Skip to content



combine_sector_results(result_dict:dict = None, write_path:str = None, significance_stars: bool = False, round_results: int = None, latex_syntax: bool = True)


Extract key result fields (coefficients, standard errors, and p-values) and combine them in a DataFrame. Has the option to write the data to a .csv file with or without extra value formatting options.


result_dict: Dict[statsmodels.genmod.generalized_linear_model.GLMResultsWrapper]
  A dictionary of GLM fit objects as returned by gme.EsimationModel.estimate()

write_path: (optional) str
  A system location and file name in which to write a csv file containing the combined results.

significance_stars: bool
  If true, combined results are output with significance stars. <0.01, <0.05, and <0.10. Default
  is False.

round_results: (optional) int
  Rounds combined results to the desired decimal place.

latex_syntax: bool
  If True, reports aspects of results, such as significance stars, using standard latex syntax.


Returns: Pandas.DataFrame
  A DataFrame containing combined GLM results for all results in the supplied dictionary.


# Using a gme.EstimationModel named 'sample_model'.
>>> sample_results = sample_model.estimate()

# Return a dataframe of results
>>> result_df = combine_sector_results(sample_results)
>>> result_df.head(5)
                          all_coeff     all_pvalue   all_stderr
log_distance              -0.739840  9.318804e-211     0.023879
agree_pta                  0.334219   5.134355e-15     0.042719
common_language            0.128770   1.076932e-03     0.039383
contiguity                 0.255161   5.857612e-08     0.047050
importer_year_fe_ARG2013  26.980367   0.000000e+00     0.361228

# Export table as a .csv file
>>> combine_sector_results(sample_results,
...                        round_results = 3, 
...                        path = 'c:\\Documents\\combined_results_saved.csv')