rMIDAS

Overview

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.

Efficient handling of large data

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)

Background and suggested citation

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

Lall, Ranjit, and Thomas Robinson. 2021. “The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning.” Political Analysis. https://doi.org/10.1017/pan.2020.49.

An ungated version of the paper is available here.

Installation

rMIDAS is now 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. Users must have Python 3.7 or 3.8 installed in order to run MIDAS (3.9 is currently untested), as well as the following dependencies:

• matplotlib
• numpy
• pandas
• scikit-learn
• tensorflow
• tensorflow-addons (for TF2)

rMIDAS will automatically try to set up a Python configuration unless users specify their own version using set_python_env() (examples below). Setting a custom Python install must be performed before training or imputing data occurs:

library(rMIDAS)

# 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 condaenv, where conda can be supplied to choose a specific executable
set_python_env(x = "conda_env", type = "condaenv", conda = "auto")

# 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 condaenv. 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:

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

Vignettes (including simple example)

rMIDAS is packaged with two 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

Contributing to rMIDAS

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 London School of Economics. To apply, please send your CV (or a summary of relevant skills/experience) to R.Lall@lse.ac.uk.

Getting help

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.