Package: mice 3.17.0

Stef van Buuren

mice: Multivariate Imputation by Chained Equations

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. 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.

Authors:Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb], Mingyang Cai [ctb], Thom Volker [ctb], Edoardo Costantini [ctb], Caspar van Lissa [ctb], Hanne Oberman [ctb], Stephen Wade [ctb]

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NEWS

# Install 'mice' in R:
install.packages('mice', repos = c('https://amices.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/amices/mice/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • boys - Growth of Dutch boys
  • brandsma - Brandsma school data used Snijders and Bosker
  • employee - Employee selection data
  • fdd - SE Fireworks disaster data
  • fdd.pred - SE Fireworks disaster data
  • fdgs - Fifth Dutch growth study 2009
  • leiden85 - Leiden 85+ study
  • mammalsleep - Mammal sleep data
  • mnar_demo_data - MNAR demo data
  • nhanes - NHANES example - all variables numerical
  • nhanes2 - NHANES example - mixed numerical and discrete variables
  • pattern1 - Datasets with various missing data patterns
  • pattern2 - Datasets with various missing data patterns
  • pattern3 - Datasets with various missing data patterns
  • pattern4 - Datasets with various missing data patterns
  • popmis - Hox pupil popularity data with missing popularity scores
  • pops - Project on preterm and small for gestational age infants
  • pops.pred - Project on preterm and small for gestational age infants
  • potthoffroy - Potthoff-Roy data
  • selfreport - Self-reported and measured BMI
  • tbc - Terneuzen birth cohort
  • tbc.target - Terneuzen birth cohort
  • toenail - Toenail data
  • toenail2 - Toenail data
  • walking - Walking disability data
  • windspeed - Subset of Irish wind speed data

On CRAN:

chained-equationsfcsimputationmicemissing-datamissing-valuesmultiple-imputationmultivariate-data

16.27 score 447 stars 142 packages 8.4k scripts 60k downloads 455 mentions 123 exports 59 dependencies

Last updated 3 hours agofrom:31f5dc2e4b. Checks:OK: 7 NOTE: 2. Indexed: yes.

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Doc / VignettesOKNov 23 2024
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Exports:.norm.draw.pmm.matchamputeampute.continuousampute.default.freqampute.default.oddsampute.default.patternsampute.default.typeampute.default.weightsampute.discreteampute.mcarappendbreakas.midsas.miraas.mitml.resultbwplotcbindccccicompleteconstruct.blocksconvergenceD1D2D3densityplotestimiceextractBSficofilterfix.coeffluxfluxplotfuturemicegetfitgetqbarglanceglm.midsibindiciciis.madsis.midsis.mipois.mirais.mitml.resultlm.midsmake.blocksmake.blotsmake.formulasmake.methodmake.postmake.predictorMatrixmake.visitSequencemake.wherematchindexmcarmd.pairsmd.patternmdcmicemice.impute.2l.binmice.impute.2l.lmermice.impute.2l.normmice.impute.2l.panmice.impute.2lonly.meanmice.impute.2lonly.normmice.impute.2lonly.pmmmice.impute.cartmice.impute.jomoImputemice.impute.lasso.logregmice.impute.lasso.normmice.impute.lasso.select.logregmice.impute.lasso.select.normmice.impute.ldamice.impute.logregmice.impute.logreg.bootmice.impute.meanmice.impute.midastouchmice.impute.mnar.logregmice.impute.mnar.normmice.impute.mpmmmice.impute.normmice.impute.norm.bootmice.impute.norm.nobmice.impute.norm.predictmice.impute.panImputemice.impute.passivemice.impute.pmmmice.impute.polrmice.impute.polyregmice.impute.quadraticmice.impute.rfmice.impute.rimice.impute.samplemice.midsmice.thememids2mplusmids2spssmiponame.blocksname.formulasnccnelsonaalennicnimpnorm.drawparlmicepoolpool.comparepool.r.squaredpool.scalarpool.scalar.synpool.synpool.tablequickpredrbindsqueezestripplotsupports.transparenttidyversionxyplot

Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayondplyrfansiforcatsforeachgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixminqamitmlnlmenloptrnnetnumDerivordinalpanpillarpkgconfigprettyunitsprogresspurrrR6RcppRcppEigenreadrrlangrpartshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr

