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mice (version 2.22)

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

65,189

Version

2.22

License

GPL-2 | GPL-3

Maintainer

Stef van Buuren

Last Published

June 11th, 2014

Functions in mice (2.22)

fluxplot

Fluxplot of the missing data pattern
md.pattern

Missing data pattern
cc

Complete cases
bwplot.mids

Box-and-whisker plot of observed and imputed data
icn

Incomplete cases n
ici

Incomplete case indicator
lm.mids

Linear regression for mids object
mice.impute.quadratic

Imputation of quadratric terms
mice.impute.fastpmm

Imputation by fast predictive mean matching
mammalsleep

Mammal sleep data
mice.impute.norm.nob

Imputation by linear regression (non Bayesian)
mice.impute.norm

Imputation by Bayesian linear regression
as.mids

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

Set the theme for the plotting Trellis functions
ccn

Complete cases n
nhanes2

NHANES example - mixed numerical and discrete variables
potthoffroy

Potthoff-Roy data
pool

Multiple imputation pooling
with.mids

Evaluate an expression in multiple imputed datasets
mice.impute.passive

Passive imputation
as.mira

Create a mira object from repeated analyses
mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap
is.mipo

Check for mipo object
mice.impute.rf

Imputation by random forests
quickpred

Quick selection of predictors from the data
mids2mplus

Export mids object to Mplus
boys

Growth of Dutch boys
cci

Complete case indicator
is.mids

Check for mids object
fdgs

Fifth Dutch growth study 2009
rbind.mids

Rowwise combination of a mids object.
mira-class

Multiply imputed repeated analyses (mira)
mipo-class

Multiply imputed pooled analysis (mipo)
plot.mids

Plot the trace lines of the MICE algorithm
mice.impute.2l.norm

Imputation by a two-level normal model
nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator
pool.scalar

Multiple imputation pooling: univariate version
densityplot.mids

Density plot of observed and imputed data
pattern

Datasets with various missing data patterns
squeeze

Squeeze the imputed values to be within specified boundaries.
pool.compare

Compare two nested models fitted to imputed data
mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching
md.pairs

Missing data pattern by variable pairs
selfreport

Self-reported and measured BMI
ibind

Combine imputations fitted to the same data
popmis

Hox pupil popularity data with missing popularity scores
appendbreak

Appends specified break to the data
ifdo

Conditional imputation helper
summary.mira

Summary of a mira object
fico

Fraction of incomplete cases among cases with observed
mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression
complete

Creates imputed data sets from a mids object
windspeed

Subset of Irish wind speed data
mice.impute.sample

Imputation by simple random sampling
mice.mids

Multivariate Imputation by Chained Equations (Iteration Step)
leiden85

Leiden 85+ study
mice.impute.mean

Imputation by the mean
mice.impute.polyreg

Imputation by polytomous regression - unordered
print.mids

Print a mids object
nhanes

NHANES example - all variables numerical
is.mira

Check for mira object
supports.transparent

Supports semi-transparent foreground colors?
mice.impute.norm.predict

Imputation by linear regression, prediction method
mdc

Graphical parameter for missing data plots.
flux

Influx and outflux of multivariate missing data patterns
extractBS

Extract broken stick estimates from a lmer object
mice.impute.pmm

Imputation by predictive mean matching
mice.impute.ri

Imputation by the random indicator method for nonignorable data
tbc

Terneuzen birth cohort
fdd

SE Fireworks disaster data
ic

Incomplete cases
mice.impute.2l.pan

Imputation by a two-level normal model using pan
mice

Multivariate Imputation by Chained Equations (MICE)
norm.draw

Draws values of beta and sigma by Bayesian linear regression
pool.r.squared

Pooling: R squared
walking

Walking disability data
getfit

Extracts fit objects from mira object
mice.impute.logreg

Imputation by logistic regression
mice.impute.cart

Imputation by classification and regression trees
mice.impute.polr

Imputation by polytomous regression - ordered
mice.impute.2lonly.mean

Imputation of the mean within the class
cbind.mids

Columnwise combination of a mids object.
mids-class

Multiply imputed data set (mids)
stripplot.mids

Stripplot of observed and imputed data
xyplot.mids

Scatterplot of observed and imputed data
mids2spss

Export mids object to SPSS
glm.mids

Generalized linear model for mids object
long2mids

Conversion of a imputed data set (long form) to a mids object
mice.impute.lda

Imputation by linear discriminant analysis
mice.impute.norm.boot

Imputation by linear regression, bootstrap method
pops

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

Echoes the package version number