def mad(a, c=Gaussian.ppf(3 / 4.), axis=0, center=np.median):
# c \approx .6745
"""
The Median Absolute Deviation along given axis of an array
Parameters
----------
a : array-like
Input array.
c : float, optional
The normalization constant. Defined as scipy.stats.norm.ppf(3/4.),
which is approximately .6745.
axis : int, optional
The defaul is 0. Can also be None.
center : callable or float
If a callable is provided, such as the default `np.median` then it
is expected to be called center(a). The axis argument will be applied
via np.apply_over_axes. Otherwise, provide a float.
Returns
-------
mad : float
`mad` = median(abs(`a` - center))/`c`
"""
a = np.asarray(a)
if callable(center):
center = np.apply_over_axes(center, a, axis)
return np.median((np.fabs(a - center)) / c, axis=axis)
def mad(a, c=Gaussian.ppf(3/4.), axis=0, center=np.median):
# c \approx .6745
"""
The Median Absolute Deviation along given axis of an array
Parameters
----------
a : array-like
Input array.
c : float, optional
The normalization constant. Defined as scipy.stats.norm.ppf(3/4.),
which is approximately .6745.
axis : int, optional
The defaul is 0. Can also be None.
center : callable or float
If a callable is provided, such as the default `np.median` then it
is expected to be called center(a). The axis argument will be applied
via np.apply_over_axes. Otherwise, provide a float.
Returns
-------
mad : float
`mad` = median(abs(`a` - center))/`c`
"""
a = np.asarray(a)
if callable(center):
center = np.apply_over_axes(center, a, axis)
return np.median((np.fabs(a-center))/c, axis=axis)
def __init__(self, shape, h = 1.0, domain = None, norm = None):
"""
shape should be a function taking and returning numeric type.
For sanity it should always return positive or zero but this isn't
enforced in case you want to do weird things. Bear in mind that the
statistical tests etc. may not be valid for non-positive kernels.
The bandwidth of the kernel is supplied as h.
You may specify a domain as a list of 2 values [min, max], in which case
kernel will be treated as zero outside these values. This will speed up
calculation.
You may also specify the normalisation constant for the supplied Kernel.
If you do this number will be stored and used as the normalisation
without calculation. It is recommended you do this if you know the
constant, to speed up calculation. In particular if the shape function
provided is already normalised you should provide norm = 1.0.
Warning: I think several calculations assume that the kernel is
normalized. No tests for non-normalized kernel.
"""
self._normconst = norm # a value or None, if None, then calculate
self.domain = domain
self.weights = None
if callable(shape):
self._shape = shape
else:
raise TypeError("shape must be a callable object/function")
self._h = h
self._L2Norm = None
self._kernel_var = None
self._normal_reference_constant = None
self._order = None
def date_parser(timestr, parserinfo=None, **kwargs):
"""
Uses dateutil.parser.parse, but also handles monthly dates of the form
1999m4, 1999:m4, 1999:mIV, 1999mIV and the same for quarterly data
with q instead of m. It is not case sensitive. The default for annual
data is the end of the year, which also differs from dateutil.
"""
flags = re.IGNORECASE | re.VERBOSE
if re.search(_q_pattern, timestr, flags):
y,q = timestr.replace(":","").lower().split('q')
month, day = _quarter_to_day[q.upper()]
year = int(y)
elif re.search(_m_pattern, timestr, flags):
y,m = timestr.replace(":","").lower().split('m')
month, day = _month_to_day[m.upper()]
year = int(y)
if _is_leap(y) and month == 2:
day += 1
elif re.search(_y_pattern, timestr, flags):
month, day = 12, 31
year = int(timestr)
else:
if (hasattr(pandas_datetools, 'parser') and
not callable(pandas_datetools.parser)):
# exists in 0.8.0 pandas, but it's the class not the module
return pandas_datetools.parser.parse(timestr, parserinfo,
**kwargs)
else: # 0.8.1 pandas version didn't import this into namespace
from dateutil import parser
return parser.parse(timestr, parserinfo, **kwargs)
return datetime.datetime(year, month, day)
def get_columns(self, *args, **kw):
"""
Calling function for factor instance.
"""
v = self.namespace[self._name]
while True:
if callable(v):
if isinstance(v, (Term, Formula)):
v = copy.copy(v)
v.namespace = self.namespace
v = v(*args, **kw)
else:
break
n = len(v)
if self.ordinal:
col = [float(self.keys.index(v[i])) for i in range(n)]
return np.array(col)
else:
value = []
for key in self.keys:
col = [float((v[i] == key)) for i in range(n)]
value.append(col)
return np.array(value)
def get_columns(self, *args, **kw):
"""
Calling function for factor instance.
