summary3
This file mainly modified based on statsmodels.iolib.summary2. Now you can use the function summary_col()
to output the results of multiple models with stars and export them as a excel/csv file.
Next show some examples including OLS,GLM,GEE,LOGIT and Panel regression results.Other models do not test yet. But what can be determined is that multi-equation models like VAR model does not work here.
# Load the data and fit
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
# ols
dat = sm.datasets.get_rdataset("Guerry", "HistData").data
res_ols = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()
#glm
data = sm.datasets.scotland.load()
data.exog = sm.add_constant(data.exog)
gamma_model = sm.GLM(data.endog, data.exog, family=sm.families.Gamma())
res_glm = gamma_model.fit()
# gee
data = sm.datasets.get_rdataset('epil', package='MASS').data
fam = sm.families.Poisson()
ind = sm.cov_struct.Exchangeable()
mod = smf.gee("y ~ age + trt + base", "subject", data, cov_struct=ind, family=fam)
res_gee = mod.fit()
# logit
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog)
logit_mod = sm.Logit(spector_data.endog, spector_data.exog)
res_logit = logit_mod.fit()
# load panel data and fit the model
from linearmodels.datasets import wage_panel
data = wage_panel.load()
year = pd.Categorical(data.year)
data = data.set_index(['nr', 'year'])
data['year'] = year
from linearmodels.panel import PooledOLS
exog_vars = ['black','hisp','exper','expersq','married', 'educ', 'union', 'year']
exog = sm.add_constant(data[exog_vars])
mod = PooledOLS(data.lwage, exog)
res_pooled = mod.fit()
from linearmodels.panel import PanelOLS
exog_vars = ['expersq','union','married']
exog = sm.add_constant(data[exog_vars])
mod = PanelOLS(data.lwage, exog, entity_effects=True, time_effects=True)
res_fe_re = mod.fit()
from linearmodels.panel import FirstDifferenceOLS
exog_vars = ['exper','expersq', 'union', 'married']
exog = data[exog_vars]
mod = FirstDifferenceOLS(data.lwage, exog)
res_fd = mod.fit()
exog_vars = ['black','hisp','exper','expersq','married', 'educ', 'union']
exog = sm.add_constant(data[exog_vars])
mod = PooledOLS(data.lwage, exog)
res_robust = mod.fit(cov_type='robust')
res_clust_entity = mod.fit(cov_type='clustered', cluster_entity=True)
res_clust_entity_time = mod.fit(cov_type='clustered', cluster_entity=True, cluster_time=True)
Then we import the function summary_col() from the modified summary2 that I named summary3 as a module.Thus we can directly output the concatenated results with stars and some default model informations.
from summary3 import summary_col
For single regression result,we can directly pass the result object,surely a list is better:
# summary_col(res_ols)
summary_col([res_ols])
This will return the Summary class instance, in Notebook the output is:
We can also use print function to output as text. Parameter more_info will add new model information to print.
For example,
print(summary_col([res_ols,res_glm,res_gee,res_logit],more_info=['df_model','scale']))
The incompete output is
We can also use regressor_order to designate the order of variables,show to display the anyone of pvalues,
tvalues or std.err you want,title to define a custom title for your table.
print(sumary_col([res_fe_re,res_fd,res_robust,res_clust_entity,res_clust_entity_time],
regressor_order=['black'],show='se',title='Panel Results Summary Table'))
The output is
Finally,if you want to export the summary results to external files,you can do like this
summary_col([res_glm,res_logit]).to_excel()
Above will obtain a excel file in your working directory named 'summary_results'. Of course you can define the filename and path just like use pandas(actually indeed so) .
summary_col([res_clust_entity,res_fd]).to_csv('your path\\filename.csv')