def test_jarque_bera():
#tests against R fBasics
st_pv_R = np.array([1.9662677226861689, 0.3741367669648314])
jb = jarque_bera(x)[:2]
assert_almost_equal(jb, st_pv_R, 14)
st_pv_R = np.array([78.329987305556, 0.000000000000])
jb = jarque_bera(x**2)[:2]
assert_almost_equal(jb, st_pv_R, 13)
st_pv_R = np.array([5.7135750796706670, 0.0574530296971343])
jb = jarque_bera(np.log(x**2))[:2]
assert_almost_equal(jb, st_pv_R, 14)
st_pv_R = np.array([2.6489315748495761, 0.2659449923067881])
jb = jarque_bera(np.exp(-x**2))[:2]
assert_almost_equal(jb, st_pv_R, 14)
def test_jarque_bera():
#tests against R fBasics
st_pv_R = np.array([1.9662677226861689, 0.3741367669648314])
jb = jarque_bera(x)[:2]
assert_almost_equal(jb, st_pv_R, 14)
st_pv_R = np.array([78.329987305556, 0.000000000000])
jb = jarque_bera(x**2)[:2]
assert_almost_equal(jb, st_pv_R, 13)
st_pv_R = np.array([5.7135750796706670, 0.0574530296971343])
jb = jarque_bera(np.log(x**2))[:2]
assert_almost_equal(jb, st_pv_R, 14)
st_pv_R = np.array([2.6489315748495761, 0.2659449923067881])
jb = jarque_bera(np.exp(-x**2))[:2]
assert_almost_equal(jb, st_pv_R, 14)
def summary(self, yname=None, xname=None, returns='text'):
"""returns a string that summarizes the regression results
Parameters
-----------
yname : string, optional
Default is `Y`
xname : list of strings, optional
Default is `X.#` for # in p the number of regressors
Returns
-------
String summarizing the fit of a linear model.
Examples
--------
>>> import scikits.statsmodels.api as sm
>>> data = sm.datasets.longley.load()
>>> data.exog = sm.add_constant(data.exog)
>>> ols_results = sm.OLS(data.endog, data.exog).results
>>> print ols_results.summary()
...
Notes
-----
All residual statistics are calculated on whitened residuals.
"""
import time
from scikits.statsmodels.iolib.table import SimpleTable
from scikits.statsmodels.stats.stattools import (jarque_bera,
omni_normtest, durbin_watson)
if yname is None:
yname = self.model.endog_names
if xname is None:
xname = self.model.exog_names
modeltype = self.model.__class__.__name__
llf, aic, bic = self.llf, self.aic, self.bic
JB, JBpv, skew, kurtosis = jarque_bera(self.wresid)
omni, omnipv = omni_normtest(self.wresid)
t = time.localtime()
part1_fmt = dict(
data_fmts = ["%s"],
empty_cell = '',
colwidths = 15,
colsep=' ',
row_pre = '| ',
row_post = '|',
table_dec_above='=',
table_dec_below='',
header_dec_below=None,
header_fmt = '%s',
stub_fmt = '%s',
title_align='c',
header_align = 'r',
data_aligns = "r",
stubs_align = "l",
fmt = 'txt'
)
part2_fmt = dict(
#data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"],
data_fmts = ["%#10.4g","%#10.4g","%#6.4f","%#6.4f"],
#data_fmts = ["%#15.4F","%#15.4F","%#15.4F","%#14.4G"],
empty_cell = '',
colwidths = 14,
colsep=' ',
row_pre = '| ',
row_post = ' |',
table_dec_above='=',
table_dec_below='=',
header_dec_below='-',
header_fmt = '%s',
stub_fmt = '%s',
title_align='c',
header_align = 'r',
data_aligns = 'r',
stubs_align = 'l',
fmt = 'txt'
)
part3_fmt = dict(
#data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"],
data_fmts = ["%#10.4g","%#10.4g","%#10.4g","%#6.4g"],
empty_cell = '',
colwidths = 15,
colsep=' ',
row_pre = '| ',
row_post = ' |',
table_dec_above=None,
table_dec_below='-',
header_dec_below='-',
header_fmt = '%s',
stub_fmt = '%s',
title_align='c',
header_align = 'r',
data_aligns = 'r',
stubs_align = 'l',
fmt = 'txt'
)
# Print the first part of the summary table
part1data = [[yname],
[modeltype],
['Least Squares'],
[time.strftime("%a, %d %b %Y",t)],
[time.strftime("%H:%M:%S",t)],
[self.nobs],
[self.df_resid],
[self.df_model]]
part1header = None
part1title = 'Summary of Regression Results'
part1stubs = ('Dependent Variable:',
'Model:',
'Method:',
'Date:',
'Time:',
'# obs:',
'Df residuals:',
'Df model:')
part1 = SimpleTable(part1data,
part1header,
part1stubs,
title=part1title,
txt_fmt = part1_fmt)
######## summary Part 2 #######
part2data = zip([self.params[i] for i in range(len(xname))],
[self.bse[i] for i in range(len(xname))],
[self.tvalues[i] for i in range(len(xname))],
[self.pvalues[i] for i in range(len(xname))])
part2header = ('coefficient', 'std. error', 't-statistic', 'prob.')
