mice

Multivariate Imputation by Chained Equations

https://github.com/amices/mice

Science Score: 36.0%

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Low similarity (16.5%) to scientific vocabulary

Keywords

chained-equations fcs imputation mice missing-data missing-values multiple-imputation multivariate-data

Keywords from Contributors

latex date-time tidy-data rmarkdown visualisation codecov coverage coverage-report travis-ci package-creation

Last synced: 6 months ago · JSON representation

Repository

Multivariate Imputation by Chained Equations

Basic Info

Host: GitHub
Owner: amices
License: gpl-2.0
Language: R
Default Branch: master
Homepage: https://amices.org/mice/
Size: 164 MB

Statistics

Stars: 476
Watchers: 19
Forks: 116
Open Issues: 30
Releases: 0

Topics

chained-equations fcs imputation mice missing-data missing-values multiple-imputation multivariate-data

Created almost 13 years ago · Last pushed 9 months ago

Metadata Files

Readme Changelog License Code of conduct

README.Rmd

---
output:
  md_document:
    variant: gfm
bibliography: refs.bibtex
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)
options(width = 60, digits = 3)
set.seed(1)
```

# mice 


[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/mice)](https://cran.r-project.org/package=mice)
[![](https://cranlogs.r-pkg.org/badges/mice)](https://cran.r-project.org/package=mice)
[![R-CMD-check](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml)
[![](https://img.shields.io/badge/github%20version-3.18.0-orange.svg)](https://amices.org/mice/)


## [Multivariate Imputation by Chained Equations](https://amices.org/mice/)

The [`mice`](https://cran.r-project.org/package=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 can be installed from GitHub as follows: 

```{r eval = FALSE}
install.packages("devtools")
devtools::install_github(repo = "amices/mice")
```


## Minimal example

```{r pattern, fig.cap = "Missing data pattern of `nhanes` data. Blue is observed, red is missing."}
library(mice, warn.conflicts = FALSE)

# show the missing data pattern
md.pattern(nhanes)
```

The table and the graph summarize where the missing data occur in 
the `nhanes` dataset.

```{r stripplot, fig.cap = "Distribution of `chl` per imputed data set."}
# multiple impute the missing values
imp <- mice(nhanes, maxit = 2, m = 2, seed = 1)

# inspect quality of imputations
stripplot(imp, chl, pch = 19, xlab = "Imputation number")
```

In general, we would like the imputations to be plausible, i.e., 
values that could have been observed if they had not been missing.

```{r}
# fit complete-data model
fit <- with(imp, lm(chl ~ age + bmi))

