from statsmodels.compat.python import range, lrange, lmap, lzip, zip_longest
import numpy as np
from statsmodels.iolib.table import SimpleTable
from statsmodels.iolib.tableformatting import (gen_fmt, fmt_2,
fmt_params, fmt_base, fmt_2cols)
#from statsmodels.iolib.summary2d import summary_params_2dflat
#from summary2d import summary_params_2dflat
def forg(x, prec=3):
if prec == 3:
#for 3 decimals
if (abs(x) >= 1e4) or (abs(x) < 1e-4):
return '%9.3g' % x
else:
return '%9.3f' % x
elif prec == 4:
if (abs(x) >= 1e4) or (abs(x) < 1e-4):
return '%10.4g' % x
else:
return '%10.4f' % x
else:
raise NotImplementedError
def summary(self, yname=None, xname=None, title=0, alpha=.05,
returns='text', model_info=None):
"""
Parameters
-----------
yname : string
optional, Default is `Y`
xname : list of strings
optional, Default is `X.#` for # in p the number of regressors
Confidance interval : (0,1) not implimented
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 statsmodels 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
-----
conf_int calculated from normal dist.
"""
import time as time
#TODO Make sure all self.model.__class__.__name__ are listed
model_types = {'OLS' : 'Ordinary least squares',
'GLS' : 'Generalized least squares',
'GLSAR' : 'Generalized least squares with AR(p)',
'WLS' : 'Weigthed least squares',
'RLM' : 'Robust linear model',
'GLM' : 'Generalized linear model'
}
model_methods = {'OLS' : 'Least Squares',
'GLS' : 'Least Squares',
'GLSAR' : 'Least Squares',
'WLS' : 'Least Squares',
'RLM' : '?',
'GLM' : '?'
}
if title==0:
title = model_types[self.model.__class__.__name__]
if yname is None:
try:
yname = self.model.endog_names
except AttributeError:
yname = 'y'
if xname is None:
try:
xname = self.model.exog_names
except AttributeError:
xname = ['var_%d' % i for i in range(len(self.params))]
time_now = time.localtime()
time_of_day = [time.strftime("%H:%M:%S", time_now)]
date = time.strftime("%a, %d %b %Y", time_now)
modeltype = self.model.__class__.__name__
#dist_family = self.model.family.__class__.__name__
nobs = self.nobs
df_model = self.df_model
df_resid = self.df_resid
#General part of the summary table, Applicable to all? models
#------------------------------------------------------------
#TODO: define this generically, overwrite in model classes
#replace definition of stubs data by single list
#e.g.
gen_left = [('Model type:', [modeltype]),
('Date:', [date]),
('Dependent Variable:', yname), #What happens with multiple names?
('df model', [df_model])
]