"""
v = self.namespace[self._name]
while True:
if callable(v):
if isinstance(v, (Term, Formula)):
v = copy.copy(v)
v.namespace = self.namespace
v = v(*args, **kw)
else: break
n = len(v)
if self.ordinal:
col = [float(self.keys.index(v[i])) for i in range(n)]
return np.array(col)
else:
value = []
for key in self.keys:
col = [float((v[i] == key)) for i in range(n)]
value.append(col)
return np.array(value)
def date_parser(timestr, parserinfo=None, **kwargs):
"""
Uses dateutil.parser.parse, but also handles monthly dates of the form
1999m4, 1999:m4, 1999:mIV, 1999mIV and the same for quarterly data
with q instead of m. It is not case sensitive. The default for annual
data is the end of the year, which also differs from dateutil.
"""
flags = re.IGNORECASE | re.VERBOSE
if re.search(_q_pattern, timestr, flags):
y, q = timestr.replace(":", "").lower().split('q')
month, day = _quarter_to_day[q.upper()]
year = int(y)
elif re.search(_m_pattern, timestr, flags):
y, m = timestr.replace(":", "").lower().split('m')
month, day = _month_to_day[m.upper()]
year = int(y)
if _is_leap(y) and month == 2:
day += 1
elif re.search(_y_pattern, timestr, flags):
month, day = 12, 31
year = int(timestr)
else:
if (hasattr(pandas_datetools, 'parser')
and not callable(pandas_datetools.parser)):
# exists in 0.8.0 pandas, but it's the class not the module
return pandas_datetools.parser.parse(timestr, parserinfo, **kwargs)
else: # 0.8.1 pandas version didn't import this into namespace
from dateutil import parser
return parser.parse(timestr, parserinfo, **kwargs)
return datetime.datetime(year, month, day)
def __init__(self, shape, h = 1.0, domain = None, norm = None):
"""
shape should be a function taking and returning numeric type.
For sanity it should always return positive or zero but this isn't
enforced in case you want to do weird things. Bear in mind that the
statistical tests etc. may not be valid for non-positive kernels.
The bandwidth of the kernel is supplied as h.
You may specify a domain as a list of 2 values [min, max], in which case
kernel will be treated as zero outside these values. This will speed up
calculation.
You may also specify the normalisation constant for the supplied Kernel.
If you do this number will be stored and used as the normalisation
without calculation. It is recommended you do this if you know the
constant, to speed up calculation. In particular if the shape function
provided is already normalised you should provide norm = 1.0.
Warning: I think several calculations assume that the kernel is
normalized. No tests for non-normalized kernel.
"""
self._normconst = norm # a value or None, if None, then calculate
self.domain = domain
self.weights = None
if callable(shape):
self._shape = shape
else:
raise TypeError("shape must be a callable object/function")
self._h = h
self._L2Norm = None
self._kernel_var = None
self._normal_reference_constant = None
self._order = None
def __call__(self, *args, **kw):
"""
Return the columns associated to self in a design matrix.
If the term has no 'func' attribute, it returns
``self.namespace[self.termname]``
else, it returns
``self.func(*args, **kw)``
"""
if not hasattr(self, 'func'):
val = self.namespace[self.termname]
else:
val = self.func
if callable(val):
if isinstance(val, (Term, Formula)):
val = copy.copy(val)
val.namespace = self.namespace
val = val(*args, **kw)
val = np.asarray(val)
return np.squeeze(val)
def __call__(self, *args, **kw):
"""
Return the columns associated to self in a design matrix.
