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mice (version 3.11.0)

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

3.11.0

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

August 5th, 2020

Functions in mice (3.11.0)

Compare two nested models using D1-statistic

ampute.default.type

Default type in ampute()

ampute.default.patterns

Default patterns in ampute

Compare two nested models using D2-statistic

Generate Missing Data for Simulation Purposes

Compare two nested models using D3-statistic

ampute.default.weights

Default weights in ampute

ampute.default.freq

Default freq in ampute

ampute.default.odds

Default odds in ampute()

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Compare several nested models

construct.blocks

Construct blocks from formulas and predictorMatrix

Appends specified break to the data

Brandsma school data used Snijders and Bosker (2012)

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

Extends a formula with predictors

Complete case indicator

extend.formulas

Extends formula's with predictor matrix settings

Extract broken stick estimates from a lmer object

as.mitml.result

Converts into a mitml.result object

Extracts the completed data from a mids object

densityplot.mids

Density plot of observed and imputed data

SE Fireworks disaster data

Check for mira object

Select incomplete cases

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

Creates a blots argument

Create a mira object from repeated analyses

Fifth Dutch growth study 2009

Box-and-whisker plot of observed and imputed data

is.mitml.result

Check for mitml.result object

Combine R Objects by Rows and Columns

Conditional imputation helper

Fluxplot of the missing data pattern

Fraction of incomplete cases among cases with observed

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

Graphical parameter for missing data plots.

Multivariate Amputation In A MCAR Manner

Incomplete case indicator

Employee selection data

mice: Multivariate Imputation by Chained Equations

Check for mads object

Fix coefficients and update model

Check for mids object

Check for mipo object

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Extract list of fitted model

Combine mids objects by columns

Growth of Dutch boys

Multivariate Amputed Data Set (mads)

Leiden 85+ study

Computes least squares parameters

Generalized linear model for mids object

mice.impute.lda

Imputation by linear discriminant analysis

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

Imputation by random forests

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.quadratic

Imputation of quadratic terms

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression

Creates a formulas argument

mice.impute.passive

Passive imputation

Missing data pattern

mice.impute.pmm

Imputation by predictive mean matching

mice.impute.2lonly.mean

Imputation of most likely value within the class

Select complete cases

Export mids object to SPSS

Extract estimate from mipo object

Influx and outflux of multivariate missing data patterns

Glance method to extract information from a `mipo` object

Creates a method argument

Creates a blocks argument

make.predictorMatrix

Creates a predictorMatrix argument

make.visitSequence

Creates a visitSequence argument

mipo: Multiple imputation pooled object

Mammal sleep data

mice.impute.2l.pan

Imputation by a two-level normal model using pan

Compare two nested models fitted to imputed data

Pooling: R squared

Missing data pattern by variable pairs

mice.impute.panImpute

Impute multilevel missing data using pan

mice.impute.sample

Imputation by simple random sampling

mice.impute.norm.predict

Imputation by linear regression through prediction

mice.impute.logreg

Imputation by logistic regression

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS

Imputation by the random indicator method for nonignorable data

mice.impute.polr

Imputation of ordered data by polytomous regression

Print a mids object

Cumulative hazard rate or Nelson-Aalen estimator

Print a mads object

mice.impute.norm

Imputation by Bayesian linear regression

NHANES example - all variables numerical

Export mids object to Mplus

Multiply imputed data set (mids)

Multiply imputed repeated analyses (mira)

Name formula list elements

Enlarge number of imputations by combining mids objects

Echoes the package version number

Objects exported from other packages

Linear regression for mids object

Self-reported and measured BMI

Walking disability data

mice.impute.polyreg

Imputation of unordered data by polytomous regression

mice.impute.mean

Imputation by the mean

Number of incomplete cases

Creates a post argument

Name imputation blocks

Draws values of beta and sigma by Bayesian linear regression

Wrapper function that runs MICE in parallel

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.2l.norm

Imputation by a two-level normal model

Multiple imputation pooling: univariate version

Datasets with various missing data patterns

NHANES example - mixed numerical and discrete variables

Plot the trace lines of the MICE algorithm

Hox pupil popularity data with missing popularity scores

Number of complete cases

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching

Creates a where argument

mice.impute.cart

Imputation by classification and regression trees

Quick selection of predictors from the data

Set the theme for the plotting Trellis functions

Multivariate Imputation by Chained Equations (Iteration Step)

Number of imputations per block

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

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

Combine estimates by Rubin's rules

Potthoff-Roy data

Combine mids objects by rows

Summary of a mira object

supports.transparent

Supports semi-transparent foreground colors?

Subset of Irish wind speed data

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

Evaluate an expression in multiple imputed datasets

Terneuzen birth cohort

Scatterplot of observed and imputed data

Tidy method to extract results from a `mipo` object