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index.Rmd
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---
output: rmarkdown::html_vignette
bibliography: refs.bibtex
---
# [MICE: Multivariate Imputation by Chained Equations](http://stefvanbuuren.github.io/mice/)
## Software
1. [`mice` package at CRAN](https://cran.r-project.org/package=mice)
2. [mice GitHUB repository](https://github.com/stefvanbuuren/mice)
## Installation
The `mice` package can be installed from CRAN as follows:
```{r eval = FALSE}
install.packages("mice")
```
The latest version is can be installed from GitHub as follows:
```{r eval = FALSE}
install.packages("devtools")
devtools::install_github(repo = "stefvanbuuren/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`](https://stefvanbuuren.github.io/Winnipeg/)
2. [Statistical Methods for combined data sets](https://stefvanbuuren.github.io/RECAPworkshop/)
## Vignettes
1. [Ad hoc methods and the MICE algorithm](https://gerkovink.github.io/miceVignettes/Ad_hoc_and_mice/Ad_hoc_methods.html)
2. [Convergence and pooling](https://gerkovink.github.io/miceVignettes/Convergence_pooling/Convergence_and_pooling.html)
3. [Inspecting how the observed data and missingness are related](https://gerkovink.github.io/miceVignettes/Missingness_inspection/Missingness_inspection.html)
4. [Passive imputation and post-processing](https://gerkovink.github.io/miceVignettes/Passive_Post_processing/Passive_imputation_post_processing.html)
5. [Imputing multilevel data](https://gerkovink.github.io/miceVignettes/Multi_level/Multi_level_data.html)
6. [Sensitivity analysis with `mice`](https://gerkovink.github.io/miceVignettes/Sensitivity_analysis/Sensitivity_analysis.html)
7. [Generate missing values with `ampute`](https://stefvanbuuren.github.io/mice/ampute.html)
## Code from publications
1. [Flexible Imputation of Missing Data](http://stefvanbuuren.github.io/mice/FIMD.html)
2. [mice: Multivariate Imputation by Chained Equations in R](http://stefvanbuuren.github.io/mice/JSS.html)
## Related packages
Packages that extend the functionality of `mice` include:
1. [`ImputeRobust`: Multiple Imputation with `GAMLSS`](https://github.com/dsalfran/ImputeRobust)
2. [`countimp`: Incomplete count data](https://github.com/kkleinke/countimp)
3. [`miceadds`: Functions for multilevel imputation](https://cran.r-project.org/package=miceadds)
4. [`micemd`: Functions for multilevel imputation](https://cran.r-project.org/package=micemd)
5. [`CALIBERrfimpute`: Another random forest method](https://cran.r-project.org/package=CALIBERrfimpute)
6. [`smcfcs`: Addressing incompatibility in selected models](https://github.com/jwb133/smcfcs)
7. [`parlMICE`: Parallel MICE imputation wrapper](https://gerkovink.github.io/parlMICE/Vignette_parlMICE.html)
8. [`fancyimpyute`: MICE in Python for ordinal data](https://github.com/hammerlab/fancyimpute)
## Further reading
1. [Article](https://www.jstatsoft.org/v45/i03/paper) in the Journal of Statistical Software [@VANBUUREN2011].
2. The first application on [missing blood pressure](http://www.stefvanbuuren.nl/publications/Multiple%20imputation%20-%20Stat%20Med%201999.pdf) data [@VANBUUREN1999].
3. Term [Fully Conditional Specification](http://www.stefvanbuuren.nl/publications/FCS%20in%20multivariate%20imputation%20-%20JSCS%202006.pdf) describes a general class of methods that specify imputations model for multivariate data as a set of conditional distributions [@VANBUUREN2006].
4. Details about imputing [mixes of numerical and categorical data](http://www.stefvanbuuren.nl/publications/MI%20by%20FCS%20-%20SMMR%202007.pdf) can be found in [@VANBUUREN2007].
5. [Wulff and Ejlskov](http://www.ejbrm.com/issue/download.html?idArticle=450 ) provide a comprehensive overview of MICE.
6. Many more details and applications can be found in the book *Flexible Imputation of Missing Data. Second Edition* [@VANBUUREN2018].
## References