part2stubs = xname
#dfmt={'data_fmt':["%#12.6g","%#12.6g","%#10.4g","%#5.4g"]}
part2 = SimpleTable(part2data,
part2header,
part2stubs,
title=None,
txt_fmt = part2_fmt)
self.summary2 = part2
######## summary Part 3 #######
part3Lheader = ['Models stats']
part3Rheader = ['Residual stats']
part3Lstubs = ('R-squared:',
'Adjusted R-squared:',
'F-statistic:',
'Prob (F-statistic):',
'Log likelihood:',
'AIC criterion:',
'BIC criterion:',)
part3Rstubs = ('Durbin-Watson:',
'Omnibus:',
'Prob(Omnibus):',
'JB:',
'Prob(JB):',
'Skew:',
'Kurtosis:')
part3Ldata = [[self.rsquared], [self.rsquared_adj],
[self.fvalue],
[self.f_pvalue],
[llf],
[aic],
[bic]]
part3Rdata = [[durbin_watson(self.wresid)],
[omni],
[omnipv],
[JB],
[JBpv],
[skew],
[kurtosis]]
part3L = SimpleTable(part3Ldata, part3Lheader, part3Lstubs,
txt_fmt = part3_fmt)
part3R = SimpleTable(part3Rdata, part3Rheader, part3Rstubs,
txt_fmt = part3_fmt)
part3L.extend_right(part3R)
######## Return Summary Tables ########
# join table parts then print
if returns == 'text':
return str(part1) + '\n' + str(part2) + '\n' + str(part3L)
elif returns == 'tables':
return [part1, part2 ,part3L]
elif returns == 'csv':
return part1.as_csv() + '\n' + part2.as_csv() + '\n' + \
part3L.as_csv()
elif returns == 'latex':
print('not available yet')
elif returns == 'html':
print('not available yet')
def summary(self, yname=None, xname=None, returns='text'):
"""returns a string that summarizes the regression results
Parameters
-----------
yname : string, optional
Default is `Y`
xname : list of strings, optional
Default is `X.#` for # in p the number of regressors
Returns
-------
String summarizing the fit of a linear model.
Examples
--------
>>> import scikits.statsmodels.api as sm
>>> data = sm.datasets.longley.load()
>>> data.exog = sm.add_constant(data.exog)
>>> ols_results = sm.OLS(data.endog, data.exog).results
>>> print ols_results.summary()
...
Notes
-----
All residual statistics are calculated on whitened residuals.