If the term has no 'func' attribute, it returns
``self.namespace[self.termname]``
else, it returns
``self.func(*args, **kw)``
"""
if not hasattr(self, 'func'):
val = self.namespace[self.termname]
else:
val = self.func
if callable(val):
if isinstance(val, (Term, Formula)):
val = copy.copy(val)
val.namespace = self.namespace
val = val(*args, **kw)
val = np.asarray(val)
return np.squeeze(val)
def __init__(self, rvs, cdf, args=(), N=20):
if isinstance(rvs, string_types):
#cdf = getattr(stats, rvs).cdf
if (not cdf) or (cdf == rvs):
cdf = getattr(distributions, rvs).cdf
rvs = getattr(distributions, rvs).rvs
else:
raise AttributeError(
'if rvs is string, cdf has to be the same distribution')
if isinstance(cdf, string_types):
cdf = getattr(distributions, cdf).cdf
if callable(rvs):
kwds = {'size': N}
vals = np.sort(rvs(*args, **kwds))
else:
vals = np.sort(rvs)
N = len(vals)
cdfvals = cdf(vals, *args)
self.nobs = N
self.vals_sorted = vals
self.cdfvals = cdfvals
def __init__(self, rvs, cdf, args=(), N=20):
if isinstance(rvs, string_types):
#cdf = getattr(stats, rvs).cdf
if (not cdf) or (cdf == rvs):
cdf = getattr(distributions, rvs).cdf
rvs = getattr(distributions, rvs).rvs
else:
raise AttributeError('if rvs is string, cdf has to be the same distribution')
if isinstance(cdf, string_types):
cdf = getattr(distributions, cdf).cdf
if callable(rvs):
kwds = {'size':N}
vals = np.sort(rvs(*args,**kwds))
else:
vals = np.sort(rvs)
N = len(vals)
cdfvals = cdf(vals, *args)
self.nobs = N
self.vals_sorted = vals
self.cdfvals = cdfvals
def _updateloglike(self, params, xi10, ntrain, penalty, upperbounds,
lowerbounds, F, A, H, Q, R, history):
"""
"""
paramsorig = params
# are the bounds binding?
if penalty:
params = np.min((np.max(
(lowerbounds, params), axis=0), upperbounds),
axis=0)
#TODO: does it make sense for all of these to be allowed to be None?
if F != None and callable(F):
F = F(params)
elif F == None:
F = 0
if A != None and callable(A):
A = A(params)
elif A == None:
A = 0
if H != None and callable(H):
H = H(params)
elif H == None:
H = 0
print(callable(Q))
if Q != None and callable(Q):
Q = Q(params)
elif Q == None:
Q = 0
if R != None and callable(R):
R = R(params)
elif R == None:
R = 0
X = self.exog
if X == None:
X = 0
y = self.endog
loglike = kalmanfilter(F, A, H, Q, R, y, X, xi10, ntrain, history)
# use a quadratic penalty function to move away from bounds
if penalty:
loglike += penalty * np.sum((paramsorig - params)**2)
return loglike
def _updateloglike(self, params, xi10, ntrain, penalty, upperbounds, lowerbounds,
F,A,H,Q,R, history):
"""
"""
paramsorig = params
# are the bounds binding?
if penalty:
params = np.min((np.max((lowerbounds, params), axis=0),upperbounds),
axis=0)
#TODO: does it make sense for all of these to be allowed to be None?
if F != None and callable(F):
F = F(params)
elif F == None:
F = 0
if A != None and callable(A):
A = A(params)
elif A == None:
A = 0
if H != None and callable(H):
H = H(params)
elif H == None:
H = 0
print(callable(Q))
if Q != None and callable(Q):
Q = Q(params)
elif Q == None:
Q = 0
if R != None and callable(R):
R = R(params)
elif R == None:
R = 0
X = self.exog
if X == None:
X = 0
y = self.endog
loglike = kalmanfilter(F,A,H,Q,R,y,X, xi10, ntrain, history)
# use a quadratic penalty function to move away from bounds
if penalty:
loglike += penalty * np.sum((paramsorig-params)**2)
return loglike
def margeff_cov_params(model, params, exog, cov_params, at, derivative,
dummy_ind, count_ind, method, J):
"""
Computes the variance-covariance of marginal effects by the delta method.
Parameters
----------
model : model instance
The model that returned the fitted results. Its pdf method is used
for computing the Jacobian of discrete variables in dummy_ind and
count_ind
params : array-like
estimated model parameters
exog : array-like
exogenous variables at which to calculate the derivative
cov_params : array-like
The variance-covariance of the parameters
at : str
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.
Only overall has any effect here.you
derivative : function or array-like
If a function, it returns the marginal effects of the model with
respect to the exogenous variables evaluated at exog. Expected to be
called derivative(params, exog). This will be numerically
differentiated. Otherwise, it can be the Jacobian of the marginal
effects with respect to the parameters.
dummy_ind : array-like
Indices of the columns of exog that contain dummy variables
count_ind : array-like
Indices of the columns of exog that contain count variables
Notes
-----
For continuous regressors, the variance-covariance is given by
Asy. Var[MargEff] = [d margeff / d params] V [d margeff / d params]'
where V is the parameter variance-covariance.