gen_stubs_left, gen_data_left = zip_longest(*gen_left) #transpose row col
gen_title = title
gen_header = None
## gen_stubs_left = ('Model type:',
## 'Date:',
## 'Dependent Variable:',
## 'df model'
## )
## gen_data_left = [[modeltype],
## [date],
## yname, #What happens with multiple names?
## [df_model]
## ]
gen_table_left = SimpleTable(gen_data_left,
gen_header,
gen_stubs_left,
title = gen_title,
txt_fmt = gen_fmt
)
gen_stubs_right = ('Method:',
'Time:',
'Number of Obs:',
'df resid'
)
gen_data_right = ([modeltype], #was dist family need to look at more
time_of_day,
[nobs],
[df_resid]
)
gen_table_right = SimpleTable(gen_data_right,
gen_header,
gen_stubs_right,
title = gen_title,
txt_fmt = gen_fmt
)
gen_table_left.extend_right(gen_table_right)
general_table = gen_table_left
#Parameters part of the summary table
#------------------------------------
#Note: this is not necessary since we standardized names, only t versus normal
tstats = {'OLS' : self.t(),
'GLS' : self.t(),
'GLSAR' : self.t(),
'WLS' : self.t(),
'RLM' : self.t(),
'GLM' : self.t()
}
prob_stats = {'OLS' : self.pvalues,
'GLS' : self.pvalues,
'GLSAR' : self.pvalues,
'WLS' : self.pvalues,
'RLM' : self.pvalues,
'GLM' : self.pvalues
}
#Dictionary to store the header names for the parameter part of the
#summary table. look up by modeltype
alp = str((1-alpha)*100)+'%'
param_header = {
'OLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'GLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'GLSAR' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'WLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'GLM' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'], #glm uses t-distribution
'RLM' : ['coef', 'std err', 'z', 'P>|z|', alp + ' Conf. Interval'] #checke z
}
params_stubs = xname
params = self.params
conf_int = self.conf_int(alpha)
std_err = self.bse
exog_len = lrange(len(xname))
tstat = tstats[modeltype]
prob_stat = prob_stats[modeltype]
# Simpletable should be able to handle the formating
params_data = lzip(["%#6.4g" % (params[i]) for i in exog_len],
["%#6.4f" % (std_err[i]) for i in exog_len],
["%#6.4f" % (tstat[i]) for i in exog_len],
["%#6.4f" % (prob_stat[i]) for i in exog_len],
["(%#5g, %#5g)" % tuple(conf_int[i]) for i in \
exog_len]
)
parameter_table = SimpleTable(params_data,
param_header[modeltype],
params_stubs,
title = None,
txt_fmt = fmt_2, #gen_fmt,
)
#special table
#-------------
#TODO: exists in linear_model, what about other models
#residual diagnostics
#output options
#--------------
#TODO: JP the rest needs to be fixed, similar to summary in linear_model
def ols_printer():
"""
print summary table for ols models
"""
table = str(general_table)+'\n'+str(parameter_table)
return table
def ols_to_csv():
"""
exports ols summary data to csv
"""
pass
def glm_printer():
table = str(general_table)+'\n'+str(parameter_table)
return table
pass
printers = {'OLS': ols_printer,
'GLM' : glm_printer
}
if returns=='print':
try:
return printers[modeltype]()
except KeyError:
return printers['OLS']()
def _getnames(self, yname=None, xname=None):
'''extract names from model or construct names
'''
if yname is None:
if hasattr(self.model, 'endog_names') and (
not self.model.endog_names is None):
yname = self.model.endog_names
else:
yname = 'y'
if xname is None:
if hasattr(self.model, 'exog_names') and (
not self.model.exog_names is None):
xname = self.model.exog_names
else:
xname = ['var_%d' % i for i in range(len(self.params))]
return yname, xname
def summary_top(results, title=None, gleft=None, gright=None, yname=None, xname=None):
'''generate top table(s)
TODO: this still uses predefined model_methods
? allow gleft, gright to be 1 element tuples instead of filling with None?
'''
#change of names ?
gen_left, gen_right = gleft, gright
#time and names are always included
import time
time_now = time.localtime()
time_of_day = [time.strftime("%H:%M:%S", time_now)]
date = time.strftime("%a, %d %b %Y", time_now)
yname, xname = _getnames(results, yname=yname, xname=xname)
#create dictionary with default
#use lambdas because some values raise exception if they are not available
#alternate spellings are commented out to force unique labels
default_items = dict([
('Dependent Variable:', lambda: [yname]),
('Dep. Variable:', lambda: [yname]),
('Model:', lambda: [results.model.__class__.__name__]),
#('Model type:', lambda: [results.model.__class__.__name__]),
('Date:', lambda: [date]),
('Time:', lambda: time_of_day),
('Number of Obs:', lambda: [results.nobs]),