"""
import time
from scikits.statsmodels.iolib.table import SimpleTable
from scikits.statsmodels.stats.stattools import (jarque_bera,
omni_normtest, durbin_watson)
if yname is None:
yname = self.model.endog_names
if xname is None:
xname = self.model.exog_names
modeltype = self.model.__class__.__name__
llf, aic, bic = self.llf, self.aic, self.bic
JB, JBpv, skew, kurtosis = jarque_bera(self.wresid)
omni, omnipv = omni_normtest(self.wresid)
t = time.localtime()
part1_fmt = dict(
data_fmts = ["%s"],
empty_cell = '',
colwidths = 15,
colsep=' ',
row_pre = '| ',
row_post = '|',
table_dec_above='=',
table_dec_below='',
header_dec_below=None,
header_fmt = '%s',
stub_fmt = '%s',
title_align='c',
header_align = 'r',
data_aligns = "r",
stubs_align = "l",
fmt = 'txt'
)
part2_fmt = dict(
#data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"],
data_fmts = ["%#10.4g","%#10.4g","%#6.4f","%#6.4f"],
#data_fmts = ["%#15.4F","%#15.4F","%#15.4F","%#14.4G"],
empty_cell = '',
colwidths = 14,
colsep=' ',
row_pre = '| ',
row_post = ' |',
table_dec_above='=',
table_dec_below='=',
header_dec_below='-',
header_fmt = '%s',
stub_fmt = '%s',
title_align='c',
header_align = 'r',
data_aligns = 'r',
stubs_align = 'l',
fmt = 'txt'
)
part3_fmt = dict(
#data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"],
data_fmts = ["%#10.4g","%#10.4g","%#10.4g","%#6.4g"],
empty_cell = '',
colwidths = 15,
colsep=' ',
row_pre = '| ',
row_post = ' |',
table_dec_above=None,
table_dec_below='-',
header_dec_below='-',
header_fmt = '%s',
stub_fmt = '%s',
title_align='c',
header_align = 'r',
data_aligns = 'r',
stubs_align = 'l',
fmt = 'txt'
)
# Print the first part of the summary table
part1data = [[yname],
[modeltype],
['Least Squares'],
[time.strftime("%a, %d %b %Y",t)],
[time.strftime("%H:%M:%S",t)],
[self.nobs],
[self.df_resid],
[self.df_model]]
part1header = None
part1title = 'Summary of Regression Results'
part1stubs = ('Dependent Variable:',
'Model:',
'Method:',
'Date:',
'Time:',
'# obs:',
'Df residuals:',
'Df model:')
part1 = SimpleTable(part1data,
part1header,
part1stubs,
title=part1title,
txt_fmt = part1_fmt)
######## summary Part 2 #######
part2data = zip([self.params[i] for i in range(len(xname))],
[self.bse[i] for i in range(len(xname))],
[self.tvalues[i] for i in range(len(xname))],
[self.pvalues[i] for i in range(len(xname))])
part2header = ('coefficient', 'std. error', 't-statistic', 'prob.')
part2stubs = xname
#dfmt={'data_fmt':["%#12.6g","%#12.6g","%#10.4g","%#5.4g"]}
part2 = SimpleTable(part2data,
part2header,
part2stubs,
title=None,
txt_fmt = part2_fmt)
self.summary2 = part2
######## summary Part 3 #######
part3Lheader = ['Models stats']
part3Rheader = ['Residual stats']
part3Lstubs = ('R-squared:',
'Adjusted R-squared:',
'F-statistic:',
'Prob (F-statistic):',
'Log likelihood:',
'AIC criterion:',
'BIC criterion:',)
part3Rstubs = ('Durbin-Watson:',
'Omnibus:',
'Prob(Omnibus):',
'JB:',
'Prob(JB):',
'Skew:',
'Kurtosis:')
part3Ldata = [[self.rsquared], [self.rsquared_adj],
[self.fvalue],
[self.f_pvalue],
[llf],
[aic],
[bic]]
part3Rdata = [[durbin_watson(self.wresid)],
[omni],
[omnipv],
[JB],
[JBpv],
[skew],
[kurtosis]]
part3L = SimpleTable(part3Ldata, part3Lheader, part3Lstubs,
txt_fmt = part3_fmt)
part3R = SimpleTable(part3Rdata, part3Rheader, part3Rstubs,
txt_fmt = part3_fmt)
part3L.extend_right(part3R)
######## Return Summary Tables ########
# join table parts then print
if returns == 'text':
return str(part1) + '\n' + str(part2) + '\n' + str(part3L)
elif returns == 'tables':
return [part1, part2 ,part3L]
elif returns == 'csv':
return part1.as_csv() + '\n' + part2.as_csv() + '\n' + \
part3L.as_csv()
elif returns == 'latex':
print('not available yet')
elif returns == 'html':
print('not available yet')
