Learn R Programming

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

mice (version 3.10.0)

Multivariate Imputation by Chained Equations

Description

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . 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

79,941

Version

3.10.0

License

GPL-2 | GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/stefvanbuuren/mice

Homepage

https://stefvanbuuren.name/mice/

Maintainer

Stef van Buuren

Last Published

July 13th, 2020

Functions in mice (3.10.0)

Compare two nested models using D2-statistic

ampute.default.weights

Default weights in ampute

Compare two nested models using D1-statistic

Compare two nested models using D3-statistic

Generate Missing Data for Simulation Purposes

ampute.default.odds

Default odds in ampute()

ampute.default.freq

Default freq in ampute

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute.default.patterns

Default patterns in ampute

ampute.default.type

Default type in ampute()

as.mitml.result

Converts into a mitml.result object

Brandsma school data used Snijders and Bosker (2012)

Compare several nested models

Appends specified break to the data

Create a mira object from repeated analyses

Multivariate Amputation In A MCAR Manner

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

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

Box-and-whisker plot of observed and imputed data

Combine mids objects by columns

Growth of Dutch boys

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

Complete case indicator

Employee selection data

Extracts the completed data from a mids object

Combine R Objects by Rows and Columns

Computes least squares parameters

Select complete cases

construct.blocks

Construct blocks from formulas and predictorMatrix

Extends a formula with predictors

Fifth Dutch growth study 2009

densityplot.mids

Density plot of observed and imputed data

Fix coefficients and update model

Extract estimate from mipo object

Fraction of incomplete cases among cases with observed

Glance method to extract information from a `mipo` object

Check for mids object

Fluxplot of the missing data pattern

Check for mipo object

Extract list of fitted model

Influx and outflux of multivariate missing data patterns

Check for mads object

extend.formulas

Extends formula's with predictor matrix settings

Multivariate Amputed Data Set (mads)

Creates a blocks argument

Creates a blots argument

Graphical parameter for missing data plots.

Extract broken stick estimates from a lmer object

Leiden 85+ study

Linear regression for mids object

mice: Multivariate Imputation by Chained Equations

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

SE Fireworks disaster data

Select incomplete cases

Generalized linear model for mids object

mice.impute.logreg

Imputation by logistic regression

Incomplete case indicator

Creates a formulas argument

Check for mira object

Creates a method argument

Enlarge number of imputations by combining mids objects

Creates a post argument

Missing data pattern by variable pairs

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

make.visitSequence

Creates a visitSequence argument

make.predictorMatrix

Creates a predictorMatrix argument

mice.impute.cart

Imputation by classification and regression trees

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

Missing data pattern

mice.impute.lda

Imputation by linear discriminant analysis

is.mitml.result

Check for mitml.result object

mice.impute.norm.predict

Imputation by linear regression through prediction

Cumulative hazard rate or Nelson-Aalen estimator

Creates a where argument

Datasets with various missing data patterns

Mammal sleep data

Conditional imputation helper

Number of complete cases

Plot the trace lines of the MICE algorithm

mice.impute.polr

Imputation of ordered data by polytomous regression

Number of incomplete cases

Number of imputations per block

mice.impute.pmm

Imputation by predictive mean matching

mice.impute.passive

Passive imputation

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Print a mids object

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

mice.impute.panImpute

Impute multilevel missing data using pan

mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS

Print a mads object

Terneuzen birth cohort

Potthoff-Roy data

Tidy method to extract results from a `mipo` object

Multiply imputed data set (mids)

Export mids object to Mplus

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.2lonly.mean

Imputation of most likely value within the class

mice.impute.mean

Imputation by the mean

Imputation by the random indicator method for nonignorable data

mice.impute.2l.norm

Imputation by a two-level normal model

mice.impute.2l.pan

Imputation by a two-level normal model using pan

Multivariate Imputation by Chained Equations (Iteration Step)

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

Name formula list elements

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

Name imputation blocks

mice.impute.quadratic

Imputation of quadratic terms

Set the theme for the plotting Trellis functions

Quick selection of predictors from the data

Multiply imputed repeated analyses (mira)

Imputation by random forests

Summary of a mira object

Export mids object to SPSS

mice.impute.sample

Imputation by simple random sampling

Combine mids objects by rows

Draws values of beta and sigma by Bayesian linear regression

mipo: Multiple imputation pooled object

supports.transparent

Supports semi-transparent foreground colors?

NHANES example - all variables numerical

NHANES example - mixed numerical and discrete variables

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

Compare two nested models fitted to imputed data

Wrapper function that runs MICE in parallel

Pooling: R squared

Echoes the package version number

Combine estimates by Rubin's rules

Walking disability data

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

Self-reported and measured BMI

Objects exported from other packages

Scatterplot of observed and imputed data

Multiple imputation pooling: univariate version

Subset of Irish wind speed data

Evaluate an expression in multiple imputed datasets

Hox pupil popularity data with missing popularity scores

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data