#('No. of Observations:', lambda: ["%#6d" % results.nobs]),
('No. Observations:', lambda: ["%#6d" % results.nobs]),
#('Df model:', lambda: [results.df_model]),
('Df Model:', lambda: ["%#6d" % results.df_model]),
#TODO: check when we have non-integer df
('Df Residuals:', lambda: ["%#6d" % results.df_resid]),
#('Df resid:', lambda: [results.df_resid]),
#('df resid:', lambda: [results.df_resid]), #check capitalization
('Log-Likelihood:', lambda: ["%#8.5g" % results.llf]) #doesn't exist for RLM - exception
#('Method:', lambda: [???]), #no default for this
])
if title is None:
title = results.model.__class__.__name__ + 'Regression Results'
if gen_left is None:
#default: General part of the summary table, Applicable to all? models
gen_left = [('Dep. Variable:', None),
('Model type:', None),
('Date:', None),
('No. Observations:', None),
('Df model:', None),
('Df resid:', None)]
try:
llf = results.llf
gen_left.append(('Log-Likelihood', None))
except: #AttributeError, NotImplementedError
pass
gen_right = []
gen_title = title
gen_header = None
#needed_values = [k for k,v in gleft + gright if v is None] #not used anymore
#replace missing (None) values with default values
gen_left_ = []
for item, value in gen_left:
if value is None:
value = default_items[item]() #let KeyErrors raise exception
gen_left_.append((item, value))
gen_left = gen_left_
if gen_right:
gen_right_ = []
for item, value in gen_right:
if value is None:
value = default_items[item]() #let KeyErrors raise exception
gen_right_.append((item, value))
gen_right = gen_right_
#check
missing_values = [k for k,v in gen_left + gen_right if v is None]
assert missing_values == [], missing_values
#pad both tables to equal number of rows
if gen_right:
if len(gen_right) < len(gen_left):
#fill up with blank lines to same length
gen_right += [(' ', ' ')] * (len(gen_left) - len(gen_right))
elif len(gen_right) > len(gen_left):
#fill up with blank lines to same length, just to keep it symmetric
gen_left += [(' ', ' ')] * (len(gen_right) - len(gen_left))
#padding in SimpleTable doesn't work like I want
#force extra spacing and exact string length in right table
gen_right = [('%-21s' % (' '+k), v) for k,v in gen_right]
gen_stubs_right, gen_data_right = zip_longest(*gen_right) #transpose row col
gen_table_right = SimpleTable(gen_data_right,
gen_header,
gen_stubs_right,
title = gen_title,
txt_fmt = fmt_2cols #gen_fmt
)
else:
gen_table_right = [] #because .extend_right seems works with []
#moved below so that we can pad if needed to match length of gen_right
#transpose rows and columns, `unzip`
gen_stubs_left, gen_data_left = zip_longest(*gen_left) #transpose row col
gen_table_left = SimpleTable(gen_data_left,
gen_header,
gen_stubs_left,
title = gen_title,
txt_fmt = fmt_2cols
)
gen_table_left.extend_right(gen_table_right)
general_table = gen_table_left
return general_table #, gen_table_left, gen_table_right
def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
skip_header=False, title=None):
'''create a summary table for the parameters
Parameters
----------
res : results instance
some required information is directly taken from the result
instance
yname : string or None
optional name for the endogenous variable, default is "y"
xname : list of strings or None
optional names for the exogenous variables, default is "var_xx"
alpha : float
significance level for the confidence intervals
use_t : bool
indicator whether the p-values are based on the Student-t
distribution (if True) or on the normal distribution (if False)
skip_headers : bool
If false (default), then the header row is added. If true, then no
header row is added.
Returns
-------
params_table : SimpleTable instance
'''
#Parameters part of the summary table
#------------------------------------
#Note: this is not necessary since we standardized names, only t versus normal
if isinstance(results, tuple):
#for multivariate endog
#TODO: check whether I don't want to refactor this
#we need to give parameter alpha to conf_int
results, params, std_err, tvalues, pvalues, conf_int = results
else:
params = results.params
std_err = results.bse
tvalues = results.tvalues #is this sometimes called zvalues
pvalues = results.pvalues
conf_int = results.conf_int(alpha)
#Dictionary to store the header names for the parameter part of the
#summary table. look up by modeltype
alp = str((1-alpha)*100)+'%'
if use_t:
param_header = ['coef', 'std err', 't', 'P>|t|',
'[' + alp + ' Conf. Int.]']
else:
param_header = ['coef', 'std err', 'z', 'P>|z|',
'[' + alp + ' Conf. Int.]']
