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mice (version 3.1.0)

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

63,066

Version

3.1.0

License

GPL-2 | GPL-3

Homepage

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

Maintainer

Stef van Buuren

Last Published

June 20th, 2018

Functions in mice (3.1.0)

Compare two nested models using D1-statistic

Compare two nested models using D2-statistic

Brandsma school data used Snijders and Bosker (2012)

ampute.default.patterns

Default patterns in ampute

Growth of Dutch boys

ampute.default.odds

Default odds in ampute()

Complete case indicator

as.mitml.result

Converts into a mitml.result object

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

Fix coefficients and update model

Extracts the completed data from a mids object

Box-and-whisker plot of observed and imputed data

Influx and outflux of multivariate missing data patterns

Combine R Objects by Rows and Columns

Fifth Dutch growth study 2009

Enlarge number of imputations by combining mids objects

Select incomplete cases

Linear regression for mids object

Multivariate Amputed Data Set (mads)

make.visitSequence

Creates a visitSequence argument

Fraction of incomplete cases among cases with observed

Extract estimate from mipo object

Generalized linear model for mids object

Creates a formulas argument

Creates a where argument

Creates a method argument

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice: Multivariate Imputation by Chained Equations

mice.impute.2lonly.mean

Imputation of the mean within the class

ampute.default.type

Default type in ampute()

ampute.default.weights

Default weights in ampute

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

Create a mira object from repeated analyses

Compare two nested models using D3-statistic

Extract broken stick estimates from a lmer object

Multivariate Amputation In A MCAR Manner

Generate Missing Data for Simulation Purposes

Employee selection data

Computes least squares parameters

SE Fireworks disaster data

Incomplete case indicator

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

Conditional imputation helper

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

mice.impute.norm.predict

Imputation by linear regression through prediction

construct.blocks

Construct blocks from formulas and predictorMatrix

mice.impute.panImpute

Impute multilevel missing data using pan

Creates a blots argument

Creates a blocks argument

Multivariate Imputation by Chained Equations (Iteration Step)

mice.impute.mean

Imputation by the mean

mice.impute.passive

Passive imputation

Fluxplot of the missing data pattern

densityplot.mids

Density plot of observed and imputed data

Set the theme for the plotting Trellis functions

Extract list of fitted model

Multiply imputed data set (mids)

mice.impute.pmm

Imputation by predictive mean matching

Export mids object to Mplus

Number of imputations per block

mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap

Number of complete cases

Name formula list elements

Hox pupil popularity data with missing popularity scores

Missing data pattern

Check for mipo object

Check for mira object

Graphical parameter for missing data plots.

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

is.mitml.result

Check for mitml.result object

Combine mids objects by rows

Leiden 85+ study

Mammal sleep data

Self-reported and measured BMI

Echoes the package version number

Missing data pattern by variable pairs

mice.impute.lda

Imputation by linear discriminant analysis

Terneuzen birth cohort

Draws values of beta and sigma by Bayesian linear regression

mice.impute.logreg

Imputation by logistic regression

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

Pooling: R squared

Multiple imputation pooling: univariate version

mice.impute.cart

Imputation by classification and regression trees

Summary of a mira object

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.polr

Imputation of ordered data by polytomous regression

supports.transparent

Supports semi-transparent foreground colors?

Imputation by the random indicator method for nonignorable data

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Export mids object to SPSS

mice.impute.sample

Imputation by simple random sampling

Cumulative hazard rate or Nelson-Aalen estimator

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

mipo: Multiple imputation pooled object

Multiply imputed repeated analyses (mira)

NHANES example - all variables numerical

Combine estimates by Rubin's rules

Compare two nested models fitted to imputed data

ampute.default.freq

Default freq in ampute

Name imputation blocks

Compare several nested models

Squeeze the imputed values to be within specified boundaries.

Plot the trace lines of the MICE algorithm

Appends specified break to the data

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

Combine mids objects by columns

Scatterplot of observed and imputed data

Stripplot of observed and imputed data

Print a mads object

Quick selection of predictors from the data

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

Evaluate an expression in multiple imputed datasets

Select complete cases

Extends a formula with predictors

Check for mids object

Check for mads object

extend.formulas

Extends formula's with predictor matrix settings

Creates a post argument

make.predictorMatrix

Creates a predictorMatrix argument

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

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.2l.norm

Imputation by a two-level normal model

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

mice.impute.norm

Imputation by Bayesian linear regression

mice.impute.quadratic

Imputation of quadratic terms

Imputation by random forests

Number of incomplete cases

NHANES example - mixed numerical and discrete variables

Wrapper function that runs MICE in parallel

Datasets with various missing data patterns

Print a mids object

Walking disability data

Potthoff-Roy data

Subset of Irish wind speed data