Learn R Programming

⚠️There's a newer version (3.19.0) of this package.Take me there.

mice (version 2.18)

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

106,176

Version

2.18

License

GPL-2 | GPL-3

Homepage

http://www.stefvanbuuren.nl

Maintainer

Stef van Buuren

Last Published

July 31st, 2013

Functions in mice (2.18)

Box-and-whisker plot of observed and imputed data

SE Fireworks disaster data

Incomplete cases

Multiply imputed repeated analyses (mira)

mice.impute.passive

Passive imputation

mice.impute.lda

Imputation by linear discriminant analysis

Pooling: R squared

Columnwise combination of a mids object.

mice.impute.2l.pan

Imputation by a two-level normal model using pan

Generalized linear model for mids object

Subset of Irish wind speed data

Evaluate an expression in multiple imputed datasets

Quick selection of predictors from the data

mice.impute.norm.predict

Imputation by linear regression, prediction method

Incomplete cases n

Fifth Dutch growth study 2009

Rowwise combination of a mids object.

mice.impute.polr

Imputation by polytomous regression - ordered

Complete cases n

Multivariate Imputation by Chained Equations (MICE)

Export mids object to Mplus

Squeeze the imputed values to be within specified boundaries.

Self-reported and measured BMI

mice.impute.2lonly.mean

Imputation of the mean within the class

Check for mipo object

Check for mira object

Potthoff-Roy data

NHANES example - all variables numerical

Extract broken stick estimates from a lmer object

Mammal sleep data

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

supports.transparent

Supports semi-transparent foreground colors?

NHANES example - mixed numerical and discrete variables

Multiple imputation pooling

Multiple imputation pooling: univariate version

Leiden 85+ study

Walking disability data

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

densityplot.mids

Density plot of observed and imputed data

Creates imputed data sets from a mids object

mice.impute.cart

Imputation by classification and regression trees

Terneuzen birth cohort

Summary of a mira object

Datasets with various missing data patterns

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

Appends specified break to the data

Missing data pattern by variable pairs

Incomplete case indicator

Graphical parameter for missing data plots.

Complete case indicator

mice.impute.2l.norm

Imputation by a two-level normal model

Imputation by the random indicator method for nonignorable data

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.logreg

Imputation by logistic regression

mice.impute.norm.nob

Imputation by linear regression (non Bayesian)

Fraction of incomplete cases among cases with observed

mice.impute.polyreg

Imputation by polytomous regression - unordered

Conditional imputation helper

Echoes the package version number

Fluxplot of the missing data pattern

Cumulative hazard rate or Nelson-Aalen estimator

Multivariate Imputation by Chained Equations (Iteration Step)

Stripplot of observed and imputed data

Plot the trace lines of the MICE algorithm

Hox pupil popularity data with missing popularity scores

Extracts fit objects from mira object

Scatterplot of observed and imputed data

Multiply imputed pooled analysis (mipo)

Growth of Dutch boys

Linear regression for mids object

Missing data pattern

Create a mira object from repeated analyses

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

Combine imputations fitted to the same data

Compare two nested models fitted to imputed data

Conversion of a imputed data set (long form) to a mids object

Influx and outflux of multivariate missing data patterns

Multiply imputed data set (mids)

mice.impute.mean

Imputation by the mean

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

Check for mids object

mice.impute.quadratic

Imputation of quadratric terms

Print a mids object

mice.impute.pmm

Imputation by predictive mean matching

Export mids object to SPSS

mice.impute.sample

Imputation by simple random sampling

Set the theme for the plotting Trellis functions