The outer Jacobians are computed via numerical differentiation if
derivative is a function.
"""
if callable(derivative):
from statsmodels.tools.numdiff import approx_fprime_cs
params = params.ravel('F') # for Multinomial
try:
jacobian_mat = approx_fprime_cs(params,
derivative,
args=(exog, method))
except TypeError: # norm.cdf doesn't take complex values
from statsmodels.tools.numdiff import approx_fprime
jacobian_mat = approx_fprime(params,
derivative,
args=(exog, method))
if at == 'overall':
jacobian_mat = np.mean(jacobian_mat, axis=1)
else:
jacobian_mat = jacobian_mat.squeeze() # exog was 2d row vector
if dummy_ind is not None:
jacobian_mat = _margeff_cov_params_dummy(model, jacobian_mat,
params, exog, dummy_ind,
method, J)
if count_ind is not None:
jacobian_mat = _margeff_cov_params_count(model, jacobian_mat,
params, exog, count_ind,
method, J)
else:
jacobian_mat = derivative
#NOTE: this won't go through for at == 'all'
return np.dot(np.dot(jacobian_mat, cov_params), jacobian_mat.T)
def kstest(rvs, cdf, args=(), N=20, alternative = 'two_sided', mode='approx',**kwds):
"""
Perform the Kolmogorov-Smirnov test for goodness of fit
This performs a test of the distribution G(x) of an observed
random variable against a given distribution F(x). Under the null
hypothesis the two distributions are identical, G(x)=F(x). The
alternative hypothesis can be either 'two_sided' (default), 'less'
or 'greater'. The KS test is only valid for continuous distributions.
Parameters
----------
rvs : string or array or callable
string: name of a distribution in scipy.stats
array: 1-D observations of random variables
callable: function to generate random variables, requires keyword
argument `size`
cdf : string or callable
string: name of a distribution in scipy.stats, if rvs is a string then
cdf can evaluate to `False` or be the same as rvs
callable: function to evaluate cdf
args : tuple, sequence
distribution parameters, used if rvs or cdf are strings
N : int
sample size if rvs is string or callable
alternative : 'two_sided' (default), 'less' or 'greater'
defines the alternative hypothesis (see explanation)
mode : 'approx' (default) or 'asymp'
defines the distribution used for calculating p-value
'approx' : use approximation to exact distribution of test statistic
'asymp' : use asymptotic distribution of test statistic
Returns
-------
D : float
KS test statistic, either D, D+ or D-
p-value : float
one-tailed or two-tailed p-value
Notes
-----
In the one-sided test, the alternative is that the empirical
cumulative distribution function of the random variable is "less"
or "greater" than the cumulative distribution function F(x) of the
hypothesis, G(x)<=F(x), resp. G(x)>=F(x).
Examples
--------
>>> from scipy import stats
>>> import numpy as np
>>> from scipy.stats import kstest
>>> x = np.linspace(-15,15,9)
>>> kstest(x,'norm')
(0.44435602715924361, 0.038850142705171065)
>>> np.random.seed(987654321) # set random seed to get the same result
>>> kstest('norm','',N=100)
(0.058352892479417884, 0.88531190944151261)
is equivalent to this
>>> np.random.seed(987654321)
>>> kstest(stats.norm.rvs(size=100),'norm')
(0.058352892479417884, 0.88531190944151261)
Test against one-sided alternative hypothesis:
>>> np.random.seed(987654321)
Shift distribution to larger values, so that cdf_dgp(x)< norm.cdf(x):
>>> x = stats.norm.rvs(loc=0.2, size=100)
>>> kstest(x,'norm', alternative = 'less')
(0.12464329735846891, 0.040989164077641749)
Reject equal distribution against alternative hypothesis: less
>>> kstest(x,'norm', alternative = 'greater')
(0.0072115233216311081, 0.98531158590396395)
