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mice (version 3.12.0)

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

3.12.0

License

GPL-2 | GPL-3

Issues

Pull Requests

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Repository

https://github.com/amices/mice

Homepage

https://amices.org/mice/

Maintainer

Stef van Buuren

Last Published

November 14th, 2020

Functions in mice (3.12.0)

Compare two nested models using D2-statistic

as.mitml.result

Converts into a mitml.result object

ampute.default.type

Default type in ampute()

Appends specified break to the data

Computes least squares parameters

Compare several nested models

ampute.default.odds

Default odds in ampute()

Extends a formula with predictors

Employee selection data

Combine R objects by rows and columns

Growth of Dutch boys

Influx and outflux of multivariate missing data patterns

extend.formulas

Extends formula's with predictor matrix settings

Conditional imputation helper

Creates a blocks argument

Incomplete case indicator

Combine mids objects by columns

Fluxplot of the missing data pattern

ampute.default.patterns

Default patterns in ampute

Creates a blots argument

Fraction of incomplete cases among cases with observed

Select complete cases

Fifth Dutch growth study 2009

ampute.continuous

Multivariate amputation based on continuous probability functions

Subset rows of a mids object

Glance method to extract information from a `mipo` object

Check for mira object

Check for mipo object

Creates a post argument

Generalized linear model for mids object

Create a mira object from repeated analyses

ampute.default.weights

Default weights in ampute

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

mice: Multivariate Imputation by Chained Equations

mice.impute.2l.norm

Imputation by a two-level normal model

Graphical parameter for missing data plots

Complete case indicator

mice.impute.2l.pan

Imputation by a two-level normal model using pan

Extracts the completed data from a mids object

make.predictorMatrix

Creates a predictorMatrix argument

ampute.default.freq

Default freq in ampute

Brandsma school data used Snijders and Bosker (2012)

Fix coefficients and update model

Check for mads object

make.visitSequence

Creates a visitSequence argument

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.lda

Imputation by linear discriminant analysis

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

Enlarge number of imputations by combining mids objects

Creates a where argument

Select incomplete cases

construct.blocks

Construct blocks from formulas and predictorMatrix

Extract broken stick estimates from a lmer object

densityplot.mids

Density plot of observed and imputed data

Creates a formulas argument

Creates a method argument

Missing data pattern by variable pairs

Missing data pattern

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.2lonly.mean

Imputation of most likely value within the class

SE Fireworks disaster data

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

Imputation by the random indicator method for nonignorable data

mice.impute.norm.predict

Imputation by linear regression through prediction

mice.impute.logreg

Imputation by logistic regression

Extract estimate from mipo object

Extract list of fitted models

mice.impute.sample

Imputation by simple random sampling

mice.impute.panImpute

Impute multilevel missing data using pan

Multiply imputed data set (mids)

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

Export mids object to Mplus

mice.impute.quadratic

Imputation of quadratic terms

Imputation by random forests

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Check for mids object

Find index of matched donor units

is.mitml.result

Check for mitml.result object

Export mids object to SPSS

Mammal sleep data

Leiden 85+ study

mipo: Multiple imputation pooled object

Name imputation blocks

mice.impute.cart

Imputation by classification and regression trees

Linear regression for mids object

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

Draws values of beta and sigma by Bayesian linear regression

Wrapper function that runs MICE in parallel

Datasets with various missing data patterns

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS

Multivariate amputed data set (mads)

mice.impute.mean

Imputation by the mean

Terneuzen birth cohort

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.passive

Passive imputation

Multiply imputed repeated analyses (mira)

mice.impute.pmm

Imputation by predictive mean matching

Plot the trace lines of the MICE algorithm

Name formula list elements

Subset of Irish wind speed data

mice.impute.polr

Imputation of ordered data by polytomous regression

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Tidy method to extract results from a `mipo` object

Multivariate Imputation by Chained Equations (Iteration Step)

Number of complete cases

Pools R^2 of m models fitted to multiply-imputed data

Compare two nested models fitted to imputed data

Evaluate an expression in multiple imputed datasets

Number of incomplete cases

Multiple imputation pooling: univariate version

Set the theme for the plotting Trellis functions

Cumulative hazard rate or Nelson-Aalen estimator

Hox pupil popularity data with missing popularity scores

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

Number of imputations per block

NHANES example - all variables numerical

Print a mids object

Combine estimates by Rubin's rules

Print a mads object

Quick selection of predictors from the data

Potthoff-Roy data

Objects exported from other packages

Self-reported and measured BMI

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

Squeeze the imputed values to be within specified boundaries.

Combine mids objects by rows

Echoes the package version number

NHANES example - mixed numerical and discrete variables

Stripplot of observed and imputed data

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

Walking disability data

Summary of a mira object

supports.transparent

Supports semi-transparent foreground colors?

Scatterplot of observed and imputed data

Box-and-whisker plot of observed and imputed data

Compare two nested models using D3-statistic

Generate missing data for simulation purposes

Compare two nested models using D1-statistic

ampute.discrete

Multivariate amputation based on discrete probability functions

Multivariate amputation under a MCAR mechanism