class TimeSeriesModelResults(base.LikelihoodModelResults):
def __init__(self, model, params, normalized_cov_params, scale=1.):
self.data = model.data
super(TimeSeriesModelResults,
self).__init__(model, params, normalized_cov_params, scale)
class TimeSeriesResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_attrs,
_attrs)
_methods = {'predict' : 'dates'}
_wrap_methods = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(TimeSeriesResultsWrapper, # noqa:E305
TimeSeriesModelResults)
if __name__ == "__main__":
import statsmodels.api as sm
import pandas
data = sm.datasets.macrodata.load(as_pandas=False)
#make a DataFrame
#TODO: attach a DataFrame to some of the datasets, for quicker use
dates = [str(int(x[0])) +':'+ str(int(x[1])) \
for x in data.data[['year','quarter']]]
df = pandas.DataFrame(data.data[['realgdp','realinv','realcons']], index=dates)
ex_mod = TimeSeriesModel(df)
Notes
-----
Support for mutli-link and multi-exog models is still experimental
in MLEInfluence. Interface and some definitions might still change.
Note: Difference to R betareg: Betareg has the same general leverage
as this model. However, they use a linear approximation hat matrix
to scale and studentize influence and residual statistics.
MLEInfluence uses the generalized leverage as hat_matrix_diag.
Additionally, MLEInfluence uses pearson residuals for residual
analusis.
References
----------
todo
"""
from statsmodels.stats.outliers_influence import MLEInfluence
return MLEInfluence(self)
def bootstrap(self, *args, **kwargs):
raise NotImplementedError
class BetaResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(BetaResultsWrapper, BetaResults)
if f1 <= f + L1_wt * np.abs(x) + 1e-10:
return x + h
# Fallback for models where the loss is not quadratic
from scipy.optimize import brent
x_opt = brent(func, args=(model, ), brack=(x - 1, x + 1), tol=tol)
return x_opt
class RegularizedResults(Results):
def __init__(self, model, params):
super(RegularizedResults, self).__init__(model, params)
@cache_readonly
def fittedvalues(self):
return self.model.predict(self.params)
class RegularizedResultsWrapper(wrap.ResultsWrapper):
_attrs = {
'params': 'columns',
'resid': 'rows',
'fittedvalues': 'rows',
}
_wrap_attrs = _attrs
wrap.populate_wrapper(
RegularizedResultsWrapper, # noqa:E305
RegularizedResults)
params : array
Fitted parameters
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
cov_type : string
The type of covariance matrix estimator to use. Can be one of 'approx',
'opg', 'robust', or 'none'.
Attributes
----------
model : Model instance
A reference to the model that was fit.
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
nobs : float
The number of observations used to fit the model.
params : array
The parameters of the model.
scale : float
This is currently set to 1.0 and not used by the model or its results.
"""
pass
class MarkovRegressionResultsWrapper(
markov_switching.MarkovSwitchingResultsWrapper):
pass
wrap.populate_wrapper(MarkovRegressionResultsWrapper, # noqa:E305
MarkovRegressionResults)
dates = self.data.dates._mpl_repr()
else:
dates = np.arange(self.nobs)
d = max(self.nobs_diffuse, self.loglikelihood_burn)
# Plot cusum series and reference line
ax.plot(dates[d:], self.cusum_squares, label='CUSUM of squares')
ref_line = (np.arange(d, self.nobs) - d) / (self.nobs - d)
ax.plot(dates[d:], ref_line, 'k', alpha=0.3)
# Plot significance bounds
lower_line, upper_line = self._cusum_squares_significance_bounds(alpha)
ax.plot([dates[d], dates[-1]], upper_line, 'k--',
label='%d%% significance' % (alpha * 100))
ax.plot([dates[d], dates[-1]], lower_line, 'k--')
ax.legend(loc=legend_loc)
return fig
class RecursiveLSResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(RecursiveLSResultsWrapper, # noqa:E305
RecursiveLSResults)
summary.tables.append(table)
# Add a table for all other parameters
masks = []
for m in (endog_masks, [state_cov_mask]):
m = np.array(m).flatten()
if len(m) > 0:
masks.append(m)
masks = np.concatenate(masks)
inverse_mask = np.array(list(set(indices).difference(set(masks))))
if len(inverse_mask) > 0:
table = make_table(self,
inverse_mask,
"Other parameters",
strip_end=False)
summary.tables.append(table)
return summary
summary.__doc__ = MLEResults.summary.__doc__
class VARMAXResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs, _attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(VARMAXResultsWrapper, VARMAXResults)
#add warnings/notes, added to text format only
etext =[]
wstr = \
'''If the model instance has been used for another fit with different fit
parameters, then the fit options might not be the correct ones anymore .'''
