Description:
poweranalysis 0.1.5
power-analysis 💪🔍
power-analysis is a Python package for performing power analysis and calculating sample sizes for statistical models. The package provides classes for defining statistical models, performing power analysis, calculating sample sizes for two-sample t-tests, and conducting power analysis on observational panel data using a clustered bootstrap method.
Installation 📥
You can install the power_analysis package using pip:
pip install power-analysis
Usage 🧑💻
Panel Data Power Analysis
To use the PanelBootstrap class for power analysis on observational panel data, you can follow these steps:
Import the required packages and classes:
import numpy as np
import pandas as pd
from power_analysis import PanelBootstrap
Load your panel data into a pandas DataFrame:
data = pd.read_csv('your_data.csv')
Create a PanelBootstrap object with the required parameters:
power_analysis = PanelBootstrap(data, outcome_var='outcome', treatment_var='treatment', individual_var='individual', random_seed=42)
Use the fit_model method to fit a linear regression model and obtain the treatment effect and p-value:
treatment_effect, p_value = power_analysis.fit_model(data)
Perform a clustered bootstrap analysis to obtain the mean effect size, standard deviation of effect sizes, and a list of p-values:
mean_effect_size, std_effect_size, p_values = power_analysis.clustered_bootstrap(n_bootstrap=1000)
Calculate the statistical power when varying the number of observations (N) or effect sizes:
n_values = [50, 100, 150, 200, 250, 300]
alpha = 0.05
n_bootstrap = 1000
power_by_n = power_analysis.calculate_power_by_n(n_values, alpha, n_bootstrap)
power_by_effect_size = power_analysis.calculate_power_by_effect_size(effect_sizes, alpha, n_bootstrap)
Contributing 🤝
Contributions to power-analysis are welcome! If you find a bug or would like to suggest a new feature, please open an issue on GitHub.
License 📜
power-analysis is licensed under the MIT license. See LICENSE for more information.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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