Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science —multiple imputation— fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise.
Flexible Imputation of Missing Data is supported by many examples using real data. It provides detailed guidance of implementation in R throughout the book. The tables below contains the R code used in the book, as well as the output and graphs it produces using mice 2.12.
Flexible Imputation of Missing Data - Online materials