Multiple Imputation of Missing Values in R

wrangling
modeling

How to use R package ‘mice’ to solve missing data problems by multiple imputation

Author

Stef van Buuren

Published

April 19, 2022

In this workshop we laerned why and how you can use the R package mice to solve missing data problems by means of multiple imputation. Multiple imputation is a generic approach to analyse incomplete data that reduces bias and increases precision.

To get an idea of what the workshop was about, you could read (in Dutch) this short and accessible overview of multiple imputation. Alternatively, you could check out the (English) Wikipedia page.

To install the R package mice , copy and paste the next line into your R Studio Editor and run it:

install.packages(“mice”)

Join this workshop meeting on Zoom by clicking this link

Stef van Buuren is Professor of Statistical Analysis of Incomplete Data at the University of Utrecht and Principal Scientist at the Netherlands Organisation for Applied Scientific Research TNO in Leiden. He invented the MICE algorithm for multiple imputation of missing data. Here you can find more details about Stef and his work.

Materials

The slides that accompanied Stef’s presentation.

Contact

Stef van Buuren

Citation

For attribution, please cite this work as (Buuren 2022)

References

Buuren, S. van. 2022. NSC-R Workshops: Multiple Imputation of Missing Values in R.” NSCR. https://nscrweb.netlify.app/posts/2022-04-28-multiple-imputation-in-r/.