if skip_header:
param_header = None
_, xname = _getnames(results, yname=yname, xname=xname)
params_stubs = xname
exog_idx = lrange(len(xname))
#center confidence intervals if they are unequal lengths
# confint = ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \
# exog_idx]
confint = ["%s %s" % tuple(lmap(forg, conf_int[i])) for i in \
exog_idx]
len_ci = lmap(len, confint)
max_ci = max(len_ci)
min_ci = min(len_ci)
if min_ci < max_ci:
confint = [ci.center(max_ci) for ci in confint]
#explicit f/g formatting, now uses forg, f or g depending on values
# params_data = lzip(["%#6.4g" % (params[i]) for i in exog_idx],
# ["%#6.4f" % (std_err[i]) for i in exog_idx],
# ["%#6.3f" % (tvalues[i]) for i in exog_idx],
# ["%#6.3f" % (pvalues[i]) for i in exog_idx],
# confint
## ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \
## exog_idx]
# )
params_data = lzip([forg(params[i], prec=4) for i in exog_idx],
[forg(std_err[i]) for i in exog_idx],
[forg(tvalues[i]) for i in exog_idx],
["%#6.3f" % (pvalues[i]) for i in exog_idx],
confint
# ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \
# exog_idx]
)
parameter_table = SimpleTable(params_data,
param_header,
params_stubs,
title = title,
txt_fmt = fmt_params #gen_fmt #fmt_2, #gen_fmt,
)
return parameter_table
def summary_params_frame(results, yname=None, xname=None, alpha=.05,
use_t=True):
'''create a summary table for the parameters
Parameters
----------
res : results instance
some required information is directly taken from the result
instance
yname : string or None
optional name for the endogenous variable, default is "y"
xname : list of strings or None
optional names for the exogenous variables, default is "var_xx"
alpha : float
significance level for the confidence intervals
use_t : bool
indicator whether the p-values are based on the Student-t
distribution (if True) or on the normal distribution (if False)
skip_headers : bool
If false (default), then the header row is added. If true, then no
header row is added.
Returns
-------
params_table : SimpleTable instance
'''
#Parameters part of the summary table
#------------------------------------
#Note: this is not necessary since we standardized names, only t versus normal
if isinstance(results, tuple):
#for multivariate endog
#TODO: check whether I don't want to refactor this
#we need to give parameter alpha to conf_int
results, params, std_err, tvalues, pvalues, conf_int = results
else:
params = results.params
std_err = results.bse
tvalues = results.tvalues #is this sometimes called zvalues
pvalues = results.pvalues
conf_int = results.conf_int(alpha)
#Dictionary to store the header names for the parameter part of the
#summary table. look up by modeltype
alp = str((1-alpha)*100)+'%'
if use_t:
param_header = ['coef', 'std err', 't', 'P>|t|',
'Conf. Int. Low', 'Conf. Int. Upp.']
else:
param_header = ['coef', 'std err', 'z', 'P>|z|',
'Conf. Int. Low', 'Conf. Int. Upp.']
_, xname = _getnames(results, yname=yname, xname=xname)
#------------------
from pandas import DataFrame
table = np.column_stack((params, std_err, tvalues, pvalues, conf_int))
return DataFrame(table, columns=param_header, index=xname)
def summary_params_2d(result, extras=None, endog_names=None, exog_names=None,
title=None):
'''create summary table of regression parameters with several equations
This allows interleaving of parameters with bse and/or tvalues
Parameters
----------
result : result instance
the result instance with params and attributes in extras
extras : list of strings
additional attributes to add below a parameter row, e.g. bse or tvalues
endog_names : None or list of strings
names for rows of the parameter array (multivariate endog)
exog_names : None or list of strings
names for columns of the parameter array (exog)
alpha : float
level for confidence intervals, default 0.95
title : None or string
Returns
-------
tables : list of SimpleTable
this contains a list of all seperate Subtables
table_all : SimpleTable
the merged table with results concatenated for each row of the parameter
array
'''
if endog_names is None:
#TODO: note the [1:] is specific to current MNLogit
endog_names = ['endog_%d' % i for i in
np.unique(result.model.endog)[1:]]
if exog_names is None:
exog_names = ['var%d' %i for i in range(len(result.params))]
#TODO: check formatting options with different values
#res_params = [['%10.4f'%item for item in row] for row in result.params]
res_params = [[forg(item, prec=4) for item in row] for row in result.params]
if extras: #not None or non-empty
#maybe this should be a simple triple loop instead of list comprehension?
