Learn R Programming

⚠️There's a newer version (3.17.0) of this package.Take me there.

mice (version 2.13)

Multivariate Imputation by Chained Equations

Description

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

Copy Link

Version

Install

install.packages('mice')

Monthly Downloads

65,189

Version

2.13

License

GPL-2 | GPL-3

Maintainer

Stef van Buuren

Last Published

July 4th, 2012

Functions in mice (2.13)

mice.impute.2l.pan

Imputation by a Two-Level Normal Model using pan
cc

Extracts complete and incomplete cases
mice.impute.logreg

Multiple Imputation by Logistic Regression
mice.impute.polyreg

Imputation by Polytomous Regression
nhanes

NHANES example - all variables numerical
mice.impute.mean

Imputation by the Mean
mids

Multiply Imputed Data Set
flux

Influx and outflux of multivatiate missing data patterns
quickpred

Quick selection of predictors from the data
mira

Multiply Imputed Repeated Analyses
mice.impute.passive

Passive Imputation
mice.impute.norm

Imputation by Bayesian Linear Regression
getfit

Extracts fit objects from mira object
cci

Extracts (in)complete case indicator
mdc

Graphical parameter for missing data plots.
mice.impute.quadratic

Imputation of quadratric terms
mice.impute.sample

Imputation by Simple Random Sampling
lm.mids

Linear Regression on Multiply Imputed Data
md.pairs

Missing data pattern by variable pairs
mice.impute.2lonly.norm

Imputation at Level 2 by Bayesian Linear Regression
mice.impute.pmm

Imputation by Predictive Mean Matching
mice.auxiliary

Auxiliary functions used in FIMD
mice.impute.norm.predict

Imputation by Linear Regression, Prediction Method
mice.impute.lda

Imputation by Linear Discriminant Analysis
mids2spss

Export Multiply Imputed Data to SPSS
version

Echoes the package version number
glm.mids

Generalized Linear Model for Multiply Imputed Data
supports.transparent

Does the current graphic device support semi-transparent foreground colors?
mice.internal

Internal mice functions
nhanes2

NHANES example - mixed numerical and discrete variables
mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)
with.mids

Evaluate an expression in multiple imputed datasets
pool.r.squared

Pooling: R squared
pool

Multiple Imputation Pooling
mids2mplus

Export Multiply Imputed Data to Mplus
md.pattern

Missing Data Pattern
stripplot

Box-and-whisker plot, stripplot, density plot and scatterplot for imputed data
mice

Multivariate Imputation by Chained Equations (MICE)
ccn

Number of (in)complete cases
mice.impute.2lonly.pmm

Imputation at Level 2 by Predictive Mean Matching
mipo

Multiply Imputed Pooled Analysis
complete

Creates a Complete Flat File from a Multiply Imputed Data Set
mice.impute.norm.boot

Imputation by Linear Regression, Bootstrap Method
nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator