def summary(self, yname=None, xname=None, title='Generalized linear model',
returns='print'):
"""
Print a table of results or returns SimpleTable() instance which
summarizes the Generalized linear model results.
Parameters
-----------
yname : string
optional, Default is `Y`
xname : list of strings
optional, Default is `X.#` for # in p the number of regressors
title : string
optional, Defualt is 'Generalized linear model'
returns : string
'text', 'table', 'csv', 'latex', 'html'
Returns
-------
Defualt :
returns='print'
Prints the summarirized results
Option :
returns='text'
Prints the summarirized results
Option :
returns='table'
SimpleTable instance : summarizing the fit of a linear model.
Option :
returns='csv'
returns a string of csv of the results, to import into a spreadsheet
Option :
returns='latex'
Not implimented yet
Option :
returns='HTML'
Not implimented yet
Examples (needs updating)
--------
>>> 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
-----
stand_errors are not implimented.
conf_int calculated from normal dist.
"""
import time as Time
from iolib import SimpleTable
from stattools import jarque_bera, omni_normtest, durbin_watson
if yname is None:
yname = 'Y'
if xname is None:
xname = ['x%d' % i for i in range(self.model.exog.shape[1])]
#List of results used in summary
#yname = yname
#xname = xname
time = Time.localtime()
dist_family = self.model.family.__class__.__name__
aic = self.aic
bic = self.bic
deviance = self.deviance
df_model = self.df_model
df_resid = self.df_resid
fittedvalues = self.fittedvalues
llf = self.llf
mu = self.mu
nobs = self.nobs
normalized_cov_params = self.normalized_cov_params
null_deviance = self.null_deviance
params = self.params
pearson_chi2 = self.pearson_chi2
pinv_wexog = self.pinv_wexog
resid_anscombe = self.resid_anscombe
resid_deviance = self.resid_deviance
resid_pearson = self.resid_pearson
resid_response = self.resid_response
resid_working = self.resid_working
scale = self.scale
#TODO #stand_errors = self.stand_errors
stand_errors = [' ' for x in range(len(self.params))]
#Added note about conf_int
pvalues = self.pvalues
conf_int = self.conf_int()
cov_params = self.cov_params()
#f_test() = self.f_test()
t = self.tvalues
#t_test = self.t_test()
table_1l_fmt = dict(
data_fmts = ["%s", "%s", "%s", "%s", "%s"],
empty_cell = '',
colwidths = 17,
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'
)
# Note table_1l_fmt over rides the below formating.
table_1r_fmt = dict(
data_fmts = ["%s", "%s", "%s", "%s", "%S"],
empty_cell = '',
colwidths = 16,
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'
)
table_2_fmt = dict(
data_fmts = ["%s", "%s", "%s", "%s"],
#data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"],
#data_fmts = ["%#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'
)
######## summary table 1 #######
table_1l_title = title
table_1l_header = None
table_1l_stubs = ('Model Family:',
'Method:',
'Dependent Variable:',
'Date:',
'Time:',
)
table_1l_data = [
[dist_family],
['IRLS'],
[yname],
[Time.strftime("%a, %d %b %Y",time)],
[Time.strftime("%H:%M:%S",time)],
]
table_1l = SimpleTable(table_1l_data,
table_1l_header,
table_1l_stubs,
title=table_1l_title,
txt_fmt = table_1l_fmt)
table_1r_title = None
table_1r_header = None
table_1r_stubs = ('# of obs:',
'Df residuals:',
'Df model:',
'Scale:',
'Log likelihood:'
)
table_1r_data = [
[nobs],
[df_resid],
[df_model],
["%#6.4f" % (scale,)],
["%#6.4f" % (llf,)]
]
table_1r = SimpleTable(table_1r_data,
table_1r_header,
table_1r_stubs,
title=table_1r_title,
txt_fmt = table_1r_fmt)
######## summary table 2 #######
#TODO add % range to confidance interval column header
table_2header = ('coefficient', 'stand errors', 't-statistic',
'Conf. Interval')
table_2stubs = xname
table_2data = zip(["%#6.4f" % (params[i]) for i in range(len(xname))],
[stand_errors[i] for i in range(len(xname))],
["%#6.4f" % (t[i]) for i in range(len(xname))],
["""[%#6.3f, %#6.3f]""" % tuple(conf_int[i]) for i in
range(len(xname))])
#dfmt={'data_fmt':["%#12.6g","%#12.6g","%#10.4g","%#5.4g"]}
table_2 = SimpleTable(table_2data,
table_2header,
table_2stubs,
title=None,
txt_fmt = table_2_fmt)
######## Return Summary Tables ########
# join table table_s then print
if returns == 'text':
table_1l.extend_right(table_1r)
return str(table_1l) + '\n' + str(table_2)
elif returns == 'print':
table_1l.extend_right(table_1r)
print(str(table_1l) + '\n' + str(table_2))
elif returns == 'tables':
return [table_1l, table_1r, table_2]
#return [table_1, table_2 ,table_3L, notes]
elif returns == 'csv':
return table_1.as_csv() + '\n' + table_2.as_csv() + '\n' + \
table_3L.as_csv()
elif returns == 'latex':
print('not avalible yet')
elif returns == html:
print('not avalible yet')
def summary_old(self,
yname=None,
xname=None,
title='Generalized linear model',
returns='text'):
"""
Print a table of results or returns SimpleTable() instance which
summarizes the Generalized linear model results.
