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mice (version 2.15)

Multivariate Imputation by Chained Equations

Description

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. 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

106,176

Version

2.15

License

GPL-2 | GPL-3

Maintainer

Stef van Buuren

Last Published

April 2nd, 2013

Functions in mice (2.15)

flux

Influx and outflux of multivariate missing data patterns
ibind

Combine imputations fitted to the same data
mice.impute.norm

Imputation by Bayesian linear regression
with.mids

Evaluate an expression in multiple imputed datasets
fico

Fraction of incomplete cases among cases with observed
ccn

Complete cases n
lm.mids

Linear regression for mids object
mdc

Graphical parameter for missing data plots.
mice.impute.norm.boot

Imputation by linear regression, bootstrap method
mice.impute.logreg

Imputation by logistic regression
boys

Growth of Dutch boys
long2mids

Conversion of a imputed data set (long form) to a mids object
mids2spss

Export mids object to SPSS
ic

Incomplete cases
mice.theme

Set the theme for the plotting Trellis functions
nhanes

NHANES example - all variables numerical
walking

Walking disability data
md.pattern

Missing data pattern
pops

Project on preterm and small for gestational age infants (POPS)
mice.impute.2lonly.mean

Imputation of the mean within the class
as.mira

Create a mira object from repeated analyses
cci

Complete case indicator
mice.impute.norm.nob

Imputation by linear regression (non Bayesian)
cbind.mids

Columnwise combination of a mids object.
extractBS

Extract broken stick estimates from a lmer object
bwplot.mids

Box-and-whisker plot of observed and imputed data
mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching
mammalsleep

Mammal sleep data
version

Echoes the package version number
pool

Multiple imputation pooling
mira-class

Multiply imputed repeated analyses (mira)
getfit

Extracts fit objects from mira object
leiden85

Leiden 85+ study
quickpred

Quick selection of predictors from the data
ici

Incomplete case indicator
supports.transparent

Supports semi-transparent foreground colors?
cc

Complete cases
mids-class

Multiply imputed data set (mids)
print.mids

Print a mids object
mice.impute.2l.pan

Imputation by a two-level normal model using pan
mice.impute.cart

Imputation by classification and regression trees
mice.impute.norm.predict

Imputation by linear regression, prediction method
mice.impute.2l.norm

Imputation by a two-level normal model
nhanes2

NHANES example - mixed numerical and discrete variables
icn

Incomplete cases n
xyplot.mids

Scatterplot of observed and imputed data
mids2mplus

Export mids object to Mplus
summary.mira

Summary of a mira object
ifdo

Conditional imputation helper
pool.scalar

Multiple imputation pooling: univariate version
mice.impute.quadratic

Imputation of quadratric terms
fdgs

Fifth Dutch growth study 2009
windspeed

Subset of Irish wind speed data
tbc

Terneuzen birth cohort
mice.impute.passive

Passive imputation
fdd

SE Fireworks disaster data
mice.impute.lda

Imputation by linear discriminant analysis
mice.impute.polr

Imputation by polytomous regression - ordered
densityplot.mids

Density plot of observed and imputed data
mice.impute.sample

Imputation by simple random sampling
plot.mids

Plot the trace lines of the MICE algorithm
mipo-class

Multiply imputed pooled analysis (mipo)
mice.impute.polyreg

Imputation by polytomous regression - unordered
squeeze

Squeeze the imputed values to be within specified boundaries.
pool.r.squared

Pooling: R squared
complete

Creates imputed data sets from a mids object
glm.mids

Generalized linear model for mids object
nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator
rbind.mids

Rowwise combination of a mids object.
md.pairs

Missing data pattern by variable pairs
mice

Multivariate Imputation by Chained Equations (MICE)
popmis

Hox pupil popularity data with missing popularity scores
mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression
selfreport

Self-reported and measured BMI
potthoffroy

Potthoff-Roy data
stripplot.mids

Stripplot of observed and imputed data
fluxplot

Fluxplot of the missing data pattern
mice.impute.mean

Imputation by the mean
mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)
mice.impute.pmm

Imputation by predictive mean matching
appendbreak

Appends specified break to the data
pool.compare

Compare two nested models fitted to imputed data
pattern

Datasets with various missing data patterns