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mice (version 3.6.0)

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

44,040

Version

3.6.0

License

GPL-2 | GPL-3

Homepage

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

Maintainer

Stef van Buuren

Last Published

July 10th, 2019

Functions in mice (3.6.0)

Compare two nested models using D1-statistic

ampute.default.freq

Default freq in ampute

ampute.default.odds

Default odds in ampute()

ampute.default.type

Default type in ampute()

Compare two nested models using D2-statistic

ampute.default.weights

Default weights in ampute

ampute.default.patterns

Default patterns in ampute

Compare two nested models using D3-statistic

ampute.continuous

Multivariate Amputation Based On Continuous Probability Functions

Generate Missing Data for Simulation Purposes

ampute.discrete

Multivariate Amputation Based On Discrete Probability Functions

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

Create a mira object from repeated analyses

Appends specified break to the data

Compare several nested models

Multivariate Amputation In A MCAR Manner

Employee selection data

Computes least squares parameters

as.mitml.result

Converts into a mitml.result object

Combine mids objects by columns

Growth of Dutch boys

Select complete cases

Combine R Objects by Rows and Columns

Extends a formula with predictors

Box-and-whisker plot of observed and imputed data

densityplot.mids

Density plot of observed and imputed data

construct.blocks

Construct blocks from formulas and predictorMatrix

Fluxplot of the missing data pattern

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

Brandsma school data used Snijders and Bosker (2012)

Extract broken stick estimates from a lmer object

Complete case indicator

SE Fireworks disaster data

Fifth Dutch growth study 2009

Fraction of incomplete cases among cases with observed

Enlarge number of imputations by combining mids objects

Incomplete case indicator

extend.formulas

Extends formula's with predictor matrix settings

Check for mipo object

Check for mira object

Creates a blocks argument

Conditional imputation helper

Extract estimate from mipo object

Extracts the completed data from a mids object

Generalized linear model for mids object

Creates a blots argument

Creates a formulas argument

Extract list of fitted model

Select incomplete cases

is.mitml.result

Check for mitml.result object

Leiden 85+ study

mice.impute.2l.lmer

Imputation by a two-level normal model using lmer

Graphical parameter for missing data plots.

Missing data pattern

mice.impute.lda

Imputation by linear discriminant analysis

Fix coefficients and update model

Influx and outflux of multivariate missing data patterns

Check for mids object

Check for mads object

Creates a post argument

mice.impute.norm.boot

Imputation by linear regression, bootstrap method

mice.impute.logreg

Imputation by logistic regression

mice.impute.2l.pan

Imputation by a two-level normal model using pan

mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection

Linear regression for mids object

mice.impute.2lonly.mean

Imputation of the mean within the class

mice.impute.2l.norm

Imputation by a two-level normal model

make.visitSequence

Creates a visitSequence argument

Creates a method argument

Creates a where argument

mice: Multivariate Imputation by Chained Equations

Multivariate Amputed Data Set (mads)

mice.impute.norm.nob

Imputation by linear regression without parameter uncertainty

mice.impute.quadratic

Imputation of quadratic terms

Imputation by random forests

Imputation by the random indicator method for nonignorable data

Number of imputations per block

mice.impute.sample

Imputation by simple random sampling

Draws values of beta and sigma by Bayesian linear regression

mice.impute.norm

Imputation by Bayesian linear regression

Datasets with various missing data patterns

Wrapper function that runs MICE in parallel

mice.impute.2l.bin

Imputation by a two-level logistic model using glmer

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.logreg.boot

Imputation by logistic regression using the bootstrap

mice.impute.pmm

Imputation by predictive mean matching

Set the theme for the plotting Trellis functions

mice.impute.passive

Passive imputation

NHANES example - mixed numerical and discrete variables

Multivariate Imputation by Chained Equations (Iteration Step)

Summary of a mira object

supports.transparent

Supports semi-transparent foreground colors?

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

mice.impute.mean

Imputation by the mean

make.predictorMatrix

Creates a predictorMatrix argument

mice.impute.cart

Imputation by classification and regression trees

mice.impute.norm.predict

Imputation by linear regression through prediction

Terneuzen birth cohort

NHANES example - all variables numerical

Export mids object to Mplus

mice.impute.panImpute

Impute multilevel missing data using pan

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

Multiply imputed data set (mids)

Cumulative hazard rate or Nelson-Aalen estimator

Plot the trace lines of the MICE algorithm

Number of incomplete cases

Combine estimates by Rubin's rules

Mammal sleep data

Compare two nested models fitted to imputed data

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

Scatterplot of observed and imputed data

mice.impute.polyreg

Imputation of unordered data by polytomous regression

Missing data pattern by variable pairs

Name imputation blocks

mice.impute.polr

Imputation of ordered data by polytomous regression

Multiply imputed repeated analyses (mira)

Export mids object to SPSS

mipo: Multiple imputation pooled object

mice.impute.jomoImpute

Multivariate multilevel imputation using jomo

Pooling: R squared

Echoes the package version number

Potthoff-Roy data

Walking disability data

Print a mids object

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

Combine mids objects by rows

Name formula list elements

Hox pupil popularity data with missing popularity scores

Number of complete cases

Evaluate an expression in multiple imputed datasets

Self-reported and measured BMI

Subset of Irish wind speed data

Multiple imputation pooling: univariate version

Print a mads object

Quick selection of predictors from the data