):
if atexog is not None: # user supplied
if not isinstance(atexog, dict):
raise ValueError("atexog should be a dict not %s"\
% type(atexog))
for key in atexog:
exog[:,key] = atexog[key]
if at == 'mean':
exog = np.atleast_2d(exog.mean(0))
elif at == 'median':
exog = np.atleast_2d(np.median(exog, axis=0))
elif at == 'zero':
exog = np.zeros((1,exog.shape[1]))
exog[0,~ind] = 1
return exog
def _get_count_effects(effects, exog, count_ind, method, model, params):
for i, tf in enumerate(count_ind):
if tf == True:
exog0 = exog.copy()
effect0 = model.predict(params, exog0)
wf1 = model.predict
exog0[:,i] += 1
effect1 = model.predict(params, exog0)
#TODO: compute discrete elasticity correctly
#Stata doesn't use the midpoint method or a weighted average.
#Check elsewhere
if 'ey' in method:
pass
##TODO: don't know if this is theoretically correct
#fittedvalues0 = np.dot(exog0,params)
#fittedvalues1 = np.dot(exog1,params)
#weight1 = model.exog[:,i].mean()
#weight0 = 1 - weight1
#wfv = (.5*model.cdf(fittedvalues1) + \
# .5*model.cdf(fittedvalues0))
#effects[i] = ((effect1 - effect0)/wfv).mean()
effects[i] = (effect1 - effect0).mean()
return effects
def _get_dummy_effects(effects, exog, dummy_ind, method, model, params):
for i, tf in enumerate(dummy_ind):
if tf == True:
exog0 = exog.copy() # only copy once, can we avoid a copy?
exog0[:,i] = 0
effect0 = model.predict(params, exog0)
#fittedvalues0 = np.dot(exog0,params)
exog0[:,i] = 1
effect1 = model.predict(params, exog0)
if 'ey' in method:
effect0 = np.log(effect0)
effect1 = np.log(effect1)
effects[i] = (effect1 - effect0).mean() # mean for overall
return effects
def _effects_at(effects, at, ind):
if at == 'all':
effects = effects[:,ind]
elif at == 'overall':
effects = effects.mean(0)[ind]
else:
effects = effects[0,ind]
return effects
#### 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):
super(DiscreteModel, self).__init__(endog, exog)
self.raise_on_perfect_prediction = True
def initialize(self):
"""
Initialize is called by
statsmodels.model.LikelihoodModel.__init__
and should contain any preprocessing that needs to be done for a model.
"""
self.df_model = float(tools.rank(self.exog) - 1) # assumes constant
self.df_resid = float(self.exog.shape[0] - tools.rank(self.exog))
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):
endog = self.endog
fittedvalues = self.cdf(np.dot(self.exog, params))
if (self.raise_on_perfect_prediction and
np.allclose(fittedvalues - endog, 0)):
msg = "Perfect separation detected, results not available"
raise PerfectSeparationError(msg)
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.LikelihoodModel.fit
"""
if callback is None:
callback = self._check_perfect_pred
else:
pass # make a function factory to have multiple call-backs
mlefit = super(DiscreteModel, self).fit(start_params=start_params,
method=method, maxiter=maxiter, full_output=full_output,
disp=disp, callback=callback, **kwargs)
return mlefit # up to subclasses to wrap results
fit.__doc__ += base.LikelihoodModel.fit.__doc__
def predict(self, params, exog=None, linear=False):
"""
Predict response variable of a model given exogenous variables.
"""
raise NotImplementedError
def _derivative_exog(self, params, exog=None):
"""
This should implement the derivative of the non-linear function
"""
raise NotImplementedError
class BinaryModel(DiscreteModel):
def predict(self, params, exog=None, linear=False):
"""
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.
linear : bool, optional
If True, returns the linear predictor dot(exog,params). Else,
returns the value of the cdf at the linear predictor.
Returns
-------
array
Fitted values at exog.
"""
if exog is None:
exog = self.exog
if not linear:
return self.cdf(np.dot(exog, params))
else:
return np.dot(exog, params)
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
bnryfit = super(BinaryModel, self).fit(start_params=start_params,
method=method, maxiter=maxiter, full_output=full_output,
disp=disp, callback=callback, **kwargs)
discretefit = BinaryResults(self, bnryfit)
return BinaryResultsWrapper(discretefit)
fit.__doc__ = DiscreteModel.fit.__doc__
def _derivative_exog(self, params, exog=None):
"""
For computing marginal effects.