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
class RLMResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(RLMResultsWrapper, RLMResults)
if __name__=="__main__":
#NOTE: This is to be removed
#Delivery Time Data is taken from Montgomery and Peck
import statsmodels.api as sm
#delivery time(minutes)
endog = np.array([16.68, 11.50, 12.03, 14.88, 13.75, 18.11, 8.00, 17.83,
79.24, 21.50, 40.33, 21.00, 13.50, 19.75, 24.00, 29.00, 15.35, 19.00,
9.50, 35.10, 17.90, 52.32, 18.75, 19.83, 10.75])
#number of cases, distance (Feet)
exog = np.array([[7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6,
7, 3, 17, 10, 26, 9, 8, 4], [560, 220, 340, 80, 150, 330, 110, 210, 1460,
605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635,
av2 = k1 * av - k2 * vn
vm = np.eye(p) - 2 * sum(cv) / len(cv) + av2
a, b = np.linalg.eigh(vm)
jj = np.argsort(-a)
a = a[jj]
b = b[:, jj]
params = np.linalg.solve(self._covxr.T, b)
results = DimReductionResults(self, params, eigs=a)
return DimReductionResultsWrapper(results)
class DimReductionResults(model.Results):
def __init__(self, model, params, eigs):
super(DimReductionResults, self).__init__(
model, params)
self.eigs = eigs
class DimReductionResultsWrapper(wrap.ResultsWrapper):
_attrs = {
'params': 'columns',
}
_wrap_attrs = _attrs
wrap.populate_wrapper(DimReductionResultsWrapper,
DimReductionResults)
a coefficient is constrained to be zero (in which case it is zero).
polynomial_trend : array
Array containing trend polynomial coefficients, ordered from lowest
degree to highest. Initialized with ones, unless a coefficient is
constrained to be zero (in which case it is zero).
model_orders : list of int
The orders of each of the polynomials in the model.
param_terms : list of str
List of parameters actually included in the model, in sorted order.
See Also
--------
dismalpy.ssm.mlemodel.MLEResults
dismalpy.ssm.kalman_smoother.SmootherResults
dismalpy.ssm.kalman_filter.FilterResults
dismalpy.ssm.representation.FrozenRepresentation
"""
pass
class SARIMAXResultsWrapper(mlemodel.MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(SARIMAXResultsWrapper, SARIMAXResults)
----------
model : UnobservedComponents instance
The fitted model instance
Attributes
----------
specification : dictionary
Dictionary including all attributes from the unobserved components
model instance.
See Also
--------
dismalpy.ssm.mlemodel.MLEResults
dismalpy.ssm.kalman_smoother.SmootherResults
dismalpy.ssm.kalman_filter.FilterResults
dismalpy.ssm.representation.FrozenRepresentation
"""
pass
class UnobservedComponentsResultsWrapper(
mlemodel.MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(
mlemodel.MLEResultsWrapper._wrap_attrs, _attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(
mlemodel.MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(UnobservedComponentsResultsWrapper,
UnobservedComponentsResults)
model : DynamicFactor instance
The fitted model instance
Attributes
----------
specification : dictionary
Dictionary including all attributes from the DynamicFactor model
instance.
coefficient_matrices_var : array
Array containing autoregressive lag polynomial coefficient matrices,
ordered from lowest degree to highest.
See Also
--------
dismalpy.ssm.mlemodel.MLEResults
dismalpy.ssm.kalman_smoother.SmootherResults
dismalpy.ssm.kalman_filter.FilterResults
dismalpy.ssm.representation.FrozenRepresentation
"""
pass
class DynamicFactorResultsWrapper(mlemodel.MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(
mlemodel.MLEResultsWrapper._wrap_attrs, _attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(
mlemodel.MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(DynamicFactorResultsWrapper, DynamicFactorResults)
if not title is None: title = "Nonlinear Quantile Regression Results" from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=False) return smry class NLRQResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(NLRQResultsWrapper, NLRQResults) def Polynomial1(x, x0, par): K = x.shape[1]-1 mu0 = par[0] mu1 = par[1:K+1].reshape(K, 1) return (mu0 + np.dot(x-x0, mu1)).reshape(x.shape[0]) def DPolynomial1(x, x0, par): return np.concatenate([np.ones((x.shape[0], 1)), x-x0], axis=1), True def Polynomial2(x, x0, par): K = int(1/2+np.sqrt(x.shape[1]-3/4)) mu0 = par[0] mu1 = par[1:K+1].reshape(K, 1) mu2 = par[K+1:].reshape(K, K)
Attributes
----------
specification : dictionary
Dictionary including all attributes from the DynamicFactor model
instance.
coefficient_matrices_var : array
Array containing autoregressive lag polynomial coefficient matrices,
ordered from lowest degree to highest.
