are not identified.'''
etext.append(wstr)
elif predclose_frac > 0.1: #TODO: get better diagnosis
wstr = \
'''Possibly complete quasi-separation: A fraction %f4.2 of observations can be
perfectly predicted. This might indicate that there is complete
quasi-separation. In this case some parameters will not be identified.''' % predclose_frac
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
class DiscreteResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(DiscreteResultsWrapper, DiscreteResults)
if __name__=="__main__":
import numpy as np
import scikits.statsmodels.api as sm
# Scratch work for negative binomial models
# dvisits was written using an R package, I can provide the dataset
# on request until the copyright is cleared up
#TODO: request permission to use dvisits
data2 = np.genfromtxt('../datasets/dvisits/dvisits.csv', names=True)
# note that this has missing values for Accident
endog = data2['doctorco']
exog = data2[['sex','age','agesq','income','levyplus','freepoor',
'freerepa','illness','actdays','hscore','chcond1',
'chcond2']].view(float).reshape(len(data2),-1)
exog = sm.add_constant(exog, prepend=True)
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 scikits.statsmodels.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
#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 scikits.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,
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 scikits.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([[
fcasterr = np.sqrt(sigma2 * np.cumsum(ma_rep**2))
const = norm.ppf(1 - alpha/2.)
conf_int = np.c_[forecast - const*fcasterr, forecast + const*fcasterr]
return forecast, fcasterr, conf_int
class ARMAResultsWrapper(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(ARMAResultsWrapper, ARMAResults)
if __name__ == "__main__":
import numpy as np
import scikits.statsmodels.api as sm
# simulate arma process
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
y = arma_generate_sample([1., -.75],[1.,.25], nsample=1000)
arma = ARMA(y)
res = arma.fit(trend='nc', order=(1,1))
np.random.seed(12345)
y_arma22 = arma_generate_sample([1.,-.85,.35],[1,.25,-.9], nsample=1000)
arma22 = ARMA(y_arma22)
res22 = arma22.fit(trend = 'nc', order=(2,2))
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 scikits.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']]]
try:
import scikits.timeseries as ts
ts_dates = date_array(start_date = Date(year=1959,quarter=1,freq='Q'),
conf_int = np.c_[forecast - const * fcasterr,
forecast + const * fcasterr]
return forecast, fcasterr, conf_int
class ARMAResultsWrapper(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(ARMAResultsWrapper, ARMAResults)
if __name__ == "__main__":
import numpy as np
import scikits.statsmodels.api as sm
# simulate arma process
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
y = arma_generate_sample([1., -.75], [1., .25], nsample=1000)
arma = ARMA(y)
res = arma.fit(trend='nc', order=(1, 1))
np.random.seed(12345)
y_arma22 = arma_generate_sample([1., -.85, .35], [1, .25, -.9],
nsample=1000)
arma22 = ARMA(y_arma22)
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
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 scikits.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']]]
try:
import scikits.timeseries as ts
ts_dates = date_array(start_date=Date(year=1959, quarter=1, freq='Q'),
'''Possibly complete quasi-separation: A fraction %f4.2 of observations can be
perfectly predicted. This might indicate that there is complete
quasi-separation. In this case some parameters will not be identified.''' % predclose_frac
etext.append(wstr)
if etext:
smry.add_extra_txt(etext)
return smry
class DiscreteResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(DiscreteResultsWrapper, DiscreteResults)
if __name__ == "__main__":
import numpy as np
import scikits.statsmodels.api as sm
# Scratch work for negative binomial models
# dvisits was written using an R package, I can provide the dataset
# on request until the copyright is cleared up
#TODO: request permission to use dvisits
data2 = np.genfromtxt('../datasets/dvisits/dvisits.csv', names=True)
# note that this has missing values for Accident
endog = data2['doctorco']
exog = data2[[
'sex', 'age', 'agesq', 'income', 'levyplus', 'freepoor', 'freerepa',
'illness', 'actdays', 'hscore', 'chcond1', 'chcond2'
]].view(float).reshape(len(data2), -1)
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 scikits.statsmodels.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')
"""
'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)