regtabletotext 0.0.5

Helpers to print regression output as well-formated text

regtabletotext: helpers to print regression output as text

What is regtabletotext?

This package is a collection of helper functions to print regression output of the Python packages statsmodels and linearmodels as text strings. The helpers are particularly useful for users who want to render regression output in Quarto to HTML and PDF.

If you want to export your results to LaTeX or HTML, please check out stargazer.

How to install regtabletotext?

The package is available on pypi.org/project/regtabletotext:

pip install regtabletotext

How to use regtabletotext?

For statsmodels regression output

The following code chunk

import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
from regtabletotext import prettify_result

df = sm.datasets.get_rdataset("Guerry", "HistData").data
df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()

mod = smf.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)
res = mod.fit()

prettify_result(res)

returns this text

Model:
Lottery ~ Literacy + Wealth + Region

Coefficients:
             Estimate  Std. Error  t-Statistic  p-Value

Intercept      38.652       9.456        4.087    0.000
Region[T.E]   -15.428       9.727       -1.586    0.117
Region[T.N]   -10.017       9.260       -1.082    0.283
Region[T.S]    -4.548       7.279       -0.625    0.534
Region[T.W]   -10.091       7.196       -1.402    0.165
Literacy       -0.186       0.210       -0.886    0.378
Wealth          0.452       0.103        4.390    0.000

Summary statistics:
- Number of observations: 85
- Multiple R-squared: 0.338, Adjusted R-squared: 0.287
- F-statistic: 6.636 on 6 and 78 DF, p-value: 0.000

For linearmodels regression output

This code chunk

import pandas as pd
import numpy as np
from linearmodels import PanelOLS
from regtabletotext import prettify_result

np.random.seed(42)
n_entities = 100
n_time_periods = 5

data = {
    'entity': np.repeat(np.arange(n_entities), n_time_periods),
    'time': np.tile(np.arange(n_time_periods), n_entities),
    'y': np.random.randn(n_entities * n_time_periods),
    'x1': np.random.randn(n_entities * n_time_periods),
    'x2': np.random.randn(n_entities * n_time_periods)
}

df = pd.DataFrame(data)
df.set_index(['entity', 'time'], inplace=True)

formula = 'y ~ x1 + x2 + EntityEffects + TimeEffects'
model = PanelOLS.from_formula(formula, df)
result = model.fit()

prettify_result(result)

produces this output

Model:
y ~ x1 + x2 + EntityEffects + TimeEffects

Coefficients:
    Estimate  Std. Error  t-Statistic  p-Value

x1    -0.058       0.052       -1.122    0.263
x2    -0.041       0.050       -0.804    0.422

Summary statistics:
- Number of observations: 500
- Overall R-squared: 0.008, Within R-squared: 0.006
- F-statistic: 1.048, p-value: 0.351

Version 0.0.5

• Replaced explicit options by flexible **options parameter
• Supported classes: statsmodels.regression.linear_model.RegressionResultsWrapper and linearmodels.panel.result.PanelEffectsresult
• Introduced ALLOWED_OPTIONS global

Version 0.0.4

• First working version with statsmodels support