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mice (version 3.5.0)

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

3.5.0

License

GPL-2 | GPL-3

Homepage

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

Maintainer

Stef van Buuren

Last Published

May 13th, 2019

Functions in mice (3.5.0)

Compare two nested models using D1-statistic

Compare several nested models

Appends specified break to the data

Complete case indicator

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute.default.freq

Default freq in ampute

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

Extracts the completed data from a mids object

Fluxplot of the missing data pattern

Extract list of fitted model

Create a mira object from repeated analyses

Check for mads object

Check for mids object

is.mitml.result

Check for mitml.result object

Leiden 85+ study

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

construct.blocks

Construct blocks from formulas and predictorMatrix

mice.impute.mean

Imputation by the mean

mice.impute.polr

Imputation of ordered data by polytomous regression

densityplot.mids

Density plot of observed and imputed data

Extends a formula with predictors

mice.impute.polyreg

Imputation of unordered data by polytomous regression

extend.formulas

Extends formula's with predictor matrix settings

Creates a formulas argument

Creates a method argument

Export mids object to SPSS

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.cart

Imputation by classification and regression trees

mice.impute.2l.norm

Imputation by a two-level normal model

ampute.default.odds

Default odds in ampute()

mice.impute.norm.predict

Imputation by linear regression through prediction

ampute.default.patterns

Default patterns in ampute

mice.impute.panImpute

Impute multilevel missing data using pan

mipo: Multiple imputation pooled object

mice.impute.quadratic

Imputation of quadratic terms

Brandsma school data used Snijders and Bosker (2012)

Imputation by random forests

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

Multiply imputed repeated analyses (mira)

Hox pupil popularity data with missing popularity scores

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

Employee selection data

Computes least squares parameters

Print a mads object

Name imputation blocks

Extract broken stick estimates from a lmer object

Combine estimates by Rubin's rules

SE Fireworks disaster data

Quick selection of predictors from the data

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Compare two nested models fitted to imputed data

Potthoff-Roy data

Extract estimate from mipo object

Generalized linear model for mids object

Print a mids object

Compare two nested models using D3-statistic

Terneuzen birth cohort

Creates a blocks argument

ampute.default.type

Default type in ampute()

Creates a blots argument

Generate Missing Data for Simulation Purposes

Echoes the package version number

ampute.default.weights

Default weights in ampute

Multivariate Amputation In A MCAR Manner

as.mitml.result

Converts into a mitml.result object

Box-and-whisker plot of observed and imputed data

Growth of Dutch boys

Enlarge number of imputations by combining mids objects

Combine mids objects by columns

Select complete cases

Select incomplete cases

Combine R Objects by Rows and Columns

Fraction of incomplete cases among cases with observed

Fifth Dutch growth study 2009

Fix coefficients and update model

Multivariate Amputed Data Set (mads)

Linear regression for mids object

Influx and outflux of multivariate missing data patterns

Check for mipo object

make.visitSequence

Creates a visitSequence argument

mice: Multivariate Imputation by Chained Equations

Check for mira object

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

Missing data pattern

Incomplete case indicator

Graphical parameter for missing data plots.

Conditional imputation helper

Creates a where argument

Creates a post argument

make.predictorMatrix

Creates a predictorMatrix argument

mice.impute.passive

Passive imputation

Missing data pattern by variable pairs

Mammal sleep data

mice.impute.pmm

Imputation by predictive mean matching

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.logreg

Imputation by logistic regression

mice.impute.2l.pan

Imputation by a two-level normal model using pan

Multivariate Imputation by Chained Equations (Iteration Step)

Set the theme for the plotting Trellis functions

Number of imputations per block

mice.impute.2lonly.mean

Imputation of the mean within the class

Export mids object to Mplus

Multiply imputed data set (mids)

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

Draws values of beta and sigma by Bayesian linear regression

Plot the trace lines of the MICE algorithm

Name formula list elements

Cumulative hazard rate or Nelson-Aalen estimator

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.norm

Imputation by Bayesian linear regression

Imputation by the random indicator method for nonignorable data

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

NHANES example - all variables numerical

Summary of a mira object

Multiple imputation pooling: univariate version

Pooling: R squared

supports.transparent

Supports semi-transparent foreground colors?

mice.impute.sample

Imputation by simple random sampling

Number of complete cases

NHANES example - mixed numerical and discrete variables

Wrapper function that runs MICE in parallel

Number of incomplete cases

Datasets with various missing data patterns

Combine mids objects by rows

Self-reported and measured BMI

Evaluate an expression in multiple imputed datasets

Scatterplot of observed and imputed data

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

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

Walking disability data

Subset of Irish wind speed data

Compare two nested models using D2-statistic