See Also
--------
dismalpy.ssm.mlemodel.MLEResults
dismalpy.ssm.kalman_smoother.SmootherResults
dismalpy.ssm.kalman_filter.FilterResults
dismalpy.ssm.representation.FrozenRepresentation
"""
pass
class DynamicFactorResultsWrapper(mlemodel.MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(DynamicFactorResultsWrapper, DynamicFactorResults)
return x + h
f1 = func(x + h, model) + L1_wt*np.abs(x + h)
if f1 <= f + L1_wt*np.abs(x) + 1e-10:
return x + h
# Fallback for models where the loss is not quadratic
from scipy.optimize import brent
x_opt = brent(func, args=(model,), brack=(x-1, x+1), tol=tol)
return x_opt
class RegularizedResults(Results):
def __init__(self, model, params):
super(RegularizedResults, self).__init__(model, params)
@cache_readonly
def fittedvalues(self):
return self.model.predict(self.params)
class RegularizedResultsWrapper(wrap.ResultsWrapper):
_attrs = {
'params': 'columns',
'resid': 'rows',
'fittedvalues': 'rows',
}
_wrap_attrs = _attrs
wrap.populate_wrapper(RegularizedResultsWrapper, # noqa:E305
RegularizedResults)
class HoltWintersResultsWrapper(ResultsWrapper):
_attrs = {
'fittedvalues': 'rows',
'level': 'rows',
'resid': 'rows',
'season': 'rows',
'slope': 'rows'
}
_wrap_attrs = union_dicts(ResultsWrapper._wrap_attrs, _attrs)
_methods = {'predict': 'dates', 'forecast': 'dates'}
_wrap_methods = union_dicts(ResultsWrapper._wrap_methods, _methods)
populate_wrapper(HoltWintersResultsWrapper, HoltWintersResults)
class ExponentialSmoothing(TimeSeriesModel):
"""
Holt Winter's Exponential Smoothing
Parameters
----------
endog : array_like
Time series
trend : {"add", "mul", "additive", "multiplicative", None}, optional
Type of trend component.
damped : bool, optional
Should the trend component be damped.
seasonal : {"add", "mul", "additive", "multiplicative", None}, optional
if True display the fitted values
coefficients : bool
if True display the estimated coefficients
"""
if which == "filtered":
state = self.filtered_state
fitted_values = self.filter_results.forecasts[0]
else:
state = self.smoothed_state
fitted_values = self.smoother_results.smoothed_forecasts[0]
endog = self.model.endog
if fitted:
pd.DataFrame({"endog": endog[:, 0], "fitted_values": fitted_values}
).plot(figsize=figsize)
if coefficients:
design = block_diag(*tuple(self.model.exog_design(mod)
for mod in self.model.exog_models)
)@state
pd.DataFrame(design.transpose()).plot(figsize=figsize)
class DynamicRegressionResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(DynamicRegressionResultsWrapper, # noqa:E305
DynamicRegressionResults)
smry = summary2.Summary()
smry.add_base(results=self,
alpha=alpha,
float_format=float_format,
xname=xname,
yname=yname,
title=title)
return smry
class RLMResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(RLMResultsWrapper, RLMResults)
if __name__ == "__main__":
#NOTE: This is to be removed
#Delivery Time Data is taken from Montgomery and Peck
import statsmodels.api as sm
#delivery time(minutes)
endog = np.array([
16.68, 11.50, 12.03, 14.88, 13.75, 18.11, 8.00, 17.83, 79.24, 21.50,
40.33, 21.00, 13.50, 19.75, 24.00, 29.00, 15.35, 19.00, 9.50, 35.10,
17.90, 52.32, 18.75, 19.83, 10.75
])
#number of cases, distance (Feet)
exog = np.array([[
if hasattr(self.data, 'dates') and self.data.dates is not None:
dates = self.data.dates._mpl_repr()
else:
dates = np.arange(self.nobs)
llb = self.loglikelihood_burn
# Plot cusum series and reference line
ax.plot(dates[llb:], self.cusum_squares, label='CUSUM of squares')
ref_line = (np.arange(llb, self.nobs) - llb) / (self.nobs - llb)
ax.plot(dates[llb:], ref_line, 'k', alpha=0.3)
# Plot significance bounds
lower_line, upper_line = self._cusum_squares_significance_bounds(alpha)
ax.plot([dates[llb], dates[-1]], upper_line, 'k--',
label='%d%% significance' % (alpha * 100))
ax.plot([dates[llb], dates[-1]], lower_line, 'k--')
ax.legend(loc=legend_loc)
return fig
class RecursiveLSResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(RecursiveLSResultsWrapper, RecursiveLSResults)
def get_margeff(self, at='overall', method='dydx', atexog=None,
dummy=False, count=False):
"""Get marginal effects of the fitted model.
Not yet implemented for Zero Inflated Models
"""
raise NotImplementedError("not yet implemented for zero inflation")
class L1ZeroInflatedPoissonResults(L1CountResults, ZeroInflatedPoissonResults):
pass
class ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(ZeroInflatedPoissonResultsWrapper,
ZeroInflatedPoissonResults)
class L1ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(L1ZeroInflatedPoissonResultsWrapper,
L1ZeroInflatedPoissonResults)
class ZeroInflatedGeneralizedPoissonResults(CountResults):
__doc__ = _discrete_results_docs % {
"one_line_description" : "A results class for Zero Inflated Generalized Poisson",
"extra_attr" : ""}
@cache_readonly
def _dispersion_factor(self):
xname=xname,
title=title)
smry.add_table_params(self,
yname=yname,
xname=xname,
alpha=alpha,
use_t=False)
return smry
class RQResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(RQResultsWrapper, RQResults)
def main_2():
data = sm.datasets.anes96.load()
data.exog = sm.add_constant(data.exog, prepend=False)
print(
sm.OLS(data.endog, data.exog).fit().summary(xname=data.exog_name,
yname=data.endog_name))
qrresults = quantreg(data.endog, data.exog,
tau=0.5).fit(excludeconstant=True)
print(qrresults.summary(xname=data.exog_name, yname=data.endog_name))
def main():
import pylab as plot
in each basis direction.