# pool and summarize the results
summary(pool(fit))
```

The complete-data is fit to each imputed dataset, and the 
results are combined to arrive at estimates that properly 
account for the missing data.

## `mice 3.0`

Version 3.0 represents a major update that implements the 
following features: 

1. `blocks`: The main algorithm iterates over blocks. A block is
    simply a collection of variables. In the common MICE algorithm each 
    block was equivalent to one variable, which - of course - is 
    the default; The `blocks` argument allows mixing univariate 
    imputation method multivariate imputation methods. The `blocks` 
    feature bridges two seemingly disparate approaches, joint modeling 
    and fully conditional specification, into one framework;

2. `where`: The `where` argument is a logical matrix of the same size 
    of `data` that specifies which cells should be imputed. This opens 
    up some new analytic possibilities;
    
3.  Multivariate tests: There are new functions `D1()`, `D2()`, `D3()`
    and `anova()` that perform multivariate parameter tests on the 
    repeated analysis from on multiply-imputed data;

4. `formulas`: The old `form` argument has been redesign and is now 
    renamed to `formulas`. This provides an alternative way to specify
    imputation models that exploits the full power of R's native 
    formula's. 

5.  Better integration with the `tidyverse` framework, especially 
    for packages `dplyr`, `tibble` and  `broom`;
   
6.  Improved numerical algorithms for low-level imputation function. 
    Better handling of duplicate variables.

7.  Last but not least: A brand new edition AND online version of
    [Flexible Imputation of Missing Data. Second Edition.](https://stefvanbuuren.name/fimd/)

See [MICE: Multivariate Imputation by Chained Equations](https://amices.org/mice/) 
for more resources.

I'll be happy to take feedback and discuss suggestions. Please submit these 
through Github's issues facility.


## Resources

### Books

1. Van Buuren, S. (2018). [Flexible Imputation of Missing Data. Second Edition.](https://stefvanbuuren.name/fimd/). Chapman & Hall/CRC. Boca Raton, FL.

### Course materials

1. [Handling Missing Data in `R` with `mice`](https://amices.org/Winnipeg/)
2. [Statistical Methods for combined data sets](https://stefvanbuuren.name/RECAPworkshop/)

### Vignettes

1. [Ad hoc methods and the MICE algorithm](https://www.gerkovink.com/miceVignettes/Ad_hoc_and_mice/Ad_hoc_methods.html)
2. [Convergence and pooling](https://www.gerkovink.com/miceVignettes/Convergence_pooling/Convergence_and_pooling.html)
3. [Inspecting how the observed data and missingness are related](https://www.gerkovink.com/miceVignettes/Missingness_inspection/Missingness_inspection.html)
4. [Passive imputation and post-processing](https://www.gerkovink.com/miceVignettes/Passive_Post_processing/Passive_imputation_post_processing.html)
5. [Imputing multilevel data](https://www.gerkovink.com/miceVignettes/Multi_level/Multi_level_data.html)
6. [Sensitivity analysis with `mice`](https://www.gerkovink.com/miceVignettes/Sensitivity_analysis/Sensitivity_analysis.html)
7. [Generate missing values with `ampute`](https://rianneschouten.github.io/mice_ampute/vignette/ampute.html)
8. [`futuremice`: Wrapper for parallel MICE imputation through futures](https://www.gerkovink.com/miceVignettes/futuremice/Vignette_futuremice.html)

### Code from publications

1. [Flexible Imputation of Missing Data. Second edition.](https://github.com/stefvanbuuren/fimdbook/tree/master/R)

## Acknowledgement

The cute mice sticker was designed by Jaden M. Walters. Thanks Jaden!

## Code of Conduct

Please note that the mice project is released with a [Contributor Code of Conduct](https://amices.org/mice/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

Owner

Name: MICE
Login: amices
Kind: organization

Website: amices.org
Twitter: MICEamices
Repositories: 13
Profile: https://github.com/amices

A home for the growing MICE family

GitHub Events

Total

Create event: 23
Release event: 4
Issues event: 35
Watch event: 39
Delete event: 23
Issue comment event: 53
Push event: 141
Pull request review comment event: 7
Pull request review event: 7
Pull request event: 29
Fork event: 11

Last Year

Create event: 23
Release event: 4
Issues event: 35
Watch event: 39
Delete event: 23
Issue comment event: 53
Push event: 141
Pull request review comment event: 7
Pull request review event: 7
Pull request event: 29
Fork event: 11

Committers

Last synced: over 2 years ago

All Time

Total Commits: 1,394
Total Committers: 38
Avg Commits per committer: 36.684
Development Distribution Score (DDS): 0.282