#below_list = [[['%10s' % ('('+('%10.3f'%v).strip()+')')
extras_list = [[['%10s' % ('(' + forg(v, prec=3).strip() + ')')
for v in col]
for col in getattr(result, what)]
for what in extras
]
data = lzip(res_params, *extras_list)
data = [i for j in data for i in j] #flatten
stubs = lzip(endog_names, *[['']*len(endog_names)]*len(extras))
stubs = [i for j in stubs for i in j] #flatten
#return SimpleTable(data, headers=exog_names, stubs=stubs)
else:
data = res_params
stubs = endog_names
# return SimpleTable(data, headers=exog_names, stubs=stubs,
# data_fmts=['%10.4f'])
import copy
txt_fmt = copy.deepcopy(fmt_params)
txt_fmt.update(dict(data_fmts = ["%s"]*result.params.shape[1]))
return SimpleTable(data, headers=exog_names,
stubs=stubs,
title=title,
# data_fmts = ["%s"]),
txt_fmt = txt_fmt)
def summary_params_2dflat(result, endog_names=None, exog_names=None, alpha=0.05,
use_t=True, keep_headers=True, endog_cols=False):
#skip_headers2=True):
'''summary table for parameters that are 2d, e.g. multi-equation models
Parameters
----------
result : result instance
the result instance with params, bse, tvalues and conf_int
endog_names : None or list of strings
names for rows of the parameter array (multivariate endog)
exog_names : None or list of strings
names for columns of the parameter array (exog)
alpha : float
level for confidence intervals, default 0.95
use_t : bool
indicator whether the p-values are based on the Student-t
distribution (if True) or on the normal distribution (if False)
keep_headers : bool
If true (default), then sub-tables keep their headers. If false, then
only the first headers are kept, the other headerse are blanked out
endog_cols : bool
If false (default) then params and other result statistics have
equations by rows. If true, then equations are assumed to be in columns.
Not implemented yet.
Returns
-------
tables : list of SimpleTable
this contains a list of all seperate Subtables
table_all : SimpleTable
the merged table with results concatenated for each row of the parameter
array
'''
res = result
params = res.params
if params.ndim == 2: # we've got multiple equations
n_equ = params.shape[1]
if not len(endog_names) == params.shape[1]:
raise ValueError('endog_names has wrong length')
else:
if not len(endog_names) == len(params):
raise ValueError('endog_names has wrong length')
n_equ = 1
#VAR doesn't have conf_int
#params = res.params.T # this is a convention for multi-eq models
if not isinstance(endog_names, list):
#this might be specific to multinomial logit type, move?
if endog_names is None:
endog_basename = 'endog'
else:
endog_basename = endog_names
#TODO: note, the [1:] is specific to current MNLogit
endog_names = res.model.endog_names[1:]
#check if we have the right length of names
tables = []
for eq in range(n_equ):
restup = (res, res.params[:,eq], res.bse[:,eq], res.tvalues[:,eq],
res.pvalues[:,eq], res.conf_int(alpha)[eq])
#not used anymore in current version
# if skip_headers2:
# skiph = (row != 0)
# else:
# skiph = False
skiph = False
tble = summary_params(restup, yname=endog_names[eq],
xname=exog_names, alpha=alpha, use_t=use_t,
skip_header=skiph)
tables.append(tble)
#add titles, they will be moved to header lines in table_extend
for i in range(len(endog_names)):
tables[i].title = endog_names[i]
table_all = table_extend(tables, keep_headers=keep_headers)
return tables, table_all
def table_extend(tables, keep_headers=True):
'''extend a list of SimpleTables, adding titles to header of subtables
This function returns the merged table as a deepcopy, in contrast to the
SimpleTable extend method.
Parameters
----------
tables : list of SimpleTable instances
keep_headers : bool
If true, then all headers are kept. If falls, then the headers of
subtables are blanked out.
Returns
-------
table_all : SimpleTable
merged tables as a single SimpleTable instance
'''
from copy import deepcopy
for ii, t in enumerate(tables[:]): #[1:]:
t = deepcopy(t)
#move title to first cell of header
#TODO: check if we have multiline headers
if t[0].datatype == 'header':
t[0][0].data = t.title
t[0][0]._datatype = None
t[0][0].row = t[0][1].row
if not keep_headers and (ii > 0):
for c in t[0][1:]:
c.data = ''
#add separating line and extend tables
if ii == 0:
table_all = t
else:
r1 = table_all[-1]
r1.add_format('txt', row_dec_below='-')
table_all.extend(t)
table_all.title = None
return table_all
def summary_return(tables, return_fmt='text'):
######## Return Summary Tables ########
# join table parts then print
if return_fmt == 'text':
strdrop = lambda x: str(x).rsplit('\n',1)[0]
#convert to string drop last line
return '\n'.join(lmap(strdrop, tables[:-1]) + [str(tables[-1])])
elif return_fmt == 'tables':
return tables
elif return_fmt == 'csv':
return '\n'.join(map(lambda x: x.as_csv(), tables))
elif return_fmt == 'latex':
#TODO: insert \hline after updating SimpleTable
import copy
table = copy.deepcopy(tables[0])
del table[-1]
for part in tables[1:]:
table.extend(part)
return table.as_latex_tabular()
elif return_fmt == 'html':
return "\n".join(table.as_html() for table in tables)
else:
raise ValueError('available output formats are text, csv, latex, html')
[docs]class Summary(object):
'''class to hold tables for result summary presentation
Construction does not take any parameters. Tables and text can be added
with the `add_` methods.
