validation-correction 0.1.0

A package for misclassification error correction in regression using validation data

validation_correction

A Python package for measurement error correction in regression analysis using validation data.

Installation

pip install validation_correction

Usage

The package provides a simple interface for correcting measurement error in both linear and logistic regression using validation data. The correction is implemented using a bootstrap procedure that:

1. Resamples the validation data to estimate misclassification probabilities
2. Applies these probabilities to a bootstrap sample of the research data
3. Repeats this process to obtain valid confidence intervals

Linear Regression with Mismeasured Predictor

import pandas as pd
from validation_correction import validation_correction

# Load your data
research_data = pd.read_csv("research_data.csv")
validation_data = pd.read_csv("validation_data.csv")

# Run corrected regression with bootstrap
# Format: y ~ w || x + z
# where x is the true variable and w is its mismeasured version
result = validation_correction.ols(
    formula="y ~ w || x + z",
    data=research_data,
    val_data=validation_data,
    bootstrap=True,  # Bootstrap is required for correction
    n_boots=1000    # Number of bootstrap iterations
)

# Run naive regression (no correction)
naive_result = validation_correction.ols(
    formula="y ~ w + z",
    data=research_data,
    val_data=None
)

# Print results with bootstrap confidence intervals
print(result)

# Plot coefficient comparison
validation_correction.plot_coefficients(naive_result, result)

# Plot bootstrap distributions
validation_correction.plot_bootstrap_distributions()

Logistic Regression with Mismeasured Predictor

# Format: u||y ~ x + z
# where y is the true variable and u is its mismeasured version
result = validation_correction.logit(
    formula="u||y ~ x + z",
    data=research_data,
    val_data=validation_data,
    bootstrap=True
)

print(result)

Formula Specification

The package uses a special formula syntax to specify the relationship between true and mismeasured variables:

1. For mismeasured predictors:

   • Format: y ~ w || x + z
   • Where x is the true variable and w is its mismeasured version
   • Additional covariates (z) are measured without error

2. For mismeasured outcomes (not yet implemented):

   • Format: u || y ~ x + z
   • Where y is the true outcome and u is its mismeasured version
   • Predictors go on the right side of the ~

3. For naive regression (no correction):

   • Standard formula format: y ~ w + z
   • No || operator needed
   • Set val_data=None

Visualization

The package provides two types of visualizations:

1. Coefficient Comparison Plot:

   validation_correction.plot_coefficients(naive_result, corrected_result)
   

   • Shows point estimates and confidence intervals for both naive and corrected models
   • Useful for comparing the magnitude and direction of bias

2. Bootstrap Distribution Plot:

   validation_correction.plot_bootstrap_distributions()
   

   • Shows the distribution of coefficient estimates from bootstrap samples
   • Includes 95% confidence interval markers
   • Must run regression with bootstrap=True first

Bootstrap confidence intervals:

• Uses percentile method (2.5th and 97.5th percentiles)
• Accessible via result['[0.025]'] and result['[0.975]']
• Number of bootstrap iterations controlled by n_boots parameter
• Bootstrap is required for measurement error correction

Data Requirements

• Main dataset (data): Must contain all variables in the formula
• Validation dataset (val_data): Must contain both the true and mismeasured versions of the relevant variable
• Both datasets should be pandas DataFrames

References

• Estimating and Correcting for Misclassification Error in Empirical Textual Research, by Paul Connell and Jonathan H. Choi available at:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4913179

License

This project is licensed under the MIT License - see the LICENSE file for details.