R: Regression With Multiple Imputation (missing data handling)

Автор: Regorz Statistik

Загружено: 2022-11-29

Просмотров: 18064

Описание:

How best to treat missing data in linear regression analysis? The current view is that multiple imputation by chained equations (mice) is one of the best ways for missing data handling in regression. This multiple imputation tutorial is going to show you how to use the mice package in R to analyze datasets with missing data (MCAR, MAR) in a regression framework.

Here is a current journal article giving theoretical background and specific recommendations regarding the use of multiple imputation for missing data:
Austin, P. C., White, I. R., Lee, D. S., & van Buuren, S. (2020). Missing data in clinical research: a tutorial on multiple imputation. Canadian Journal of Cardiology.
https://www.sciencedirect.com/science...

Companion webpage with the R code:
https://www.regorz-statistik.de/en/r_...

Tutorial for checking regression assumptions with multiple imputation:
• Multiple Imputation and Checking Regressio...

R: Regression With Multiple Imputation (missing data handling)

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