Overview

2021-02-01

Software

1. mice package at CRAN
2. mice GitHUB repository

Installation

The mice package can be installed from CRAN as follows:

install.packages("mice")

The latest version is can be installed from GitHub as follows:

install.packages("devtools")
devtools::install_github("amices/mice")

Capabilities of mice package

The mice package contains functions to

• Inspect the missing data pattern
• Impute the missing data \(m\) times, resulting in \(m\) completed data sets
• Diagnose the quality of the imputed values
• Analyze each completed data set
• Pool the results of the repeated analyses
• Store and export the imputed data in various formats
• Generate simulated incomplete data
• Incorporate custom imputation methods
• Choose which cells to impute

Main functions

The main functions in the mice package are:

Function name	Description
mice()	Impute the missing data \(m\) times
with()	Analyze completed data sets
pool()	Combine parameter estimates
complete()	Export imputed data
ampute()	Generate missing data

Course materials

1. Handling Missing Data in R with mice
2. Statistical Methods for combined data sets

Vignettes

1. Ad hoc methods and the MICE algorithm
2. Convergence and pooling
3. Inspecting how the observed data and missingness are related
4. Passive imputation and post-processing
5. Combining inferences
6. Imputing multilevel data
7. Sensitivity analysis with mice
8. Generate missing values with ampute
9. parlMICE: Parallel MICE imputation wrapper

Related packages

Packages that extend the functionality of mice include:

1. ImputeRobust: Multiple Imputation with GAMLSS
2. countimp: Incomplete count data
3. miceadds: Functions for multilevel imputation
4. micemd: Functions for multilevel imputation
5. smcfcs: Addressing incompatibility in selected models
6. fancyimpyute: MICE in Python for ordinal data

Further reading

1. mice: Multivariate Imputation by Chained Equations in R in the Journal of Statistical Software (Buuren and Groothuis-Oudshoorn 2011).
2. The first application on missing blood pressure data (Buuren, Boshuizen, and Knook 1999).
3. Term Fully Conditional Specification describes a general class of methods that specify imputations model for multivariate data as a set of conditional distributions (Buuren et al. 2006).
4. Details about imputing mixes of numerical and categorical data can be found in (Buuren 2007).
5. Book Flexible Imputation of Missing Data. Second Edition (Buuren 2018).

Code from publications

1. R code from Flexible Imputation of Missing Data. Second Edition
2. R code from mice: Multivariate Imputation by Chained Equations in R

References

Buuren, S. van. 2007. “Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification.” Statistical Methods in Medical Research 16 (3): 219–42.

———. 2018. Flexible Imputation of Missing Data. Second Edition. Boca Raton, FL: Chapman & Hall/CRC Press.

Buuren, S. van, H. C. Boshuizen, and D. L. Knook. 1999. “Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis.” Statistics in Medicine 18 (6): 681–94.

Buuren, S. van, J. P. L. Brand, C. G. M. Groothuis-Oudshoorn, and D. B. Rubin. 2006. “Fully Conditional Specification in Multivariate Imputation.” Journal of Statistical Computation and Simulation 76 (12): 1049–64.

Buuren, S. van, and K. Groothuis-Oudshoorn. 2011. “MICE: Multivariate Imputation by Chained Equations in r.” Journal of Statistical Software 45 (3): 1–67.