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mice (version 3.8.0)

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.

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Install

install.packages('mice')

Monthly Downloads

83,091

Version

3.8.0

License

GPL-2 | GPL-3

Issues

Pull Requests

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Repository

https://github.com/stefvanbuuren/mice

Homepage

https://stefvanbuuren.name/mice/

Maintainer

Stef van Buuren

Last Published

February 21st, 2020

Functions in mice (3.8.0)

ampute.default.weights

Default weights in ampute

Compare two nested models using D3-statistic

Compare two nested models using D2-statistic

ampute.default.patterns

Default patterns in ampute

as.mitml.result

Converts into a mitml.result object

ampute.default.freq

Default freq in ampute

Generate Missing Data for Simulation Purposes

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

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

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

Combine R Objects by Rows and Columns

Brandsma school data used Snijders and Bosker (2012)

Growth of Dutch boys

Create a mira object from repeated analyses

Combine mids objects by columns

Select complete cases

Box-and-whisker plot of observed and imputed data

Extract estimate from mipo object

Fifth Dutch growth study 2009

construct.blocks

Construct blocks from formulas and predictorMatrix

Fluxplot of the missing data pattern

Fix coefficients and update model

Multivariate Amputed Data Set (mads)

Extract list of fitted model

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Generalized linear model for mids object

Fraction of incomplete cases among cases with observed

Linear regression for mids object

densityplot.mids

Density plot of observed and imputed data

Check for mipo object

Influx and outflux of multivariate missing data patterns

Incomplete case indicator

Appends specified break to the data

Compare several nested models

Complete case indicator

Extracts the completed data from a mids object

Computes least squares parameters

Employee selection data

Multivariate Amputation In A MCAR Manner

Check for mira object

Missing data pattern

Conditional imputation helper

Extract broken stick estimates from a lmer object

Creates a post argument

Extends a formula with predictors

Select incomplete cases

Graphical parameter for missing data plots.

SE Fireworks disaster data

Enlarge number of imputations by combining mids objects

make.predictorMatrix

Creates a predictorMatrix argument

mice: Multivariate Imputation by Chained Equations

mice.impute.cart

Imputation by classification and regression trees

Creates a blocks argument

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

Creates a blots argument

is.mitml.result

Check for mitml.result object

make.visitSequence

Creates a visitSequence argument

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Check for mids object

mice.impute.lda

Imputation by linear discriminant analysis

extend.formulas

Extends formula's with predictor matrix settings

Check for mads object

Creates a where argument

mice.impute.logreg

Imputation by logistic regression

mice.impute.2l.norm

Imputation by a two-level normal model

Creates a method argument

Creates a formulas argument

Leiden 85+ study

mice.impute.pmm

Imputation by predictive mean matching

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

Missing data pattern by variable pairs

mice.impute.polr

Imputation of ordered data by polytomous regression

mipo: Multiple imputation pooled object

Export mids object to Mplus

Mammal sleep data

Export mids object to SPSS

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.mean

Imputation by the mean

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

Print a mads object

Combine estimates by Rubin's rules

Echoes the package version number

Quick selection of predictors from the data

Multiply imputed repeated analyses (mira)

Compare two nested models fitted to imputed data

Set the theme for the plotting Trellis functions

mice.impute.2lonly.mean

Imputation of most likely value within the class

Number of incomplete cases

Number of imputations per block

mice.impute.panImpute

Impute multilevel missing data using pan

mice.impute.norm.predict

Imputation by linear regression through prediction

Multivariate Imputation by Chained Equations (Iteration Step)

mice.impute.norm

Imputation by Bayesian linear regression

Imputation by random forests

Draws values of beta and sigma by Bayesian linear regression

mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS

Name imputation blocks

NHANES example - mixed numerical and discrete variables

Multiply imputed data set (mids)

mice.impute.passive

Passive imputation

Combine mids objects by rows

Wrapper function that runs MICE in parallel

mice.impute.sample

Imputation by simple random sampling

Imputation by the random indicator method for nonignorable data

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

Cumulative hazard rate or Nelson-Aalen estimator

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Hox pupil popularity data with missing popularity scores

Datasets with various missing data patterns

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

NHANES example - all variables numerical

Summary of a mira object

supports.transparent

Supports semi-transparent foreground colors?

Multiple imputation pooling: univariate version

Squeeze the imputed values to be within specified boundaries.

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

Stripplot of observed and imputed data

Number of complete cases

Name formula list elements

Terneuzen birth cohort

mice.impute.quadratic

Imputation of quadratic terms

Evaluate an expression in multiple imputed datasets

Scatterplot of observed and imputed data

Self-reported and measured BMI

Pooling: R squared

Plot the trace lines of the MICE algorithm

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

Potthoff-Roy data

Print a mids object

Walking disability data

Subset of Irish wind speed data

Compare two nested models using D1-statistic

ampute.default.odds

Default odds in ampute()

ampute.default.type

Default type in ampute()