"""
def __init__(self, model, params, eigs):
super(DimReductionResults, self).__init__(model, params)
self.eigs = eigs
class DimReductionResultsWrapper(wrap.ResultsWrapper):
_attrs = {
'params': 'columns',
}
_wrap_attrs = _attrs
wrap.populate_wrapper(
DimReductionResultsWrapper, # noqa:E305
DimReductionResults)
def _grass_opt(params, fun, grad, maxiter, gtol):
"""
Minimize a function on a Grassmann manifold.
Parameters
----------
params : array_like
Starting value for the optimization.
fun : function
The function to be minimized.
grad : function
The gradient of fun.
A reference to the model that was fit.
filter_results : KalmanFilter instance
The underlying state space model and Kalman filter output
nobs : float
The number of observations used to fit the model.
params : array
The parameters of the model.
scale : float
This is currently set to 1.0 and not used by the model or its results.
See Also
--------
MLEModel
dismalpy.ssm.kalman_smoother.SmootherResults
dismalpy.ssm.kalman_filter.FilterResults
dismalpy.ssm.representation.FrozenRepresentation
"""
pass
class MLEResultsWrapper(mlemodel.MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(MLEResultsWrapper, MLEResults)
#ret_doc = """
# fcasterr : array-like
# confint : array-like
#"""
predict.__doc__ = '\n'.join(preddoc[:5] + preddoc[7:20] + extra_doc +
preddoc[20:])
class ARResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(ARResultsWrapper, ARResults)
if __name__ == "__main__":
import statsmodels.api as sm
sunspots = sm.datasets.sunspots.load()
# Why does R demean the data by defaut?
ar_ols = AR(sunspots.endog)
res_ols = ar_ols.fit(maxlag=9)
ar_mle = AR(sunspots.endog)
res_mle_bfgs = ar_mle.fit(maxlag=9, method="mle", solver="bfgs",
maxiter=500, gtol=1e-10)
# res_mle2 = ar_mle.fit(maxlag=1, method="mle", maxiter=500, penalty=True,
# tol=1e-13)
# ar_yw = AR(sunspots.endog)
return smry
class HoltWintersResultsWrapper(ResultsWrapper):
_attrs = {'fittedvalues': 'rows',
'level': 'rows',
'resid': 'rows',
'season': 'rows',
'slope': 'rows'}
_wrap_attrs = union_dicts(ResultsWrapper._wrap_attrs, _attrs)
_methods = {'predict': 'dates',
'forecast': 'dates'}
_wrap_methods = union_dicts(ResultsWrapper._wrap_methods, _methods)
populate_wrapper(HoltWintersResultsWrapper, HoltWintersResults)
class ExponentialSmoothing(TimeSeriesModel):
"""
Holt Winter's Exponential Smoothing
Parameters
----------
endog : array-like
Time series
trend : {"add", "mul", "additive", "multiplicative", None}, optional
Type of trend component.
damped : bool, optional
Should the trend component be damped.
seasonal : {"add", "mul", "additive", "multiplicative", None}, optional
self.data.predict_dates = dates
class TimeSeriesModelResults(base.LikelihoodModelResults):
def __init__(self, model, params, normalized_cov_params, scale=1.):
self.data = model.data
super(TimeSeriesModelResults,
self).__init__(model, params, normalized_cov_params, scale)
class TimeSeriesResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_attrs,
_attrs)
_methods = {'predict' : 'dates'}
_wrap_methods = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(TimeSeriesResultsWrapper,
TimeSeriesModelResults)
if __name__ == "__main__":
import statsmodels.api as sm
import datetime
import pandas
data = sm.datasets.macrodata.load()
#make a DataFrame
#TODO: attach a DataFrame to some of the datasets, for quicker use
dates = [str(int(x[0])) +':'+ str(int(x[1])) \
for x in data.data[['year','quarter']]]
df = pandas.DataFrame(data.data[['realgdp','realinv','realcons']], index=dates)
ex_mod = TimeSeriesModel(df)
if self.model.initialization_method != 'estimated':
params = np.array(self.initial_state)
if params.ndim > 1:
params = params[0]
names = self.model.state_names[1:]
param_header = ['initialization method: %s'
% self.model.initialization_method]
params_stubs = names
params_data = [[forg(params[i], prec=4)]
for i in range(len(params))]
initial_state_table = SimpleTable(params_data,
param_header,
params_stubs,
txt_fmt=fmt_params)
summary.tables.insert(-1, initial_state_table)
return summary
class ExponentialSmoothingResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(ExponentialSmoothingResultsWrapper, # noqa:E305
ExponentialSmoothingResults)
np.arange(len(self.params))[self.model._params_state_cov])
table = make_table(self, state_cov_mask, "Error covariance matrix",
strip_end=False)
summary.tables.append(table)
# Add a table for all other parameters
masks = []
for m in (endog_masks, [state_cov_mask]):
m = np.array(m).flatten()
if len(m) > 0:
masks.append(m)
masks = np.concatenate(masks)
inverse_mask = np.array(list(set(indices).difference(set(masks))))
