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README

rMIDAS is an R package for accurate and efficient multiple imputation using deep learning methods. The package provides a simplified workflow for imputing and then analyzing data:

• convert() carries out all necessary preprocessing steps
• train() constructs and trains a MIDAS imputation model
• complete() generates multiple completed datasets from the trained model
• combine() runs regression analysis across the complete data, following Rubin’s combination rules

rMIDAS is based on the Python package MIDASpy.

rMIDAS also incorporates several features to streamline and improve the the efficiency of multiple imputation analysis:

• Optimisation for large datasets using data.table and mltools packages
• Automatic reversing of all pre-processing steps prior to analysis
• Built-in regression function based on glm (applying Rubin’s combination rules)

For more information on MIDAS, the method underlying the software, see:

Lall, Ranjit, and Thomas Robinson. 2022. “The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning.” Political Analysis 30, no. 2: 179-196. Published version. Accepted version.

Lall, Ranjit, and Thomas Robinson. 2023. “Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS.” Journal of Statistical Software. Accepted version (in press).

rMIDAS is available on CRAN. To install the package in R, you can use the following code:

install.packages("rMIDAS")

To install the latest development version, use the following code:

# install.packages("devtools")
devtools::install_github("MIDASverse/rMIDAS")

Note that rMIDAS uses the reticulate package to interface with Python. When the package is first loaded, it will prompt the user on whether to set up a Python environment and its dependencies automatically. Users that choose to set up the environment and dependencies manually, or who use rMIDAS in headless mode can specify a Python binary using set_python_env() (examples below). Currently, Python versions from 3.6 to 3.10 are supported. For a custom Python environment the following dependencies are also required:

• matplotlib
• numpy
• pandas
• scikit-learn
• scipy
• statsmodels
• tensorflow (<2.12.0)
• tensorflow-addons (<0.20.0)

Setting a custom Python install must be performed before training or imputing data occurs. To manually set up a Python environment:

library(rMIDAS)
# Decline the automatic setup

# Point to a Python binary
set_python_env(x = "path/to/python/binary")

# Or point to a virtualenv binary
set_python_env(x = "virtual_env", type = "virtualenv")

# Or point to a conda environment
set_python_env(x = "conda_env", type = "conda")

# Now run rMIDAS::train() and rMIDAS::complete()...

You can also download the rmidas-env.yml conda environment file from this repository to set up all dependencies in a new conda environment. To do so, download the .yml file, navigate to the download directory in your console and run:

conda env create -f rmidas-env.yml

Then, prior to training a MIDAS model, make sure to load this environment in R:

# First load the rMIDAS package
library(rMIDAS)
# Decline the automatic setup

set_python_env(x = "rmidas", type = "conda")

Note: reticulate only allows you to set a Python binary once per R session, so if you wish to switch to a different Python binary, or have already run train() or convert(), you will need to restart or terminate R prior to using set_python_env().

rMIDAS is packaged with three vignettes:

1. vignette("imputation_demo", "rMIDAS") demonstrates the basic workflow and capacities of rMIDAS
2. vignette("custom_python_versions", "rMIDAS") provides detailed guidance on configuring Python binaries and environments, including some troubleshooting tips
3. vignette("use_server", "rMIDAS") provides guidance for running rMIDAS in headless mode

An additional example that showcases rMIDAS core functionalities can be found here.

Interested in contributing to rMIDAS? We are looking to hire a research assistant to work part-time (flexibly) to help us build out new features and integrate our software with existing machine learning pipelines. You would be paid the standard research assistant rate at the University of Oxford. To apply, please send your CV (or a summary of relevant skills/experience) to ranjit.lall@sjc.ox.ac.uk.

rMIDAS is still in development, and we may not have caught all bugs. If you come across any difficulties, or have any suggestions for improvements, please raise an issue here.

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