"""
#note, this form should be appropriate for
## group 1 probit, logit, logistic, cloglog, heckprob, xtprobit
if exog == None:
exog = self.exog
return np.dot(self.pdf(np.dot(exog, params))[:,None], params[None,:])
class MultinomialModel(BinaryModel):
def initialize(self):
"""
Preprocesses the data for MNLogit.
Turns the endogenous variable into an array of dummies and assigns
J and K.
"""
super(MultinomialModel, self).initialize()
#This is also a "whiten" method as used in other models (eg regression)
wendog, ynames = tools.categorical(self.endog, drop=True,
dictnames=True)
self._ynames_map = ynames
self.wendog = wendog # don't drop first category
self.J = float(wendog.shape[1])
self.K = float(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, linear=False):
"""
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.
linear : bool, optional
If True, returns the linear predictor dot(exog,params). Else,
returns the value of the cdf at the linear predictor.
Notes
-----
Column 0 is the base case, the rest conform to the rows of params
shifted up one for the base case.
"""
if exog is None: # do here to accomodate user-given exog
exog = self.exog
pred = super(MultinomialModel, self).predict(params, exog, linear)
if linear:
pred = np.column_stack((np.zeros(len(exog)), pred))
return pred
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)
callback = lambda x : None # placeholder until check_perfect_pred
# 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)
fit.__doc__ = DiscreteModel.fit.__doc__
class CountModel(DiscreteModel):
def __init__(self, endog, exog, offset=None, exposure=None):
super(CountModel, self).__init__(endog, exog)
self._check_inputs(offset, exposure) # attaches if needed
def _check_inputs(self, offset, exposure):
if offset is not None:
offset = np.asarray(offset)
if offset.shape[0] != self.endog.shape[0]:
raise ValueError("offset is not the same length as endog")
self.offset = offset
if exposure is not None:
exposure = np.log(exposure)
if exposure.shape[0] != self.endog.shape[0]:
raise ValueError("exposure is not the same length as endog")
self.exposure = exposure
#TODO: are these two methods only for Poisson? or also Negative Binomial?
def predict(self, params, exog=None, exposure=None, offset=None,
linear=False):
"""
Predict response variable of a count model given exogenous variables.
Notes
-----
If exposure is specified, then it will be logged by the method.
The user does not need to log it first.
"""
#TODO: add offset tp
if exog is None:
exog = self.exog
offset = getattr(self, 'offset', 0)
exposure = getattr(self, 'exposure', 0)
else:
if exposure is None:
exposure = 0
else:
exposure = np.log(exposure)
if offset is None:
offset = 0
if not linear:
return np.exp(np.dot(exog, params) + exposure + offset) # not cdf
else:
return np.dot(exog, params) + exposure + offset
return super(CountModel, self).predict(params, exog, linear)
def _derivative_exog(self, params, exog=None):
"""
"""
# group 3 poisson, nbreg, zip, zinb
if exog == None:
exog = self.exog
return self.predict(params, exog)[:,None] * params[None,:]
def fit(self, start_params=None, method='newton', maxiter=35,
full_output=1, disp=1, callback=None, **kwargs):
cntfit = super(CountModel, self).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)
fit.__doc__ = DiscreteModel.fit.__doc__
class OrderedModel(DiscreteModel):
pass
#### Public Model Classes ####
class Poisson(CountModel):
"""
Poisson model for count data
Parameters
----------
endog : array-like
1-d array of the response variable.
exog : array-like
`exog` is an n x p array where n is the number of observations and p
is the number of regressors including the intercept if one is included
in the data.
Attributes
-----------
endog : array
A reference to the endogenous response variable
exog : array
A reference to the exogenous design.
"""
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
-------
The value of the Poisson PMF at each point.
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\\beta
The parameter `X` is :math:`X\\beta` in the above formula.
"""
y = self.endog
return stats.poisson.pmf(y, np.exp(X))
def loglike(self, params):
"""
Loglikelihood of Poisson model
Parameters
----------
params : array-like
The parameters of the model.