if len(inverse_mask) > 0:
table = make_table(self, inverse_mask, "Other parameters",
strip_end=False)
summary.tables.append(table)
return summary
summary.__doc__ = MLEResults.summary.__doc__
class VARMAXResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(VARMAXResultsWrapper, VARMAXResults) # noqa:E305
dates = self.data.dates._mpl_repr()
else:
dates = np.arange(self.nobs)
d = max(self.nobs_diffuse, self.loglikelihood_burn)
# Plot cusum series and reference line
ax.plot(dates[d:], self.cusum_squares, label='CUSUM of squares')
ref_line = (np.arange(d, self.nobs) - d) / (self.nobs - d)
ax.plot(dates[d:], ref_line, 'k', alpha=0.3)
# Plot significance bounds
lower_line, upper_line = self._cusum_squares_significance_bounds(alpha)
ax.plot([dates[d], dates[-1]], upper_line, 'k--',
label='%d%% significance' % (alpha * 100))
ax.plot([dates[d], dates[-1]], lower_line, 'k--')
ax.legend(loc=legend_loc)
return fig
class RecursiveLSResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(RecursiveLSResultsWrapper, # noqa:E305
RecursiveLSResults)
smry = summary2.Summary()
smry.add_base(results=self,
alpha=alpha,
float_format=float_format,
xname=xname,
yname=yname,
title=title)
return smry
class RLMResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(RLMResultsWrapper, RLMResults) # noqa:E305
if __name__ == "__main__":
#NOTE: This is to be removed
#Delivery Time Data is taken from Montgomery and Peck
import statsmodels.api as sm
#delivery time(minutes)
endog = np.array([
16.68, 11.50, 12.03, 14.88, 13.75, 18.11, 8.00, 17.83, 79.24, 21.50,
40.33, 21.00, 13.50, 19.75, 24.00, 29.00, 15.35, 19.00, 9.50, 35.10,
17.90, 52.32, 18.75, 19.83, 10.75
])
#number of cases, distance (Feet)
exog = np.array([[
15))[:21].rjust(8)
t[6][2].data = 'Dev. Multi | Ordinal'[:21].rjust(21)
t[6][3].data = (
str(round(self.evidence_against_multinomial_for_ordinal, 6)) +
' | ' + str(round(self.evidence_against, 6)))[:21].rjust(8)
except:
t[4][2].data = 'Log-Likelihood Multinomial'[:21].rjust(21)
t[4][3].data = ''[:21].rjust(8)
t[5][2].data = 'Dev. Null vs Multi'[:21].rjust(21)
t[5][3].data = ''[:21].rjust(8)
t[6][2].data = 'Dev. Multi | Ordinal'[:21].rjust(21)
t[6][3].data = ''[:21].rjust(8)
warn = 'Warning: Run check_parallel_lines_assumption() first for deviance against multinomial'
t[7][2].data = 'Dev. Null vs Ordinal'[:21].rjust(21)
t[7][3].data = str(round(self.llr_pvalue, 15))[:21].rjust(8)
s.extra_txt = 'Dev. Null vs Ordinal: Evidence against intercept only model in favour of proportional odds model'
s.extra_txt = s.extra_txt + '\n' + 'Dev. Multinomial | Ordinal: Evidence against multinomial in favour of proportional odds model | Evidence against proportional odds model in favour of multinomial'
s.extra_txt = s.extra_txt + '\n' + 'Dev. Null vs Multinomial: Evidence against intercept only model in favor of Multinomial'
s.extra_txt = s.extra_txt + '\n' + warn
return s
class OrderedResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(OrderedResultsWrapper, OrderedResults)
f1 = func(x + h, model) + L1_wt * np.abs(x + h)
if f1 <= f + L1_wt * np.abs(x) + 1e-10:
return x + h
# Fallback for models where the loss is not quadratic
from scipy.optimize import brent
x_opt = brent(func, args=(model, ), brack=(x - 1, x + 1), tol=tol)
return x_opt
class RegularizedResults(Results):
def __init__(self, model, params):
super(RegularizedResults, self).__init__(model, params)
@cache_readonly
def fittedvalues(self):
return self.model.predict(self.params)
class RegularizedResultsWrapper(wrap.ResultsWrapper):
_attrs = {
'params': 'columns',
'resid': 'rows',
'fittedvalues': 'rows',
}
_wrap_attrs = _attrs
wrap.populate_wrapper(RegularizedResultsWrapper, RegularizedResults)
else:
dates = np.arange(self.nobs)
llb = self.loglikelihood_burn
# Plot cusum series and reference line
ax.plot(dates[llb:], self.cusum_squares, label='CUSUM of squares')
ref_line = (np.arange(llb, self.nobs) - llb) / (self.nobs - llb)
ax.plot(dates[llb:], ref_line, 'k', alpha=0.3)
# Plot significance bounds
lower_line, upper_line = self._cusum_squares_significance_bounds(alpha)
ax.plot([dates[llb], dates[-1]],
upper_line,
'k--',
label='%d%% significance' % (alpha * 100))
ax.plot([dates[llb], dates[-1]], lower_line, 'k--')
ax.legend(loc=legend_loc)
return fig
class RecursiveLSResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs, _attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(RecursiveLSResultsWrapper, RecursiveLSResults)
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
cov_type : string
The type of covariance matrix estimator to use. Can be one of 'approx',
'opg', 'robust', or 'none'.
