crunchers.statsmodels_helpers package

Submodules

crunchers.statsmodels_helpers.lazy_stats module

Functions for streamlining analysis.

crunchers.statsmodels_helpers.lazy_stats.build_regression_models_grid(X_hyps_dicts, ctrl_coefs_dicts, outcomes_dicts)

crunchers.statsmodels_helpers.lazy_stats.compare_coefs(row, value, results)

crunchers.statsmodels_helpers.lazy_stats.do_regression(data, y_var, X_ctrls=None, X_hyp=None, kind='OLS', **kwargs)

Provide a further abstracted way to build and run multiple types of regressions.

data (pd.DataFrame): data table to use when retrieving the column headers y_var (str): column header of the outcome variable X_ctrls (str): formula specification of the “boring” variables “column_header_1 + column_header_2”… X_hyp (str): formula specification of the “interesting” variables “column_header_1 + column_header_2”… kind (str): the type of regression to run kind in [‘GLM’,’OLS’,’RLM’] == True

crunchers.statsmodels_helpers.lazy_stats.format_all_regression_models(regs, total)

Return tuple of string formated versions of all regression tables in the regs object.

Parameters:

• (reg-tree (regs) – dict-like): tree-like dict containing the regression results objects as leaves and descriptors as nodes.
• total (int) – total number of results tables to format.

Returns:

tuple

crunchers.statsmodels_helpers.lazy_stats.get_diff(a, b)

crunchers.statsmodels_helpers.lazy_stats.get_log2_fold(a, b)

crunchers.statsmodels_helpers.lazy_stats.identify_full_ctrl_names(X_vars, orig_ctrl_names)

Return set of variable names actually used in regression, tolerating mangling of categoricals.

crunchers.statsmodels_helpers.lazy_stats.regression_grid_single(grid_item, data, kind, **kwargs)

crunchers.statsmodels_helpers.lazy_stats.report_glm(formula, data, verbose=True, **kwargs)

Fit GLM, print a report, and return the fit object.

crunchers.statsmodels_helpers.lazy_stats.report_logitreg(formula, data, verbose=True, disp=1)

Fit logistic regression, print a report, and return the fit object.

crunchers.statsmodels_helpers.lazy_stats.report_ols(formula, data, fit_regularized=False, L1_wt=1, refit=False, **kwargs)

Fit OLS regression, print a report, and return the fit object.

crunchers.statsmodels_helpers.lazy_stats.report_rlm(formula, data, verbose=True, **kwargs)

Fit RLM, print a report, and return the fit object.

crunchers.statsmodels_helpers.lazy_stats.run_regressions_grid(grid, data, kind, max_workers=None, **kwargs)

crunchers.statsmodels_helpers.lazy_stats.summarize_X_vars(results, sig_thresh=0.05, X_ctrls=None, X_ignore=None)

crunchers.statsmodels_helpers.lazy_stats.summarize_grid_OLS(regs, reg_grid)

crunchers.statsmodels_helpers.lazy_stats.summarize_grid_X_vars_OLS(regs, reg_grid, sig_thresh=0.05)

crunchers.statsmodels_helpers.lazy_stats.summarize_multi_LOGIT(results)

Return dataframe aggregating over-all stats from a dictionary-like object containing LOGIT result objects.

crunchers.statsmodels_helpers.lazy_stats.summarize_multi_OLS(results)

Return dataframe aggregating over-all stats from a dictionary-like object containing OLS result objects.

crunchers.statsmodels_helpers.lazy_stats.summarize_single_OLS(regression, col_dict, name, is_regularized=False)

Return dataframe aggregating over-all stats from a dictionary-like object containing OLS result objects.

crunchers.statsmodels_helpers.lazy_stats.tree()

Module contents