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mice (version 3.3.0)

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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Version

Install

install.packages('mice')

Monthly Downloads

83,091

Version

3.3.0

License

GPL-2 | GPL-3

Homepage

http://stefvanbuuren.github.io/mice/

Maintainer

Stef van Buuren

Last Published

July 27th, 2018

Functions in mice (3.3.0)

Complete case indicator

Extract broken stick estimates from a lmer object

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute.default.freq

Default freq in ampute

Employee selection data

SE Fireworks disaster data

Compare two nested models using D3-statistic

Generate Missing Data for Simulation Purposes

ampute.default.odds

Default odds in ampute()

Computes least squares parameters

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

Create a mira object from repeated analyses

Extends a formula with predictors

Extract estimate from mipo object

Fluxplot of the missing data pattern

extend.formulas

Extends formula's with predictor matrix settings

ampute.default.patterns

Default patterns in ampute

Enlarge number of imputations by combining mids objects

as.mitml.result

Converts into a mitml.result object

Growth of Dutch boys

Box-and-whisker plot of observed and imputed data

Select incomplete cases

Missing data pattern

Graphical parameter for missing data plots.

Combine R Objects by Rows and Columns

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.norm

Imputation by Bayesian linear regression

Multiply imputed data set (mids)

ampute.default.type

Default type in ampute()

Fifth Dutch growth study 2009

Fraction of incomplete cases among cases with observed

ampute.default.weights

Default weights in ampute

Export mids object to Mplus

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Pooling: R squared

Multiple imputation pooling: univariate version

Summary of a mira object

Multivariate Amputation In A MCAR Manner

construct.blocks

Construct blocks from formulas and predictorMatrix

Generalized linear model for mids object

densityplot.mids

Density plot of observed and imputed data

Extract list of fitted model

Creates a formulas argument

Check for mads object

supports.transparent

Supports semi-transparent foreground colors?

Incomplete case indicator

Brandsma school data used Snijders and Bosker (2012)

Scatterplot of observed and imputed data

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

Check for mids object

Conditional imputation helper

Fix coefficients and update model

Creates a post argument

is.mitml.result

Check for mitml.result object

Combine mids objects by columns

Leiden 85+ study

make.predictorMatrix

Creates a predictorMatrix argument

mice: Multivariate Imputation by Chained Equations

Creates a method argument

Select complete cases

Influx and outflux of multivariate missing data patterns

Linear regression for mids object

make.visitSequence

Creates a visitSequence argument

Multivariate Amputed Data Set (mads)

Check for mipo object

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

Mammal sleep data

Creates a where argument

Missing data pattern by variable pairs

Check for mira object

Creates a blocks argument

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.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Creates a blots argument

mice.impute.cart

Imputation by classification and regression trees

mice.impute.2l.norm

Imputation by a two-level normal model

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.panImpute

Impute multilevel missing data using pan

mice.impute.mean

Imputation by the mean

mice.impute.norm.predict

Imputation by linear regression through prediction

mice.impute.polr

Imputation of ordered data by polytomous regression

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.2lonly.mean

Imputation of the mean within the class

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.passive

Passive imputation

mice.impute.quadratic

Imputation of quadratic terms

Set the theme for the plotting Trellis functions

Multivariate Imputation by Chained Equations (Iteration Step)

mice.impute.pmm

Imputation by predictive mean matching

Imputation by random forests

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Export mids object to SPSS

mipo: Multiple imputation pooled object

mice.impute.logreg

Imputation by logistic regression

Cumulative hazard rate or Nelson-Aalen estimator

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

Imputation by the random indicator method for nonignorable data

Name formula list elements

Multiply imputed repeated analyses (mira)

mice.impute.sample

Imputation by simple random sampling

Number of imputations per block

Number of complete cases

Print a mads object

NHANES example - mixed numerical and discrete variables

Number of incomplete cases

Quick selection of predictors from the data

Draws values of beta and sigma by Bayesian linear regression

NHANES example - all variables numerical

Wrapper function that runs MICE in parallel

Name imputation blocks

Plot the trace lines of the MICE algorithm

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

Hox pupil popularity data with missing popularity scores

Datasets with various missing data patterns

Terneuzen birth cohort

Echoes the package version number

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

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

Walking disability data

Subset of Irish wind speed data

Combine estimates by Rubin's rules

Print a mids object

Potthoff-Roy data

Compare two nested models fitted to imputed data

Combine mids objects by rows

Self-reported and measured BMI

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

Evaluate an expression in multiple imputed datasets

Compare two nested models using D1-statistic

Compare two nested models using D2-statistic

Compare several nested models

Appends specified break to the data

Extracts the completed data from a mids object