Attributes
----------
model : Model instance
A reference to the model that was fit.
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
nobs : float
The number of observations used to fit the model.
params : array
The parameters of the model.
scale : float
This is currently set to 1.0 and not used by the model or its results.
"""
pass
class MarkovAutoregressionResultsWrapper(
markov_regression.MarkovRegressionResultsWrapper):
pass
wrap.populate_wrapper(MarkovAutoregressionResultsWrapper,
MarkovAutoregressionResults)
# argument.
if xname is None:
xname = self.model.exog_names
if yname is None:
yname = self.model.endog_names
# Create summary table instance
from statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(self,
gleft=top_left,
gright=top_right,
yname=yname,
xname=xname,
title=title)
smry.add_table_params(self,
yname=yname,
xname=xname,
alpha=alpha,
use_t=False)
return smry
class QIFResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(QIFResultsWrapper, QIFResults)
np.arange(len(self.params))[self.model._params_state_cov])
table = make_table(self, state_cov_mask, "Error covariance matrix",
strip_end=False)
summary.tables.append(table)
# Add a table for all other parameters
masks = []
for m in (endog_masks, [state_cov_mask]):
m = np.array(m).flatten()
if len(m) > 0:
masks.append(m)
masks = np.concatenate(masks)
inverse_mask = np.array(list(set(indices).difference(set(masks))))
if len(inverse_mask) > 0:
table = make_table(self, inverse_mask, "Other parameters",
strip_end=False)
summary.tables.append(table)
return summary
summary.__doc__ = MLEResults.summary.__doc__
class VARMAXResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(VARMAXResultsWrapper, VARMAXResults)
grad = np.zeros((q, c))
for ii in self._grp_ix:
x = lpr[ii, :]
jj = np.arange(x.shape[0], dtype=np.int)
y = self.endog[ii]
denom = 0.0
denomg = np.zeros((q, c))
for p in itertools.permutations(y):
v = np.exp(x[(jj, p)].sum())
denom += v
for i, r in enumerate(p):
if r != 0:
denomg[:, r - 1] += v * self.exog[ii[i], :]
for i, r in enumerate(y):
if r != 0:
grad[:, r - 1] += self.exog[ii[i], :]
grad -= denomg / denom
return grad.flatten()
class ConditionalResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(ConditionalResultsWrapper, ConditionalResults)
summary.tables.append(table)
# Add a table for all other parameters
masks = []
for m in (endog_masks, [state_cov_mask]):
m = np.array(m).flatten()
if len(m) > 0:
masks.append(m)
masks = np.concatenate(masks)
inverse_mask = np.array(list(set(indices).difference(set(masks))))
if len(inverse_mask) > 0:
table = make_table(self,
inverse_mask,
"Other parameters",
strip_end=False)
summary.tables.append(table)
return summary
summary.__doc__ = MLEResults.summary.__doc__
class VARMAXResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs, _attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(VARMAXResultsWrapper, VARMAXResults) # noqa:E305
#ret_doc = """
# fcasterr : array-like
# confint : array-like
#"""
predict.__doc__ = '\n'.join(preddoc[:5] + preddoc[7:20] + extra_doc +
preddoc[20:])
class ARResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(ARResultsWrapper, ARResults) # noqa:E305
if __name__ == "__main__":
import statsmodels.api as sm
sunspots = sm.datasets.sunspots.load(as_pandas=False)
# Why does R demean the data by defaut?
ar_ols = AR(sunspots.endog)
res_ols = ar_ols.fit(maxlag=9)
ar_mle = AR(sunspots.endog)
res_mle_bfgs = ar_mle.fit(maxlag=9, method="mle", solver="bfgs",
maxiter=500, gtol=1e-10)
# res_mle2 = ar_mle.fit(maxlag=1, method="mle", maxiter=500, penalty=True,
# tol=1e-13)
# ar_yw = AR(sunspots.endog)
# Add warnings/notes, added to text format only
etext = []
if hasattr(self, 'cov_type'):
etext.append(self.cov_kwds['description'])
if etext:
etext = ["[{0}] {1}".format(i + 1, text)
for i, text in enumerate(etext)]
etext.insert(0, "Warnings:")
summary.add_extra_txt(etext)
return summary
class MLEResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
# TODO right now, predict with full_results=True can return something other
# than a time series, so the `attach_dates` call will fail if we have
# 'predict': 'dates' here. In the future, remove `full_results` and replace
# it with new methods, e.g. get_prediction, get_forecast, and likely will
# want those to be a subclass of FilterResults with e.g. confidence
# intervals calculated and dates attached.
# Also, need to modify `attach_dates` to account for DataFrames.
_methods = {'predict': None}
_wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(MLEResultsWrapper, MLEResults)
ordered from lowest degree to highest. Initialized with ones, unless
a coefficient is constrained to be zero (in which case it is zero).
polynomial_trend : array
Array containing trend polynomial coefficients, ordered from lowest
degree to highest. Initialized with ones, unless a coefficient is
constrained to be zero (in which case it is zero).
model_orders : list of int
The orders of each of the polynomials in the model.
param_terms : list of str
List of parameters actually included in the model, in sorted order.
