print('not avalible yet')
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 gwstatsmodels.api as sm
import numpy as np
data = sm.datasets.longley.load()
#data.exog = add_constant(data.exog)
GLMmod = GLM(data.endog, data.exog).fit()
GLMT = GLMmod.summary(returns='tables')
## GLMT[0].extend_right(GLMT[1])
## print(GLMT[0])
## print(GLMT[2])
GLMTp = GLMmod.summary(title='Test GLM')
"""
From Stata
. webuse beetle
print('not avalible yet')
elif returns == html:
print('not avalible yet')
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 gwstatsmodels.api as sm
import numpy as np
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')
"""
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 gwstatsmodels.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']]]
try:
import scikits.timeseries as ts
ts_dates = date_array(start_date = Date(year=1959,quarter=1,freq='Q'),
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 gwstatsmodels.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']]]
try:
import scikits.timeseries as ts
ts_dates = date_array(start_date=Date(year=1959, quarter=1, freq='Q'),
interval, and .10 would return a 90% confidence interval.""".split('\n')
#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 gwstatsmodels.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)
idx = np.argsort(np.abs(roots))[::-1] # sort by reverse modulus
return roots[idx]
class VARResultsWrapper(wrap.ResultsWrapper):
_attrs = {'bse' : 'columns_eq', 'cov_params' : 'cov',
'params' : 'columns_eq', 'pvalues' : 'columns_eq',
'tvalues' : 'columns_eq', 'sigma_u' : 'cov_eq',
'sigma_u_mle' : 'cov_eq',
'stderr' : 'columns_eq'}
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
_methods)
_wrap_methods.pop('cov_params') # not yet a method in VARResults
wrap.populate_wrapper(VARResultsWrapper, VARResults)
class FEVD(object):
"""
Compute and plot Forecast error variance decomposition and asymptotic
standard errors
"""
def __init__(self, model, P=None, periods=None):
self.periods = periods
self.model = model
self.neqs = model.neqs
self.names = model.names
self.irfobj = model.irf(var_decomp=P, periods=periods)
self.orth_irfs = self.irfobj.orth_irfs
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 gwstatsmodels.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([[
#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 gwstatsmodels.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,
