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mice (version 3.9.0)

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

65,189

Version

3.9.0

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

May 14th, 2020

Functions in mice (3.9.0)

Generate Missing Data for Simulation Purposes

Growth of Dutch boys

as.mitml.result

Converts into a mitml.result object

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

Create a mira object from repeated analyses

Multivariate Amputation In A MCAR Manner

Extracts the completed data from a mids object

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

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

Brandsma school data used Snijders and Bosker (2012)

Compare several nested models

Box-and-whisker plot of observed and imputed data

Complete case indicator

Appends specified break to the data

Combine R Objects by Rows and Columns

Computes least squares parameters

Extends a formula with predictors

Extract broken stick estimates from a lmer object

extend.formulas

Extends formula's with predictor matrix settings

Incomplete case indicator

Enlarge number of imputations by combining mids objects

is.mitml.result

Check for mitml.result object

Fluxplot of the missing data pattern

Employee selection data

Combine mids objects by columns

Select incomplete cases

Extract list of fitted model

Creates a formulas argument

SE Fireworks disaster data

Conditional imputation helper

Creates a method argument

Select complete cases

Check for mipo object

Extract estimate from mipo object

Fraction of incomplete cases among cases with observed

Fifth Dutch growth study 2009

Creates a post argument

make.visitSequence

Creates a visitSequence argument

make.predictorMatrix

Creates a predictorMatrix argument

Generalized linear model for mids object

Leiden 85+ study

Check for mira object

construct.blocks

Construct blocks from formulas and predictorMatrix

densityplot.mids

Density plot of observed and imputed data

Fix coefficients and update model

Creates a where argument

Creates a blocks argument

Missing data pattern

Graphical parameter for missing data plots.

Check for mads object

Influx and outflux of multivariate missing data patterns

Missing data pattern by variable pairs

mice.impute.cart

Imputation by classification and regression trees

Mammal sleep data

Check for mids object

Creates a blots argument

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.panImpute

Impute multilevel missing data using pan

Multivariate Amputed Data Set (mads)

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

mice: Multivariate Imputation by Chained Equations

mice.impute.2l.norm

Imputation by a two-level normal model

Linear regression for mids object

mice.impute.2l.pan

Imputation by a two-level normal model using pan

Export mids object to Mplus

mice.impute.passive

Passive imputation

Export mids object to SPSS

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.2lonly.mean

Imputation of most likely value within the class

Number of imputations per block

Multiply imputed repeated analyses (mira)

mice.impute.pmm

Imputation by predictive mean matching

mipo: Multiple imputation pooled object

mice.impute.polr

Imputation of ordered data by polytomous regression

Imputation by random forests

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

Draws values of beta and sigma by Bayesian linear regression

mice.impute.logreg

Imputation by logistic regression

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.mean

Imputation by the mean

mice.impute.sample

Imputation by simple random sampling

Imputation by the random indicator method for nonignorable data

mice.impute.norm.predict

Imputation by linear regression through prediction

NHANES example - mixed numerical and discrete variables

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

Plot the trace lines of the MICE algorithm

Number of incomplete cases

Print a mads object

Hox pupil popularity data with missing popularity scores

Multivariate Imputation by Chained Equations (Iteration Step)

Quick selection of predictors from the data

Name imputation blocks

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

Echoes the package version number

Pooling: R squared

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Terneuzen birth cohort

Scatterplot of observed and imputed data

Set the theme for the plotting Trellis functions

Squeeze the imputed values to be within specified boundaries.

mice.impute.quadratic

Imputation of quadratic terms

mice.impute.norm

Imputation by Bayesian linear regression

Multiple imputation pooling: univariate version

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

Stripplot of observed and imputed data

Name formula list elements

Multiply imputed data set (mids)

Wrapper function that runs MICE in parallel

Number of complete cases

Datasets with various missing data patterns

Evaluate an expression in multiple imputed datasets

Self-reported and measured BMI

Summary of a mira object

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

supports.transparent

Supports semi-transparent foreground colors?

Combine mids objects by rows

Cumulative hazard rate or Nelson-Aalen estimator

Print a mids object

Compare two nested models fitted to imputed data

Combine estimates by Rubin's rules

Potthoff-Roy data

NHANES example - all variables numerical

Walking disability data

Subset of Irish wind speed data

ampute.default.patterns

Default patterns in ampute

Compare two nested models using D2-statistic

Compare two nested models using D3-statistic

Compare two nested models using D1-statistic

ampute.default.weights

Default weights in ampute

ampute.default.freq

Default freq in ampute

ampute.default.type

Default type in ampute()

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute.default.odds

Default odds in ampute()