README.Rmd

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# MICE: Multivariate Imputation by Chained Equations

The `mice` package implements a method to deal with missing data. The package 
creates multiple imputations (replacement values) for multivariate missing data.
The method is based on Fully Conditional Specification, where 
each incomplete variable is imputed by a separate model. The `MICE` algorithm
can impute mixes of continuous, binary, unordered categorical and ordered
categorical data. In addition, MICE can impute continuous two-level data, and 
maintain consistency between imputations by means of passive imputation. Many 
diagnostic plots are implemented to inspect the quality of the imputations.

## 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")
```


## Overview

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

## 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

## miceVignettes

A detailed series of vignettes that walk you through solving realistic inference problems with mice. 

We suggest going through these vignettes in the following order

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)
5. [Sensitivity analysis with `mice`](https://gerkovink.github.io/miceVignettes/Sensitivity_analysis/Sensitivity_analysis.html)

## Further reading

The `mice` software was published in the [Journal of Statistical Software](https://www.jstatsoft.org/article/view/v045i03) [@vanbuuren2011a]. The first application of the method concerned missing blood pressure data [@vanbuuren1999]. The term *Fully Conditional Specification* was introduced in 2006 to describe a general class of methods that specify imputations model for multivariate data as a set of conditional distributions [@vanbuuren2006]. Further details and applications can be found in the book *Flexible Imputation of Missing Data* [@vanbuuren2012].


## References