See Also
--------
dismalpy.ssm.mlemodel.MLEResults
dismalpy.ssm.kalman_smoother.SmootherResults
dismalpy.ssm.kalman_filter.FilterResults
dismalpy.ssm.representation.FrozenRepresentation
"""
pass
class SARIMAXResultsWrapper(mlemodel.MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_attrs, _attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(mlemodel.MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(SARIMAXResultsWrapper, SARIMAXResults)
if exog is not None:
orig_exog = self.model.data.orig_exog
exog_names = self.model.exog_names
self.model.data.orig_exog = self.model._input_exog
self.model.exog_names = self.model._input_exog_names
# Perform the appending procedure
out = super().append(endog,
exog=exog,
refit=refit,
fit_kwargs=fit_kwargs,
**kwargs)
# Now we reverse the temporary change made above
if exog is not None:
self.model.data.orig_exog = orig_exog
self.model.exog_names = exog_names
return out
class ARIMAResultsWrapper(sarimax.SARIMAXResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(sarimax.SARIMAXResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(
sarimax.SARIMAXResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(ARIMAResultsWrapper, ARIMAResults) # noqa:E305
if f1 <= f + L1_wt*np.abs(x) + 1e-10:
return x + h
# Fallback for models where the loss is not quadratic
from scipy.optimize import brent
x_opt = brent(func, args=(model,), brack=(x-1, x+1), tol=tol)
return x_opt
class RegularizedResults(Results):
def __init__(self, model, params):
super(RegularizedResults, self).__init__(model, params)
@cache_readonly
def fittedvalues(self):
return self.model.predict(self.params)
class RegularizedResultsWrapper(wrap.ResultsWrapper):
_attrs = {
'params': 'columns',
'resid': 'rows',
'fittedvalues': 'rows',
}
_wrap_attrs = _attrs
wrap.populate_wrapper(RegularizedResultsWrapper,
RegularizedResults)
self.data = model.data
super(TimeSeriesModelResults,
self).__init__(model, params, normalized_cov_params, scale)
class TimeSeriesResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_attrs,
_attrs)
_methods = {'predict': 'dates'}
_wrap_methods = wrap.union_dicts(
base.LikelihoodResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(
TimeSeriesResultsWrapper, # noqa:E305
TimeSeriesModelResults)
if __name__ == "__main__":
import statsmodels.api as sm
import pandas
mdata = sm.datasets.macrodata.load(as_pandas=False)
#make a DataFrame
#TODO: attach a DataFrame to some of the datasets, for quicker use
dates = [str(int(x[0])) +':'+ str(int(x[1])) \
for x in mdata.data[['year','quarter']]]
df = pandas.DataFrame(mdata.data[['realgdp', 'realinv', 'realcons']],
index=dates)
See Also
--------
statsmodels.iolib.summary2.Summary : class to hold 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)
return smry
class RLMResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(RLMResultsWrapper, RLMResults) # noqa:E305
if __name__=="__main__":
#NOTE: This is to be removed
#Delivery Time Data is taken from Montgomery and Peck
import statsmodels.api as sm
#delivery time(minutes)
endog = np.array([16.68, 11.50, 12.03, 14.88, 13.75, 18.11, 8.00, 17.83,
79.24, 21.50, 40.33, 21.00, 13.50, 19.75, 24.00, 29.00, 15.35, 19.00,
9.50, 35.10, 17.90, 52.32, 18.75, 19.83, 10.75])
#number of cases, distance (Feet)
exog = np.array([[7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6,
7, 3, 17, 10, 26, 9, 8, 4], [560, 220, 340, 80, 150, 330, 110, 210, 1460,
self.data.predict_dates = dates
class TimeSeriesModelResults(base.LikelihoodModelResults):
def __init__(self, model, params, normalized_cov_params, scale=1.):
self.data = model.data
super(TimeSeriesModelResults,
self).__init__(model, params, normalized_cov_params, scale)
class TimeSeriesResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_attrs,
_attrs)
_methods = {'predict' : 'dates'}
_wrap_methods = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(TimeSeriesResultsWrapper,
TimeSeriesModelResults)
if __name__ == "__main__":
import statsmodels.api as sm
import datetime
import pandas
data = sm.datasets.macrodata.load()
#make a DataFrame
#TODO: attach a DataFrame to some of the datasets, for quicker use
dates = [str(int(x[0])) +':'+ str(int(x[1])) \
for x in data.data[['year','quarter']]]
df = pandas.DataFrame(data.data[['realgdp','realinv','realcons']], index=dates)
ex_mod = TimeSeriesModel(df)
xname=xname, yname=yname, title=title)
return smry
class GLMResultsWrapper(lm.RegressionResultsWrapper):
_attrs = {
'resid_anscombe' : 'rows',
'resid_deviance' : 'rows',
'resid_pearson' : 'rows',
'resid_response' : 'rows',
'resid_working' : 'rows'
}
_wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs,
_attrs)
wrap.populate_wrapper(GLMResultsWrapper, GLMResults)
if __name__ == "__main__":
import statsmodels.api as sm
data = sm.datasets.longley.load()
#data.exog = add_constant(data.exog)
GLMmod = GLM(data.endog, data.exog).fit()
GLMT = GLMmod.summary(returns='tables')
## GLMT[0].extend_right(GLMT[1])
## print(GLMT[0])
## print(GLMT[2])
GLMTp = GLMmod.summary(title='Test GLM')
"""
From Stata
params : array
Fitted parameters
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
cov_type : string
The type of covariance matrix estimator to use. Can be one of 'approx',
'opg', 'robust', or 'none'.
