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mice (version 3.10.0.1)

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

84,777

Version

3.10.0.1

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

August 2nd, 2020

Functions in mice (3.10.0.1)

Compare two nested models using D2-statistic

ampute.default.freq

Default freq in ampute

Compare two nested models using D3-statistic

Generate Missing Data for Simulation Purposes

Compare two nested models using D1-statistic

ampute.default.patterns

Default patterns in ampute

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute.default.type

Default type in ampute()

ampute.default.odds

Default odds in ampute()

ampute.default.weights

Default weights in ampute

as.mitml.result

Converts into a mitml.result object

Fluxplot of the missing data pattern

Multivariate Amputation In A MCAR Manner

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Extract list of fitted model

Complete case indicator

Extracts the completed data from a mids object

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

Employee selection data

Brandsma school data used Snijders and Bosker (2012)

Compare several nested models

Extract estimate from mipo object

Computes least squares parameters

Fraction of incomplete cases among cases with observed

Fifth Dutch growth study 2009

Appends specified break to the data

construct.blocks

Construct blocks from formulas and predictorMatrix

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

Fix coefficients and update model

Box-and-whisker plot of observed and imputed data

Growth of Dutch boys

densityplot.mids

Density plot of observed and imputed data

Influx and outflux of multivariate missing data patterns

Create a mira object from repeated analyses

Combine mids objects by columns

Incomplete case indicator

Select incomplete cases

Combine R Objects by Rows and Columns

Generalized linear model for mids object

Creates a blots argument

Glance method to extract information from a `mipo` object

Extract broken stick estimates from a lmer object

SE Fireworks disaster data

Select complete cases

Extends a formula with predictors

Enlarge number of imputations by combining mids objects

Conditional imputation helper

Check for mads object

Check for mira object

Creates a method argument

Check for mids object

extend.formulas

Extends formula's with predictor matrix settings

Check for mipo object

Creates a post argument

Linear regression for mids object

Leiden 85+ study

Multivariate Amputed Data Set (mads)

is.mitml.result

Check for mitml.result object

Creates a blocks argument

Creates a where argument

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

Mammal sleep data

Graphical parameter for missing data plots.

Creates a formulas argument

mice.impute.2l.norm

Imputation by a two-level normal model

mice.impute.2l.pan

Imputation by a two-level normal model using pan

make.predictorMatrix

Creates a predictorMatrix argument

Missing data pattern by variable pairs

mice: Multivariate Imputation by Chained Equations

mice.impute.2lonly.mean

Imputation of most likely value within the class

make.visitSequence

Creates a visitSequence argument

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

Missing data pattern

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.logreg

Imputation by logistic regression

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

mice.impute.cart

Imputation by classification and regression trees

Multiply imputed data set (mids)

mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.mean

Imputation by the mean

mice.impute.norm

Imputation by Bayesian linear regression

Export mids object to Mplus

mice.impute.polyreg

Imputation of unordered data by polytomous regression

mice.impute.polr

Imputation of ordered data by polytomous regression

Multiply imputed repeated analyses (mira)

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.pmm

Imputation by predictive mean matching

mice.impute.passive

Passive imputation

Draws values of beta and sigma by Bayesian linear regression

mipo: Multiple imputation pooled object

Export mids object to SPSS

mice.impute.lda

Imputation by linear discriminant analysis

Pooling: R squared

Compare two nested models fitted to imputed data

Self-reported and measured BMI

Objects exported from other packages

Wrapper function that runs MICE in parallel

Imputation by the random indicator method for nonignorable data

NHANES example - all variables numerical

mice.impute.quadratic

Imputation of quadratic terms

Imputation by random forests

Multivariate Imputation by Chained Equations (Iteration Step)

NHANES example - mixed numerical and discrete variables

mice.impute.sample

Imputation by simple random sampling

Plot the trace lines of the MICE algorithm

Datasets with various missing data patterns

Quick selection of predictors from the data

Combine mids objects by rows

Print a mids object

Echoes the package version number

Set the theme for the plotting Trellis functions

mice.impute.norm.predict

Imputation by linear regression through prediction

Print a mads object

Number of complete cases

Cumulative hazard rate or Nelson-Aalen estimator

Number of incomplete cases

mice.impute.panImpute

Impute multilevel missing data using pan

Summary of a mira object

Walking disability data

supports.transparent

Supports semi-transparent foreground colors?

Number of imputations per block

Name imputation blocks

Name formula list elements

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

Potthoff-Roy data

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

Combine estimates by Rubin's rules

Multiple imputation pooling: univariate version

Hox pupil popularity data with missing popularity scores

Tidy method to extract results from a `mipo` object

Subset of Irish wind speed data

Terneuzen birth cohort

Evaluate an expression in multiple imputed datasets

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

Scatterplot of observed and imputed data

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