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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:

install.packages("mice")

The latest version is can be installed from GitHub as follows:

install.packages("devtools")
devtools::install_github(repo = "stefvanbuuren/mice")

See MICE: Multivariate Imputation by Chained Equations for more details.

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Version

Install

install.packages('mice')

Monthly Downloads

92,351

Version

2.46.0

License

GPL-2 | GPL-3

Homepage

http://stefvanbuuren.github.io/mice/

Maintainer

Stef van Buuren

Last Published

October 24th, 2017

Functions in mice (2.46.0)

Multivariate Amputation In A MCAR Manner

Appends specified break to the data

ampute.default.patterns

Default patterns in ampute

ampute.default.type

Default type in ampute()

Generate Missing Data for Simulation Purposes

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute.default.weights

Default weights in ampute

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

ampute.default.freq

Default freq in ampute

ampute.default.odds

Default odds in ampute()

Extracts imputed data sets from a mids object

densityplot.mids

Density plot of observed and imputed data

Extracts fit objects from mira object

Generalized linear model for mids object

Growth of Dutch boys

Box-and-whisker plot of amputed and non-amputed data

Extract broken stick estimates from a lmer object

SE Fireworks disaster data

Multivariate Amputed Data Set (mads)

Select complete cases

Complete case indicator

Influx and outflux of multivariate missing data patterns

Fluxplot of the missing data pattern

Mammal sleep data

Missing data pattern by variable pairs

Missing data pattern

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

Check for mads object

Check for mids object

Graphical parameter for missing data plots.

mice: Multivariate Imputation by Chained Equations

mice.impute.logreg

Imputation by logistic regression

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

Multivariate Imputation by Chained Equations (Iteration Step)

Set the theme for the plotting Trellis functions

Cumulative hazard rate or Nelson-Aalen estimator

Converts an multiply imputed dataset (long format) into a mids object

Create a mira object from repeated analyses

Fifth Dutch growth study 2009

NHANES example - all variables numerical

Multiple imputation pooling

Compare two nested models fitted to imputed data

Potthoff-Roy data

Check for mipo object

Check for mira object

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice.impute.2lonly.mean

Imputation of the mean within the class

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.polr

Imputation of ordered data by polytomous regression

Print a mids object

Scatterplot of observed and imputed data

Box-and-whisker plot of observed and imputed data

Columnwise combination of a mids object.

Enlarge number of imputations by combining mids objects

Select incomplete cases

Incomplete case indicator

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Export mids object to SPSS

Multiply imputed pooled analysis (mipo)

Fraction of incomplete cases among cases with observed

Leiden 85+ study

Linear regression for mids object

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Pooling: R squared

Multiple imputation pooling: univariate version

Summary of a mira object

mice.impute.norm.predict

Imputation by linear regression through prediction

Imputation by the random indicator method for nonignorable data

mice.impute.sample

Imputation by simple random sampling

Multiply imputed data set (mids)

Export mids object to Mplus

mice.impute.2l.norm

Imputation by a two-level normal model

mice.impute.cart

Imputation by classification and regression trees

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.passive

Passive imputation

Conditional imputation helper

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.mean

Imputation by the mean

Hox pupil popularity data with missing popularity scores

Project on preterm and small for gestational age infants (POPS)

Print a mads object

NHANES example - mixed numerical and discrete variables

Number of incomplete cases

Draws values of beta and sigma by Bayesian linear regression

Quick selection of predictors from the data

supports.transparent

Supports semi-transparent foreground colors?

Walking disability data

Subset of Irish wind speed data

mice.impute.pmm

Imputation by predictive mean matching

Datasets with various missing data patterns

Rowwise combination of a mids object.

Self-reported and measured BMI

Evaluate an expression in multiple imputed datasets

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.quadratic

Imputation of quadratric terms

Imputation by random forests

Multiply imputed repeated analyses (mira)

Number of complete cases

Scatterplot of amputed and non-amputed data against weighted sum scores

Plot the trace lines of the MICE algorithm

Finds an imputed value from matches in the predictive metric (deprecated)

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

Terneuzen birth cohort

Echoes the package version number