Parameters
-----------
yname : string
optional, Default is `Y`
xname : list of strings
optional, Default is `X.#` for # in p the number of regressors
title : string
optional, Defualt is 'Generalized linear model'
returns : string
'text', 'table', 'csv', 'latex', 'html'
Returns
-------
Defualt :
returns='print'
Prints the summarirized results
Option :
returns='text'
Prints the summarirized results
Option :
returns='table'
SimpleTable instance : summarizing the fit of a linear model.
Option :
returns='csv'
returns a string of csv of the results, to import into a spreadsheet
Option :
returns='latex'
Not implimented yet
Option :
returns='HTML'
Not implimented yet
Examples (needs updating)
--------
>>> 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
-----
stand_errors are not implimented.
conf_int calculated from normal dist.
"""
import time as Time
from iolib import SimpleTable
from stattools import jarque_bera, omni_normtest, durbin_watson
yname = 'Y'
if xname is None:
xname = ['x%d' % i for i in range(self.model.exog.shape[1])]
#List of results used in summary
#yname = yname
#xname = xname
time = Time.localtime()
dist_family = self.model.family.__class__.__name__
aic = self.aic
bic = self.bic
deviance = self.deviance
df_model = self.df_model
df_resid = self.df_resid
fittedvalues = self.fittedvalues
llf = self.llf
mu = self.mu
nobs = self.nobs
normalized_cov_params = self.normalized_cov_params
null_deviance = self.null_deviance
params = self.params
pearson_chi2 = self.pearson_chi2
pinv_wexog = self.pinv_wexog
resid_anscombe = self.resid_anscombe
resid_deviance = self.resid_deviance
resid_pearson = self.resid_pearson
resid_response = self.resid_response
resid_working = self.resid_working
scale = self.scale
#TODO #stand_errors = self.stand_errors
stand_errors = self.bse #[' ' for x in range(len(self.params))]
#Added note about conf_int
pvalues = self.pvalues
conf_int = self.conf_int()
cov_params = self.cov_params()
#f_test() = self.f_test()
t = self.tvalues
#t_test = self.t_test()
table_1l_fmt = dict(data_fmts=["%s", "%s", "%s", "%s", "%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')
# Note table_1l_fmt over rides the below formating. in extend_right? JP
table_1r_fmt = dict(data_fmts=["%s", "%s", "%s", "%s", "%1s"],
empty_cell='',
colwidths=12,
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')
table_2_fmt = dict(
data_fmts=["%s", "%s", "%s", "%s"],
#data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"],
#data_fmts = ["%#10.4g","%#6.4f", "%#6.4f"],
#data_fmts = ["%#15.4F","%#15.4F","%#15.4F","%#14.4G"],
empty_cell='',
colwidths=13,
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')
######## summary table 1 #######
table_1l_title = title
table_1l_header = None
table_1l_stubs = (
'Model Family:',
'Method:',
'Dependent Variable:',
'Date:',
'Time:',
)
table_1l_data = [
[dist_family],
['IRLS'],
[yname],
[Time.strftime("%a, %d %b %Y", time)],
[Time.strftime("%H:%M:%S", time)],
]
table_1l = SimpleTable(table_1l_data,
table_1l_header,
table_1l_stubs,
title=table_1l_title,
txt_fmt=table_1l_fmt)
table_1r_title = None
table_1r_header = None
table_1r_stubs = ('# of obs:', 'Df residuals:', 'Df model:', 'Scale:',
'Log likelihood:')
table_1r_data = [[nobs], [df_resid], [df_model],
["%#6.4f" % (scale, )], ["%#6.4f" % (llf, )]]
table_1r = SimpleTable(table_1r_data,
table_1r_header,
table_1r_stubs,
title=table_1r_title,
txt_fmt=table_1r_fmt)
######## summary table 2 #######
#TODO add % range to confidance interval column header
table_2header = ('coefficient', 'stand errors', 't-statistic',
'Conf. Interval')
table_2stubs = xname
table_2data = zip(
["%#6.4f" % (params[i]) for i in range(len(xname))],
["%#6.4f" % stand_errors[i] for i in range(len(xname))],
["%#6.4f" % (t[i]) for i in range(len(xname))], [
""" [%#6.3f, %#6.3f]""" % tuple(conf_int[i])
for i in range(len(xname))
])
#dfmt={'data_fmt':["%#12.6g","%#12.6g","%#10.4g","%#5.4g"]}
table_2 = SimpleTable(table_2data,
table_2header,
table_2stubs,
title=None,
txt_fmt=table_2_fmt)
######## Return Summary Tables ########
# join table table_s then print
if returns == 'text':
table_1l.extend_right(table_1r)
return str(table_1l) + '\n' + str(table_2)
elif returns == 'print':
table_1l.extend_right(table_1r)
print(str(table_1l) + '\n' + str(table_2))
elif returns == 'tables':
return [table_1l, table_1r, table_2]
#return [table_1, table_2 ,table_3L, notes]
elif returns == 'csv':
return table_1.as_csv() + '\n' + table_2.as_csv() + '\n' + \
table_3L.as_csv()
elif returns == 'latex':
print('not avalible yet')
elif returns == html:
print('not avalible yet')