Past Year

Commits: 141
Committers: 6
Avg Commits per committer: 23.5
Development Distribution Score (DDS): 0.305

Top Committers

Name	Email	Commits
Stef van Buuren	s**n@t**l	1,001
Gerko Vink	g**k@u**l	131
RianneSchouten	r**n@u**l	82
Patrick Rockenschaub	p**5@u**k	33
Thom Volker	t**r@u**l	29
Edo	3****i	16
hanneoberman	h**n@u**l	16
RianneSchouten	r**n@l**l	15
cjvanlissa	c**a@u**l	12
Stef van Buuren	s**n@u**l	7
Vincent Arel-Bundock	v**k@u**a	6
RianneSchouten	r**n@g**m	6
Bernie Gray	b**3@g**m	5
Andrew Landgraf	a****d	4
Patrick Rockenschaub	r**k@g**m	3
Lukas Wallrich	l**h@g**m	2
Vladimir Khodygo	v**o@g**k	2
efbonneville	e**e@l**l	2
Mingyang Cai	4****i	2
bgall	b****l	2
Marcio Augusto Diniz	d**o@g**m	1
Gerko Vink	g**k@g**m	1
Claudio Bustos	c**s@g**m	1
Martin Maechler	m**r@r**g	1
Byron	b**r@g**m	1
stephematician	s**n@g**m	1
Rasel Biswas	r**1@i**d	1
kkleinke	k**e@g**m	1
Arthur Yip	a**p@c**u	1
Bastiaan Quast	b**t@g**m	1
and 8 more...

Committer Domains (Top 20 + Academic)

uu.nl: 6 google.com: 1 cmu.edu: 1 isrt.ac.bd: 1 r-project.org: 1 lumc.nl: 1 glasgow.ac.uk: 1 umontreal.ca: 1 live.nl: 1 ucl.ac.uk: 1 tno.nl: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 134
Total pull requests: 68
Average time to close issues: 4 months
Average time to close pull requests: 2 months
Total issue authors: 99
Total pull request authors: 20
Average comments per issue: 3.18
Average comments per pull request: 1.72
Merged pull requests: 45
Bot issues: 0
Bot pull requests: 8

Past Year

Issues: 17
Pull requests: 13
Average time to close issues: 7 days
Average time to close pull requests: about 1 month
Issue authors: 14
Pull request authors: 4
Average comments per issue: 1.47
Average comments per pull request: 0.46
Merged pull requests: 4
Bot issues: 0
Bot pull requests: 5

View more stats

Top Authors

Issue Authors

hanneoberman (8)
stefvanbuuren (8)
Generalized (4)
thomvolker (3)
ndsubison2178 (3)
isaactpetersen (3)
gerkovink (3)
andreassoteriadesmoj (2)
franzepsy (2)
vkhodygo (2)
ajw11 (2)
Gootjes (2)
sff06 (2)
kequach (2)
jay-sf (2)

Pull Request Authors

dependabot[bot] (16)
stefvanbuuren (14)
hanneoberman (9)
gerkovink (8)
prockenschaub (6)
vkhodygo (4)
thomvolker (4)
stephematician (4)
Ralayax (2)
MichaelChirico (2)
johamunoz (2)
Mingyang-Cai (1)
edbonneville (1)
AndrewLawrence (1)
cjvanlissa (1)

Top Labels

Issue Labels

bug (33) help wanted (16) enhancement (4) wontfix (3) advanced (2) documentation (1) Methodology (1)

Pull Request Labels

dependencies (16) enhancement (4) bugfix (3) github_actions (3) advanced (2) documentation (1)

Packages

Total packages: 1
Total downloads:
- cran 74,524 last-month
Total docker downloads: 277,647

Total dependent packages: 129
Total dependent repositories: 258
Total versions: 49
Total maintainers: 1

cran.r-project.org: mice

Multivariate Imputation by Chained Equations

Homepage: https://github.com/amices/mice
Documentation: http://cran.r-project.org/web/packages/mice/mice.pdf
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Latest release: 3.18.0
published 9 months ago

Versions: 49
Dependent Packages: 129
Dependent Repositories: 258
Downloads: 74,524 Last month
Docker Downloads: 277,647

Rankings

Forks count: 0.6%

Dependent packages count: 0.7%

Stargazers count: 1.0%

Dependent repos count: 1.0%

Downloads: 1.4%

Average: 4.6%

Docker downloads count: 23.1%

Maintainers (1)

stef.vanbuuren@tno.nl

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

R >= 2.10.0 depends
Rcpp * imports
broom * imports
dplyr * imports
generics * imports
grDevices * imports
graphics * imports
lattice * imports
methods * imports
rlang * imports
stats * imports
tidyr * imports
utils * imports
withr >= 2.4.0 imports
MASS * suggests
broom.mixed * suggests
decor * suggests
glmnet * suggests
haven * suggests
knitr * suggests
lme4 * suggests
lmtest * suggests
metafor * suggests
miceadds * suggests
mitml * suggests
nnet * suggests
pan * suggests
purrr * suggests
randomForest * suggests
ranger * suggests
rmarkdown * suggests
rpart * suggests
rstan * suggests
survival * suggests
testthat * suggests

.github/workflows/pkgdown.yaml actions

actions/checkout v2 composite
r-lib/actions/setup-pandoc v1 composite
r-lib/actions/setup-r v1 composite
r-lib/actions/setup-r-dependencies v1 composite

.github/workflows/R-CMD-check.yaml actions

actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

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mice

Science Score: 36.0%

Keywords

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Basic Info

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README.Rmd

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