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mice (version 3.4.0)

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

60,676

Version

3.4.0

License

GPL-2 | GPL-3

Homepage

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

Maintainer

Stef van Buuren

Last Published

March 7th, 2019

Functions in mice (3.4.0)

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

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

Brandsma school data used Snijders and Bosker (2012)

Combine mids objects by columns

Select complete cases

Extract broken stick estimates from a lmer object

Compare two nested models using D1-statistic

Compare two nested models using D2-statistic

SE Fireworks disaster data

Complete case indicator

Extracts the completed data from a mids object

Extends a formula with predictors

ampute.default.type

Default type in ampute()

ampute.default.weights

Default weights in ampute

Compare several nested models

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Enlarge number of imputations by combining mids objects

extend.formulas

Extends formula's with predictor matrix settings

Appends specified break to the data

Fix coefficients and update model

Select incomplete cases

Check for mads object

Check for mids object

Creates a blots argument

Creates a blocks argument

make.visitSequence

Creates a visitSequence argument

Creates a where argument

Multivariate Amputation In A MCAR Manner

mice.impute.lda

Imputation by linear discriminant analysis

as.mitml.result

Converts into a mitml.result object

ampute.default.odds

Default odds in ampute()

mice.impute.logreg

Imputation by logistic regression

ampute.default.patterns

Default patterns in ampute

Creates a formulas argument

Creates a method argument

Mammal sleep data

mice.impute.mean

Imputation by the mean

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

Missing data pattern by variable pairs

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

Influx and outflux of multivariate missing data patterns

mice.impute.quadratic

Imputation of quadratic terms

Imputation by random forests

NHANES example - mixed numerical and discrete variables

Set the theme for the plotting Trellis functions

mice.impute.norm.predict

Imputation by linear regression through prediction

mice.impute.panImpute

Impute multilevel missing data using pan

Multivariate Imputation by Chained Equations (Iteration Step)

Combine R Objects by Rows and Columns

Box-and-whisker plot of observed and imputed data

Extract estimate from mipo object

Number of incomplete cases

Fifth Dutch growth study 2009

Incomplete case indicator

Fraction of incomplete cases among cases with observed

Conditional imputation helper

Wrapper function that runs MICE in parallel

Cumulative hazard rate or Nelson-Aalen estimator

NHANES example - all variables numerical

Growth of Dutch boys

Plot the trace lines of the MICE algorithm

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

Computes least squares parameters

Generalized linear model for mids object

is.mitml.result

Check for mitml.result object

Employee selection data

Leiden 85+ study

Evaluate an expression in multiple imputed datasets

mice: Multivariate Imputation by Chained Equations

Datasets with various missing data patterns

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

mice.impute.cart

Imputation by classification and regression trees

Multivariate Amputed Data Set (mads)

Linear regression for mids object

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

Summary of a mira object

mice.impute.passive

Passive imputation

mice.impute.pmm

Imputation by predictive mean matching

supports.transparent

Supports semi-transparent foreground colors?

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

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice.impute.2lonly.mean

Imputation of the mean within the class

Compare two nested models using D3-statistic

Multiply imputed data set (mids)

mice.impute.polr

Imputation of ordered data by polytomous regression

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

mice.impute.2l.norm

Imputation by a two-level normal model

Export mids object to Mplus

Generate Missing Data for Simulation Purposes

mice.impute.polyreg

Imputation of unordered data by polytomous regression

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

Walking disability data

mice.impute.norm

Imputation by Bayesian linear regression

Imputation by the random indicator method for nonignorable data

Subset of Irish wind speed data

mice.impute.sample

Imputation by simple random sampling

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

Export mids object to SPSS

Create a mira object from repeated analyses

mipo: Multiple imputation pooled object

Name formula list elements

construct.blocks

Construct blocks from formulas and predictorMatrix

Combine estimates by Rubin's rules

Number of complete cases

Number of imputations per block

Potthoff-Roy data

Draws values of beta and sigma by Bayesian linear regression

Hox pupil popularity data with missing popularity scores

densityplot.mids

Density plot of observed and imputed data

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

Compare two nested models fitted to imputed data

Print a mids object

Terneuzen birth cohort

Combine mids objects by rows

Self-reported and measured BMI

Fluxplot of the missing data pattern

Extract list of fitted model

Echoes the package version number

Check for mipo object

Check for mira object

Creates a post argument

make.predictorMatrix

Creates a predictorMatrix argument

Print a mads object

Missing data pattern

Graphical parameter for missing data plots.

Quick selection of predictors from the data

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

Name imputation blocks

Multiply imputed repeated analyses (mira)

Pooling: R squared

Multiple imputation pooling: univariate version

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

Scatterplot of observed and imputed data

ampute.default.freq

Default freq in ampute