Don't reject equal distribution against alternative hypothesis: greater
>>> kstest(x,'norm', mode='asymp')
(0.12464329735846891, 0.08944488871182088)
Testing t distributed random variables against normal distribution:
With 100 degrees of freedom the t distribution looks close to the normal
distribution, and the kstest does not reject the hypothesis that the sample
came from the normal distribution
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(100,size=100),'norm')
(0.072018929165471257, 0.67630062862479168)
With 3 degrees of freedom the t distribution looks sufficiently different
from the normal distribution, that we can reject the hypothesis that the
sample came from the normal distribution at a alpha=10% level
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(3,size=100),'norm')
(0.131016895759829, 0.058826222555312224)
"""
if isinstance(rvs, string_types):
#cdf = getattr(stats, rvs).cdf
if (not cdf) or (cdf == rvs):
cdf = getattr(distributions, rvs).cdf
rvs = getattr(distributions, rvs).rvs
else:
raise AttributeError('if rvs is string, cdf has to be the same distribution')
if isinstance(cdf, string_types):
cdf = getattr(distributions, cdf).cdf
if callable(rvs):
kwds = {'size':N}
vals = np.sort(rvs(*args,**kwds))
else:
vals = np.sort(rvs)
N = len(vals)
cdfvals = cdf(vals, *args)
if alternative in ['two_sided', 'greater']:
Dplus = (np.arange(1.0, N+1)/N - cdfvals).max()
if alternative == 'greater':
return Dplus, distributions.ksone.sf(Dplus,N)
if alternative in ['two_sided', 'less']:
Dmin = (cdfvals - np.arange(0.0, N)/N).max()
if alternative == 'less':
return Dmin, distributions.ksone.sf(Dmin,N)
if alternative == 'two_sided':
D = np.max([Dplus,Dmin])
if mode == 'asymp':
return D, distributions.kstwobign.sf(D*np.sqrt(N))
if mode == 'approx':
pval_two = distributions.kstwobign.sf(D*np.sqrt(N))
if N > 2666 or pval_two > 0.80 - N*0.3/1000.0 :
return D, distributions.kstwobign.sf(D*np.sqrt(N))
else:
return D, distributions.ksone.sf(D,N)*2
def kstest(rvs,
cdf,
args=(),
N=20,
alternative='two_sided',
mode='approx',
**kwds):
"""
Perform the Kolmogorov-Smirnov test for goodness of fit
This performs a test of the distribution G(x) of an observed
random variable against a given distribution F(x). Under the null
hypothesis the two distributions are identical, G(x)=F(x). The
alternative hypothesis can be either 'two_sided' (default), 'less'
or 'greater'. The KS test is only valid for continuous distributions.
Parameters
----------
rvs : string or array or callable
string: name of a distribution in scipy.stats
array: 1-D observations of random variables
callable: function to generate random variables, requires keyword
argument `size`
cdf : string or callable
string: name of a distribution in scipy.stats, if rvs is a string then
cdf can evaluate to `False` or be the same as rvs
callable: function to evaluate cdf
args : tuple, sequence
distribution parameters, used if rvs or cdf are strings
N : int
sample size if rvs is string or callable
alternative : 'two_sided' (default), 'less' or 'greater'
defines the alternative hypothesis (see explanation)
mode : 'approx' (default) or 'asymp'
defines the distribution used for calculating p-value
'approx' : use approximation to exact distribution of test statistic
'asymp' : use asymptotic distribution of test statistic
Returns
-------
D : float
KS test statistic, either D, D+ or D-
p-value : float
one-tailed or two-tailed p-value
Notes
-----
In the one-sided test, the alternative is that the empirical
cumulative distribution function of the random variable is "less"
or "greater" than the cumulative distribution function F(x) of the
hypothesis, G(x)<=F(x), resp. G(x)>=F(x).
