RIMWeightingPandas is a Python implementation of the Random Iterative Method (RIM) weighting algorithm, as described in the paper:

• Deming, W. E., & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Annals of Mathematical Statistics, 11, 427-444.

This method is used to adjust the weights of survey data so that they align with known marginal totals, helping to improve the accuracy of weighted statistical analysis.

• Adjusts survey data to match known target distributions (marginal totals).
• Implements the Random Iterative Method (RIM) as described by Deming and Stephan.
• Provides convergence checks to ensure that weights stabilize within specified bounds.
• Includes functionality to handle pre-existing weights and apply iterative adjustments.

First, import the class and prepare your dataset.

import pandas as pd
from RIMWeightingPandas import RIMWeightingPandas

# Example DataFrame (survey data)
data = pd.DataFrame({
    'age_group': ['18-25', '26-35', '36-45', '46-60', '60+'],
    'gender': ['M', 'F', 'M', 'F', 'M'],
    'weight': [1, 1, 1, 1, 1]  # Pre-existing weights (optional)
})

# Define the specification (target proportions for each category)
spec = {
    'age_group': {'18-25': 0.2, '26-35': 0.2, '36-45': 0.2, '46-60': 0.2, '60+': 0.2},
    'gender': {'M': 0.5, 'F': 0.5}
}

# Initialize the RIMWeightingPandas object
rim = RIMWeightingPandas(data, spec, pre_weight='weight')

# Apply RIM weighting
weighted_data = rim.apply_weights()

• __init__(data, spec, pre_weight=None, tolerance=0.001, weight_col_name='rim_weight'): Initializes the RIMWeightingPandas object.

  • data: The survey data as a Pandas DataFrame.
  • spec: A dictionary of target proportions for each category (marginal totals).
  • pre_weight: Optional column name containing existing weights in the data. Defaults to None.
  • tolerance: Convergence threshold. Defaults to 0.001.
  • weight_col_name: The name of the weight column in the DataFrame. Defaults to 'rim_weight'.

• apply_weights(max_iterations=30, min_weight=0.5, max_weight=1.5): Applies the RIM weighting algorithm.

  • max_iterations: Maximum number of iterations allowed.
  • min_weight: Minimum allowable weight for each observation. Adjusted weights that fall below this value are clipped.
  • max_weight: Maximum allowable weight for each observation. Adjusted weights that exceed this value are clipped.
  • Returns: The DataFrame with the adjusted rim_weight column.

• weighting_efficiency(): Computes the RIM weighting efficiency as a percentage, based on the formula:

  $$ \text{Efficiency (%)} = \frac{\left( \sum (P_j \times R_j) \right)^2 }{\sum P_j \times \sum P_j \times (R_j^2) } $$

  where $P_j$ are the pre-weights and $R_j$ are the adjusted rim weights.

• generate_summary(): Generates a summary of the unweighted and weighted counts per variable, including:

  • Unweighted counts & percentages.
  • Weighted counts & percentages.
  • Min/Max weights per category.

• This code is written by ChatGPT o3-mini-high based on RIMWeightingPandas
• This code is untested

After applying the RIM weighting, you can generate a summary of your data:

rim.generate_summary()

To install, use the following command:

pip install git+https://github.com/mohiteamit/rim-weighting.git

Alternatively, you can clone the repository and install it manually:

git clone https://github.com/mohiteamit/rim-weighting.git
cd rim-weighting
pip install .

This will display a formatted summary showing the unweighted and weighted counts for each variable.

I welcome contributions! If you'd like to help improve this project, feel free to fork the repository and submit a pull request.

This project is licensed under the MIT License. You can freely use, modify, and distribute this code. However, you must provide appropriate credit by mentioning this repository in any usage of the code. For more details, see the LICENSE file.