Attributes
----------
tables : list of tables
Contains the list of SimpleTable instances, horizontally concatenated tables are not saved separately.
extra_txt : string
extra lines that are added to the text output, used for warnings and explanations.
'''
def __init__(self):
self.tables = []
self.extra_txt = None
def __str__(self):
return self.as_text()
def __repr__(self):
#return '<' + str(type(self)) + '>\n"""\n' + self.__str__() + '\n"""'
return str(type(self)) + '\n"""\n' + self.__str__() + '\n"""'
def _repr_html_(self):
'''Display as HTML in IPython notebook.'''
return self.as_html()
[docs] def add_table_2cols(self, res, title=None, gleft=None, gright=None,
yname=None, xname=None):
'''add a double table, 2 tables with one column merged horizontally
Parameters
----------
res : results instance
some required information is directly taken from the result
instance
title : string or None
if None, then a default title is used.
gleft : list of tuples
elements for the left table, tuples are (name, value) pairs
If gleft is None, then a default table is created
gright : list of tuples or None
elements for the right table, tuples are (name, value) pairs
yname : string or None
optional name for the endogenous variable, default is "y"
xname : list of strings or None
optional names for the exogenous variables, default is "var_xx"
Returns
-------
None : tables are attached
'''
table = summary_top(res, title=title, gleft=gleft, gright=gright,
yname=yname, xname=xname)
self.tables.append(table)
[docs] def add_table_params(self, res, yname=None, xname=None, alpha=.05,
use_t=True):
'''create and add a table for the parameter estimates
Parameters
----------
res : results instance
some required information is directly taken from the result
instance
yname : string or None
optional name for the endogenous variable, default is "y"
xname : list of strings or None
optional names for the exogenous variables, default is "var_xx"
alpha : float
significance level for the confidence intervals
use_t : bool
indicator whether the p-values are based on the Student-t
distribution (if True) or on the normal distribution (if False)
Returns
-------
None : table is attached
'''
if res.params.ndim == 1:
table = summary_params(res, yname=yname, xname=xname, alpha=alpha,
use_t=use_t)
elif res.params.ndim == 2:
# _, table = summary_params_2dflat(res, yname=yname, xname=xname,
# alpha=alpha, use_t=use_t)
_, table = summary_params_2dflat(res, endog_names=yname,
exog_names=xname,
alpha=alpha, use_t=use_t)
else:
raise ValueError('params has to be 1d or 2d')
self.tables.append(table)
[docs] def as_text(self):
'''return tables as string
Returns
-------
txt : string
summary tables and extra text as one string
'''
txt = summary_return(self.tables, return_fmt='text')
if not self.extra_txt is None:
txt = txt + '\n\n' + self.extra_txt
return txt
[docs] def as_latex(self):
'''return tables as string
Returns
-------
latex : string
summary tables and extra text as string of Latex
Notes
-----
This currently merges tables with different number of columns.
It is recommended to use `as_latex_tabular` directly on the individual
tables.
'''
return summary_return(self.tables, return_fmt='latex')
[docs] def as_csv(self):
'''return tables as string
Returns
-------
csv : string
concatenated summary tables in comma delimited format
'''
return summary_return(self.tables, return_fmt='csv')
[docs] def as_html(self):
'''return tables as string
Returns
-------
html : string
concatenated summary tables in HTML format
'''
return summary_return(self.tables, return_fmt='html')
if __name__ == "__main__":
import statsmodels.api as sm
data = sm.datasets.longley.load()
data.exog = sm.add_constant(data.exog)
res = sm.OLS(data.endog, data.exog).fit()
#summary(
