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mice (version 3.13.0)

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

74,074

Version

3.13.0

License

GPL-2 | GPL-3

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Last Published

January 27th, 2021

Functions in mice (3.13.0)

ampute.default.freq

Default freq in ampute
D2

Compare two nested models using D2-statistic
D3

Compare two nested models using D3-statistic
D1

Compare two nested models using D1-statistic
ampute.default.patterns

Default patterns in ampute
ampute.default.odds

Default odds in ampute()
ampute.default.weights

Default weights in ampute
ampute.continuous

Multivariate amputation based on continuous probability functions
ampute

Generate missing data for simulation purposes
ampute.default.type

Default type in ampute()
as.mitml.result

Converts into a mitml.result object
ampute.discrete

Multivariate amputation based on discrete probability functions
ampute.mcar

Multivariate amputation under a MCAR mechanism
brandsma

Brandsma school data used Snijders and Bosker (2012)
anova.mira

Compare several nested models
appendbreak

Appends specified break to the data
cci

Complete case indicator
boys

Growth of Dutch boys
employee

Employee selection data
as.mids

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

Extracts the completed data from a mids object
flux

Influx and outflux of multivariate missing data patterns
bwplot.mads

Box-and-whisker plot of amputed and non-amputed data
cbind.mids

Combine mids objects by columns
cc

Select complete cases
fluxplot

Fluxplot of the missing data pattern
as.mira

Create a mira object from repeated analyses
extend.formulas

Extends formula's with predictor matrix settings
is.mitml.result

Check for mitml.result object
getqbar

Extract estimate from mipo object
getfit

Extract list of fitted models
extend.formula

Extends a formula with predictors
estimice

Computes least squares parameters
construct.blocks

Construct blocks from formulas and predictorMatrix
leiden85

Leiden 85+ study
bwplot.mids

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

Combine R objects by rows and columns
glance.mipo

Glance method to extract information from a `mipo` object
extractBS

Extract broken stick estimates from a lmer object
make.blocks

Creates a blocks argument
make.blots

Creates a blots argument
glm.mids

Generalized linear model for mids object
is.mira

Check for mira object
ici

Incomplete case indicator
fix.coef

Fix coefficients and update model
is.mipo

Check for mipo object
make.post

Creates a post argument
fdd

SE Fireworks disaster data
filter.mids

Subset rows of a mids object
md.pairs

Missing data pattern by variable pairs
densityplot.mids

Density plot of observed and imputed data
fdgs

Fifth Dutch growth study 2009
ifdo

Conditional imputation helper
mice.impute.panImpute

Impute multilevel missing data using pan
make.predictorMatrix

Creates a predictorMatrix argument
ibind

Enlarge number of imputations by combining mids objects
mice.impute.norm.predict

Imputation by linear regression through prediction
make.visitSequence

Creates a visitSequence argument
make.where

Creates a where argument
matchindex

Find index of matched donor units
mammalsleep

Mammal sleep data
ic

Select incomplete cases
mice.impute.2l.bin

Imputation by a two-level logistic model using glmer
make.formulas

Creates a formulas argument
mice.impute.2lonly.mean

Imputation of most likely value within the class
make.method

Creates a method argument
mice.impute.midastouch

Imputation by predictive mean matching with distance aided donor selection
mice.impute.2lonly.norm

Imputation at level 2 by Bayesian linear regression
mice.impute.mean

Imputation by the mean
md.pattern

Missing data pattern
mice.impute.sample

Imputation by simple random sampling
mice.impute.logreg

Imputation by logistic regression
mice.impute.ri

Imputation by the random indicator method for nonignorable data
mice.impute.2l.lmer

Imputation by a two-level normal model using lmer
is.mads

Check for mads object
mice.impute.2lonly.pmm

Imputation at level 2 by predictive mean matching
is.mids

Check for mids object
fico

Fraction of incomplete cases among cases with observed
mice.impute.cart

Imputation by classification and regression trees
mira-class

Multiply imputed repeated analyses (mira)
lm.mids

Linear regression for mids object
plot.mids

Plot the trace lines of the MICE algorithm
reexports

Objects exported from other packages
pattern

Datasets with various missing data patterns
mice.impute.logreg.boot

Imputation by logistic regression using the bootstrap
selfreport

Self-reported and measured BMI
xyplot.mads

Scatterplot of amputed and non-amputed data against weighted sum scores
nhanes2

NHANES example - mixed numerical and discrete variables
pool.r.squared

Pools R^2 of m models fitted to multiply-imputed data
nhanes

NHANES example - all variables numerical
pool.compare

Compare two nested models fitted to imputed data
mads-class

Multivariate amputed data set (mads)
tidy.mipo

Tidy method to extract results from a `mipo` object
tbc

Terneuzen birth cohort
xyplot.mids

Scatterplot of observed and imputed data
mnar_demo_data

MNAR demo data
mice.impute.jomoImpute

Multivariate multilevel imputation using jomo
mice.impute.passive

Passive imputation
mdc

Graphical parameter for missing data plots
mice.impute.norm

Imputation by Bayesian linear regression
mice.impute.mnar.logreg

Imputation under MNAR mechanism by NARFCS
mice.impute.polr

Imputation of ordered data by polytomous regression
name.blocks

Name imputation blocks
mice.theme

Set the theme for the plotting Trellis functions
mice.mids

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

Imputation by predictive mean matching
mice.impute.lda

Imputation by linear discriminant analysis
mice.impute.2l.norm

Imputation by a two-level normal model
mice

mice: Multivariate Imputation by Chained Equations
mice.impute.2l.pan

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

Imputation by linear regression without parameter uncertainty
mice.impute.norm.boot

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

Imputation of unordered data by polytomous regression
mice.impute.rf

Imputation by random forests
mice.impute.quadratic

Imputation of quadratic terms
parlmice

Wrapper function that runs MICE in parallel
name.formulas

Name formula list elements
norm.draw

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

Multiple imputation pooling: univariate version
ncc

Number of complete cases
mipo

mipo: Multiple imputation pooled object
mids2spss

Export mids object to SPSS
.pmm.match

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

Multiply imputed data set (mids)
nelsonaalen

Cumulative hazard rate or Nelson-Aalen estimator
toenail

Toenail data
popmis

Hox pupil popularity data with missing popularity scores
print.mids

Print a mids object
print.mads

Print a mads object
mids2mplus

Export mids object to Mplus
nic

Number of incomplete cases
pool

Combine estimates by Rubin's rules
pops

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

Potthoff-Roy data
summary.mira

Summary of a mira object
squeeze

Squeeze the imputed values to be within specified boundaries.
supports.transparent

Supports semi-transparent foreground colors?
windspeed

Subset of Irish wind speed data
stripplot.mids

Stripplot of observed and imputed data
toenail2

Toenail data
version

Echoes the package version number
quickpred

Quick selection of predictors from the data
walking

Walking disability data
nimp

Number of imputations per block
with.mids

Evaluate an expression in multiple imputed datasets
rbind.mids

Combine mids objects by rows