Attributes
----------
model : Model instance
A reference to the model that was fit.
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
nobs : float
The number of observations used to fit the model.
params : array
The parameters of the model.
scale : float
This is currently set to 1.0 and not used by the model or its results.
"""
pass
class MarkovAutoregressionResultsWrapper(
markov_regression.MarkovRegressionResultsWrapper):
pass
wrap.populate_wrapper(MarkovAutoregressionResultsWrapper, # noqa:E305
MarkovAutoregressionResults)
grad = np.zeros((q, c))
for ii in self._grp_ix:
x = lpr[ii, :]
jj = np.arange(x.shape[0], dtype=int)
y = self.endog[ii]
denom = 0.0
denomg = np.zeros((q, c))
for p in itertools.permutations(y):
v = np.exp(x[(jj, p)].sum())
denom += v
for i, r in enumerate(p):
if r != 0:
denomg[:, r - 1] += v * self.exog[ii[i], :]
for i, r in enumerate(y):
if r != 0:
grad[:, r - 1] += self.exog[ii[i], :]
grad -= denomg / denom
return grad.flatten()
class ConditionalResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(ConditionalResultsWrapper, ConditionalResults)
@cache_readonly
def gcv(self):
return self.scale / (1. - self.hat_matrix_trace / self.nobs)**2
@cache_readonly
def cv(self):
cv_ = ((self.resid_pearson / (1. - self.hat_matrix_diag))**2).sum()
cv_ /= self.nobs
return cv_
class GLMGamResultsWrapper(GLMResultsWrapper):
pass
wrap.populate_wrapper(GLMGamResultsWrapper, GLMGamResults)
class GLMGam(PenalizedMixin, GLM):
"""Model class for generalized additive models, GAM.
This inherits from `GLM`.
Warning: Not all inherited methods might take correctly account of the
penalization. Not all options including offset and exposure have been
verified yet.
Parameters
----------
endog : array_like
exog : array_like or None
params : array
Fitted parameters
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
cov_type : string
The type of covariance matrix estimator to use. Can be one of 'approx',
'opg', 'robust', or 'none'.
Attributes
----------
model : Model instance
A reference to the model that was fit.
filter_results : HamiltonFilterResults or KimSmootherResults instance
The underlying filter and, optionally, smoother output
nobs : float
The number of observations used to fit the model.
params : array
The parameters of the model.
scale : float
This is currently set to 1.0 and not used by the model or its results.
"""
pass
class MarkovAutoregressionResultsWrapper(
markov_regression.MarkovRegressionResultsWrapper):
pass
wrap.populate_wrapper(MarkovAutoregressionResultsWrapper,
MarkovAutoregressionResults)
#ret_doc = """
# fcasterr : array-like
# confint : array-like
#"""
predict.__doc__ = '\n'.join(preddoc[:5] + preddoc[7:20] + extra_doc +
preddoc[20:])
class ARResultsWrapper(wrap.ResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(ARResultsWrapper, ARResults)
if __name__ == "__main__":
import statsmodels.api as sm
sunspots = sm.datasets.sunspots.load()
# Why does R demean the data by defaut?
ar_ols = AR(sunspots.endog)
res_ols = ar_ols.fit(maxlag=9)
ar_mle = AR(sunspots.endog)
res_mle_bfgs = ar_mle.fit(maxlag=9, method="mle", solver="bfgs",
maxiter=500, gtol=1e-10)
# res_mle2 = ar_mle.fit(maxlag=1, method="mle", maxiter=500, penalty=True,
# tol=1e-13)
# ar_yw = AR(sunspots.endog)
('Df Model:', None),
('Outer Iterations:', ["%d" % self.fit_history['outer_iterations']]),
('Inner Iterations:', ["%d" % self.fit_history['avg_inner_iterations']]) ]
if not title is None:
title = "Quantile Regression Results"
from statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title)
smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=False)
return smry
class RQResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(RQResultsWrapper, RQResults)
def main():
data = sm.datasets.longley.load()
data.exog = sm.add_constant(data.exog, prepend=False)
xname = ["x1", "x2", "x3", "x4", "x5", "x6", "x7"]
yname = ["y"]
qrresults = RQ(data.endog, data.exog, tau=0.9).fit()
print(qrresults.summary(xname=xname, yname=yname))
if __name__ == '__main__':
main()
