- Notifications
You must be signed in to change notification settings - Fork7
young2j/summary3
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
This file mainly modified based onstatsmodels.iolib.summary2. Now you can use the functionsummary_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 fitimport numpy as npimport pandas as pdimport statsmodels.api as smimport statsmodels.formula.api as smf# olsdat = sm.datasets.get_rdataset("Guerry", "HistData").datares_ols = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()#glmdata = 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()# geedata = sm.datasets.get_rdataset('epil', package='MASS').datafam = 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()# logitspector_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 modelfrom linearmodels.datasets import wage_paneldata = wage_panel.load()year = pd.Categorical(data.year)data = data.set_index(['nr', 'year'])data['year'] = yearfrom linearmodels.panel import PooledOLSexog_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 PanelOLSexog_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 FirstDifferenceOLSexog_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 functionsummary_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_colFor 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. Parametermore_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 useregressor_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')