Returns
-------
The log likelihood of the model evaluated at `params`
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
#np.sum(stats.poisson.logpmf(endog, np.exp(XB)))
return np.sum(-np.exp(XB) + 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
-------
The log likelihood for each observation of the model evaluated at `params`
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
#np.sum(stats.poisson.logpmf(endog, np.exp(XB)))
return -np.exp(XB) + endog*XB - gammaln(endog+1)
def score(self, params):
"""
Poisson model score (gradient) vector of the log-likelihood
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
The score vector of the model 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\\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 jac(self, params):
"""
Poisson model Jacobian of the log-likelihood for each observation
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
The score vector of the model 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\\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 hessian(self, params):
"""
Poisson model Hessian matrix of the loglikelihood
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
The Hessian matrix 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\\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)
class NbReg(DiscreteModel):
pass
class Logit(BinaryModel):
"""
Binary choice logit model
Parameters
----------
endog : array-like
1-d array of the response variable.
exog : array-like
`exog` is an n x p array where n is the number of observations and p
is the number of regressors including the intercept if one is included
in the data.
Attributes
-----------
endog : array
A reference to the endogenous response variable
exog : array
A reference to the exogenous design.
"""
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
-------
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
def loglike(self, params):
"""
Log-likelihood of logit model.
Parameters
-----------
params : array-like
The parameters of the logit model.
Returns
-------
The log-likelihood function of the logit model. 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
X = self.exog
return np.sum(np.log(self.cdf(q*np.dot(X,params))))
def loglikeobs(self, params):
"""
Log-likelihood of logit model for each observation.
Parameters
-----------
params : array-like
The parameters of the logit model.
Returns
-------
The log-likelihood function of the logit model. 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
X = self.exog
return np.log(self.cdf(q*np.dot(X,params)))
def score(self, params):
"""
Logit model score (gradient) vector of the log-likelihood
Parameters
----------
params: array-like
The parameters of the model
Returns
-------
The score vector of the model 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
L = self.cdf(np.dot(X,params))
return np.dot(y - L,X)
def jac(self, params):
"""
Logit model Jacobian of the log-likelihood for each observation
Parameters
----------
params: array-like
The parameters of the model
Returns
-------
jac : ndarray, (nobs, k)
The derivative of the loglikelihood evaluated at `params` for each
observation
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
L = self.cdf(np.dot(X, params))
return (y - L)[:,None] * X
def hessian(self, params):
"""
Logit model Hessian matrix of the log-likelihood
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
The Hessian 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.cdf(np.dot(X,params))
return -np.dot(L*(1-L)*X.T,X)
class Probit(BinaryModel):
"""
Binary choice Probit model
Parameters
----------
endog : array-like
1-d array of the response variable.
exog : array-like
`exog` is an n x p array where n is the number of observations and p
is the number of regressors including the intercept if one is included
in the data.
Attributes
-----------
endog : array
A reference to the endogenous response variable
exog : array
A reference to the exogenous design.
"""
def cdf(self, X):
"""
Probit (Normal) cumulative distribution function
Parameters
----------
X : array-like
The linear predictor of the model (XB).
Returns
--------
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
--------
The pdf evaluated at 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
-------
The log-likelihood evaluated at params
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
X = self.exog
return np.sum(np.log(np.clip(self.cdf(q*np.dot(X,params)),1e-20,
1)))
def loglikeobs(self, params):
"""
Log-likelihood of probit model for each observation
Parameters
----------
params : array-like
The parameters of the model.
Returns
-------
The log-likelihood evaluated at params
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
X = self.exog
return np.log(np.clip(self.cdf(q*np.dot(X,params)), 1e-20, 1))
def score(self, params):
"""
Probit model score (gradient) vector
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
The score vector of the model 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 = np.dot(X,params)
q = 2*y - 1
# clip to get rid of invalid divide complaint
L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), 1e-20, 1-1e-20)
return np.dot(L,X)
def jac(self, params):
"""
Probit model Jacobian for each observation
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
The score vector of the model 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 = np.dot(X,params)
q = 2*y - 1
# clip to get rid of invalid divide complaint
L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), 1e-20, 1-1e-20)
return L[:,None] * X
def hessian(self, params):
"""
Probit model Hessian matrix of the log-likelihood