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mice (version 3.7.0)

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

3.7.0

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

December 13th, 2019

Functions in mice (3.7.0)

ampute.default.freq

Default freq in ampute

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Multivariate Amputation In A MCAR Manner

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

Brandsma school data used Snijders and Bosker (2012)

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

Create a mira object from repeated analyses

Complete case indicator

Combine mids objects by columns

Growth of Dutch boys

as.mitml.result

Converts into a mitml.result object

construct.blocks

Construct blocks from formulas and predictorMatrix

Compare several nested models

Employee selection data

Appends specified break to the data

Box-and-whisker plot of observed and imputed data

Extracts the completed data from a mids object

densityplot.mids

Density plot of observed and imputed data

Incomplete case indicator

Enlarge number of imputations by combining mids objects

Extract list of fitted model

Fluxplot of the missing data pattern

Computes least squares parameters

extend.formulas

Extends formula's with predictor matrix settings

Extends a formula with predictors

Select incomplete cases

Conditional imputation helper

Creates a formulas argument

Select complete cases

SE Fireworks disaster data

Check for mipo object

Extract broken stick estimates from a lmer object

Check for mira object

is.mitml.result

Check for mitml.result object

Leiden 85+ study

Fix coefficients and update model

Influx and outflux of multivariate missing data patterns

make.visitSequence

Creates a visitSequence argument

Fraction of incomplete cases among cases with observed

Check for mads object

Linear regression for mids object

Check for mids object

Creates a method argument

Combine R Objects by Rows and Columns

Fifth Dutch growth study 2009

Creates a where argument

Multivariate Amputed Data Set (mads)

mice: Multivariate Imputation by Chained Equations

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Missing data pattern by variable pairs

make.predictorMatrix

Creates a predictorMatrix argument

Extract estimate from mipo object

Creates a post argument

Generalized linear model for mids object

Mammal sleep data

mice.impute.2l.norm

Imputation by a two-level normal model

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

Creates a blocks argument

mice.impute.2lonly.mean

Imputation of most likely value within the class

Export mids object to Mplus

Multiply imputed data set (mids)

Combine estimates by Rubin's rules

Number of incomplete cases

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

NHANES example - mixed numerical and discrete variables

mice.impute.cart

Imputation by classification and regression trees

Print a mids object

Compare two nested models fitted to imputed data

Potthoff-Roy data

mice.impute.mean

Imputation by the mean

mice.impute.polr

Imputation of ordered data by polytomous regression

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

Missing data pattern

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Creates a blots argument

Multivariate Imputation by Chained Equations (Iteration Step)

Terneuzen birth cohort

mice.impute.norm.predict

Imputation by linear regression through prediction

Set the theme for the plotting Trellis functions

Multiply imputed repeated analyses (mira)

mice.impute.panImpute

Impute multilevel missing data using pan

Graphical parameter for missing data plots.

mice.impute.sample

Imputation by simple random sampling

Imputation by the random indicator method for nonignorable data

Name imputation blocks

Datasets with various missing data patterns

Self-reported and measured BMI

Combine mids objects by rows

Wrapper function that runs MICE in parallel

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.logreg

Imputation by logistic regression

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.passive

Passive imputation

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

Number of imputations per block

Plot the trace lines of the MICE algorithm

Draws values of beta and sigma by Bayesian linear regression

mice.impute.pmm

Imputation by predictive mean matching

Cumulative hazard rate or Nelson-Aalen estimator

mipo: Multiple imputation pooled object

Export mids object to SPSS

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.quadratic

Imputation of quadratic terms

Imputation by random forests

NHANES example - all variables numerical

Name formula list elements

Number of complete cases

Hox pupil popularity data with missing popularity scores

Pooling: R squared

Multiple imputation pooling: univariate version

Summary of a mira object

Print a mads object

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

Subset of Irish wind speed data

Evaluate an expression in multiple imputed datasets

Quick selection of predictors from the data

Stripplot of observed and imputed data

Scatterplot of observed and imputed data

Squeeze the imputed values to be within specified boundaries.

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

Echoes the package version number

supports.transparent

Supports semi-transparent foreground colors?

Walking disability data

ampute.default.odds

Default odds in ampute()

ampute.default.type

Default type in ampute()

Generate Missing Data for Simulation Purposes

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

Compare two nested models using D3-statistic

Compare two nested models using D1-statistic

ampute.default.weights

Default weights in ampute

ampute.default.patterns

Default patterns in ampute

Compare two nested models using D2-statistic