Examples
--------
>>> from scipy import stats
>>> import numpy as np
>>> from scipy.stats import kstest
>>> x = np.linspace(-15,15,9)
>>> kstest(x,'norm')
(0.44435602715924361, 0.038850142705171065)
>>> np.random.seed(987654321) # set random seed to get the same result
>>> kstest('norm','',N=100)
(0.058352892479417884, 0.88531190944151261)
is equivalent to this
>>> np.random.seed(987654321)
>>> kstest(stats.norm.rvs(size=100),'norm')
(0.058352892479417884, 0.88531190944151261)
Test against one-sided alternative hypothesis:
>>> np.random.seed(987654321)
Shift distribution to larger values, so that cdf_dgp(x)< norm.cdf(x):
>>> x = stats.norm.rvs(loc=0.2, size=100)
>>> kstest(x,'norm', alternative = 'less')
(0.12464329735846891, 0.040989164077641749)
Reject equal distribution against alternative hypothesis: less
>>> kstest(x,'norm', alternative = 'greater')
(0.0072115233216311081, 0.98531158590396395)
Don't reject equal distribution against alternative hypothesis: greater
>>> kstest(x,'norm', mode='asymp')
(0.12464329735846891, 0.08944488871182088)
Testing t distributed random variables against normal distribution:
With 100 degrees of freedom the t distribution looks close to the normal
distribution, and the kstest does not reject the hypothesis that the sample
came from the normal distribution
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(100,size=100),'norm')
(0.072018929165471257, 0.67630062862479168)
With 3 degrees of freedom the t distribution looks sufficiently different
from the normal distribution, that we can reject the hypothesis that the
sample came from the normal distribution at a alpha=10% level
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(3,size=100),'norm')
(0.131016895759829, 0.058826222555312224)
"""
if isinstance(rvs, string_types):
#cdf = getattr(stats, rvs).cdf
if (not cdf) or (cdf == rvs):
cdf = getattr(distributions, rvs).cdf
rvs = getattr(distributions, rvs).rvs
else:
raise AttributeError(
'if rvs is string, cdf has to be the same distribution')
if isinstance(cdf, string_types):
cdf = getattr(distributions, cdf).cdf
if callable(rvs):
kwds = {'size': N}
vals = np.sort(rvs(*args, **kwds))
else:
vals = np.sort(rvs)
N = len(vals)
cdfvals = cdf(vals, *args)
if alternative in ['two_sided', 'greater']:
Dplus = (np.arange(1.0, N + 1) / N - cdfvals).max()
if alternative == 'greater':
return Dplus, distributions.ksone.sf(Dplus, N)
if alternative in ['two_sided', 'less']:
Dmin = (cdfvals - np.arange(0.0, N) / N).max()
if alternative == 'less':
return Dmin, distributions.ksone.sf(Dmin, N)
if alternative == 'two_sided':
D = np.max([Dplus, Dmin])
if mode == 'asymp':
return D, distributions.kstwobign.sf(D * np.sqrt(N))
if mode == 'approx':
pval_two = distributions.kstwobign.sf(D * np.sqrt(N))
if N > 2666 or pval_two > 0.80 - N * 0.3 / 1000.0:
return D, distributions.kstwobign.sf(D * np.sqrt(N))
else:
return D, distributions.ksone.sf(D, N) * 2
def margeff_cov_params(model, params, exog, cov_params, at, derivative,
dummy_ind, count_ind, method, J):
"""
Computes the variance-covariance of marginal effects by the delta method.
Parameters
----------
model : model instance
The model that returned the fitted results. Its pdf method is used
for computing the Jacobian of discrete variables in dummy_ind and
count_ind
params : array-like
estimated model parameters
exog : array-like
exogenous variables at which to calculate the derivative
cov_params : array-like
The variance-covariance of the parameters
at : str
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.
Only overall has any effect here.you
derivative : function or array-like
If a function, it returns the marginal effects of the model with
respect to the exogenous variables evaluated at exog. Expected to be
called derivative(params, exog). This will be numerically
differentiated. Otherwise, it can be the Jacobian of the marginal
effects with respect to the parameters.
dummy_ind : array-like
Indices of the columns of exog that contain dummy variables
count_ind : array-like
Indices of the columns of exog that contain count variables
Notes
-----
For continuous regressors, the variance-covariance is given by
Asy. Var[MargEff] = [d margeff / d params] V [d margeff / d params]'
where V is the parameter variance-covariance.
The outer Jacobians are computed via numerical differentiation if
derivative is a function.
"""
if callable(derivative):
from statsmodels.tools.numdiff import approx_fprime_cs
params = params.ravel('F') # for Multinomial
try:
jacobian_mat = approx_fprime_cs(params, derivative,
args=(exog,method))
except TypeError: # norm.cdf doesn't take complex values
from statsmodels.tools.numdiff import approx_fprime
jacobian_mat = approx_fprime(params, derivative,
args=(exog,method))
if at == 'overall':
jacobian_mat = np.mean(jacobian_mat, axis=1)
else:
jacobian_mat = jacobian_mat.squeeze() # exog was 2d row vector
if dummy_ind is not None:
jacobian_mat = _margeff_cov_params_dummy(model, jacobian_mat,
params, exog, dummy_ind, method, J)
if count_ind is not None:
jacobian_mat = _margeff_cov_params_count(model, jacobian_mat,
params, exog, count_ind, method, J)
else:
jacobian_mat = derivative
#NOTE: this won't go through for at == 'all'
return np.dot(np.dot(jacobian_mat, cov_params), jacobian_mat.T)
