Imputepmmidas documentation

Implementation of PMM and Variations

This repository contains a modular Python framework for multiple imputation, developed as part of my master’s thesis at Ludwig Maximilian University of Munich for the Statistics Department. The focus lies on implementing and evaluating Predictive Mean Matching (PMM) and its recent extension, the midastouch algorithm.

Thesis Abstract

This thesis develops a modular Python framework for multiple imputation inspired by the R package mice, with a particular focus on Predictive Mean Matching (PMM) and the midastouch algorithm. The implementation allows for full flexibility in defining:

  • Distance metrics

  • Donor selection rules

  • Imputation parameters

To evaluate both imputation strategies, a comprehensive simulation study was conducted across three types of target variables:

  • Continuous

  • Semi-continuous

  • Discrete

Each simulation varied by:

  • Missingness mechanism: MCAR (Missing Completely At Random), left-tailed MAR, and right-tailed MAR

  • Proportion of missing data

  • Imputation configuration

In total, 675 configurations were simulated and evaluated using four key performance metrics:

  • Bias

  • Confidence interval coverage

  • Confidence interval width

  • Mean squared error (MSE)

Simulation results are available as a CSV file under simulations/simulation_df.csv.

Key Findings

  • PMM performs reliably under MCAR and mild MAR, particularly with symmetric distributions and large samples.

  • PMM struggles under skewed distributions or structured missingness, often yielding biased estimates and reduced coverage.

  • midastouch consistently matches or outperforms PMM in coverage and standard error estimation, especially under skewness or small sample sizes.

  • Unlike PMM, midastouch requires no manual tuning of donor size k and, when combined with HowManyImputations (HMI), provides an efficient and automated solution.

Getting Started

Installation

Clone this repository:

git clone https://github.com/Theanh2/Implementation_of_PMM_by_MICE.git@detached
cd Implementation_of_PMM_by_MICE

Or directly install via pip:

pip install git+https://github.com/Theanh2/Implementation_of_PMM_by_MICE.git@detached

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