Readme and manuals

Help Manual

Help pageTopics
Finds an imputed value from matches in the predictive metric (deprecated).pmm.match
Generate missing data for simulation purposesampute
Compare several nested modelsanova.mira
Appends specified break to the dataappendbreak
Converts an imputed dataset (long format) into a 'mids' objectas.mids
Create a 'mira' object from repeated analysesas.mira
Converts into a 'mitml.result' objectas.mitml.result
Growth of Dutch boysboys
Brandsma school data used Snijders and Bosker (2012)brandsma
Box-and-whisker plot of amputed and non-amputed databwplot.mads
Box-and-whisker plot of observed and imputed databwplot bwplot.mids
Combine R objects by rows and columnscbind rbind
Select complete casescc
Complete case indicatorcci
Extracts the completed data from a 'mids' objectcomplete complete.mids
Construct blocks from 'formulas' and 'predictorMatrix'construct.blocks
Computes convergence diagnostics for a 'mids' objectconvergence
Compare two nested models using D1-statisticD1
Compare two nested models using D2-statisticD2
Compare two nested models using D3-statisticD3
Density plot of observed and imputed datadensityplot densityplot.mids
Employee selection dataemployee
Computes least squares parametersestimice
Extract broken stick estimates from a 'lmer' objectextractBS
SE Fireworks disaster datafdd fdd.pred
Fifth Dutch growth study 2009fdgs
Fraction of incomplete cases among cases with observedfico
Subset rows of a 'mids' objectfilter.mids
Fix coefficients and update modelfix.coef
Influx and outflux of multivariate missing data patternsflux
Fluxplot of the missing data patternfluxplot
Wrapper function that runs MICE in parallelfuturemice
Extract list of fitted modelsgetfit
Extract estimate from 'mipo' objectgetqbar
Generalized linear model for 'mids' objectglm.mids
Enlarge number of imputations by combining 'mids' objectsibind
Select incomplete casesic
Incomplete case indicatorici ici,data.frame-method ici,matrix-method ici,mids-method
Check for 'mads' objectis.mads
Check for 'mids' objectis.mids
Check for 'mipo' objectis.mipo
Check for 'mira' objectis.mira
Check for 'mitml.result' objectis.mitml.result
Leiden 85+ studyleiden85
Linear regression for 'mids' objectlm.mids
Multivariate amputed data set ('mads')mads-class
Creates a 'blocks' argumentmake.blocks
Creates a 'blots' argumentmake.blots
Creates a 'formulas' argumentmake.formulas
Creates a 'method' argumentmake.method
Creates a 'post' argumentmake.post
Creates a 'predictorMatrix' argumentmake.predictorMatrix
Creates a 'visitSequence' argumentmake.visitSequence
Creates a 'where' argumentmake.where
Mammal sleep datamammalsleep sleep
Find index of matched donor unitsmatchindex
Missing data pattern by variable pairsmd.pairs
Missing data patternmd.pattern
Graphical parameter for missing data plotsmdc
Imputation by a two-level logistic model using 'glmer'mice.impute.2l.bin
Imputation by a two-level normal model using 'lmer'mice.impute.2l.lmer
Imputation by a two-level normal modelmice.impute.2l.norm
Imputation by a two-level normal model using 'pan'2l.pan mice.impute.2l.pan
Imputation of most likely value within the class2lonly.mean mice.impute.2lonly.mean
Imputation at level 2 by Bayesian linear regression2lonly.norm mice.impute.2lonly.norm
Imputation at level 2 by predictive mean matching2lonly.pmm mice.impute.2lonly.pmm
Imputation by classification and regression treescart mice.impute.cart
Multivariate multilevel imputation using 'jomo'mice.impute.jomoImpute
Imputation by direct use of lasso logistic regressionlasso.logreg mice.impute.lasso.logreg
Imputation by direct use of lasso linear regressionlasso.norm mice.impute.lasso.norm
Imputation by indirect use of lasso logistic regressionlasso.select.logreg mice.impute.lasso.select.logreg
Imputation by indirect use of lasso linear regressionlasso.select.norm mice.impute.lasso.select.norm
Imputation by linear discriminant analysismice.impute.lda
Imputation by logistic regressionmice.impute.logreg
Imputation by logistic regression using the bootstrapmice.impute.logreg.boot
Imputation by the meanmice.impute.mean
Imputation by predictive mean matching with distance aided donor selectionmice.impute.midastouch
Imputation under MNAR mechanism by NARFCSmice.impute.mnar.logreg mice.impute.mnar.norm mnar.logreg mnar.norm
Imputation by multivariate predictive mean matchingmice.impute.mpmm mpmm
Imputation by Bayesian linear regressionmice.impute.norm norm
Imputation by linear regression, bootstrap methodmice.impute.norm.boot norm.boot
Imputation by linear regression without parameter uncertaintymice.impute.norm.nob norm.nob
Imputation by linear regression through predictionmice.impute.norm.predict norm.predict
Impute multilevel missing data using 'pan'mice.impute.panImpute
Passive imputationmice.impute.passive
Imputation by predictive mean matchingmice.impute.pmm pmm
Imputation of ordered data by polytomous regressionmice.impute.polr
Imputation of unordered data by polytomous regressionmice.impute.polyreg
Imputation of quadratic termsmice.impute.quadratic quadratic
Imputation by random forestsmice.impute.rf
Imputation by the random indicator method for nonignorable datamice.impute.ri ri
Imputation by simple random samplingmice.impute.sample
Multivariate Imputation by Chained Equations (Iteration Step)mice.mids
Set the theme for the plotting Trellis functionsmice.theme
Multiply imputed data set ('mids')mids mids-class
Export 'mids' object to Mplusmids2mplus
Export 'mids' object to SPSSmids2spss
Multiply imputed repeated analyses ('mira')mira mira-class
MNAR demo datamnar_demo_data
Name imputation blocksname.blocks
Name formula list elementsname.formulas
Number of complete casesncc
Cumulative hazard rate or Nelson-Aalen estimatorhazard nelsonaalen
NHANES example - all variables numericalnhanes
NHANES example - mixed numerical and discrete variablesnhanes2
Number of incomplete casesnic
Number of imputations per blocknimp
Draws values of beta and sigma by Bayesian linear regression.norm.draw norm.draw
Wrapper function that runs MICE in parallelparlmice
Datasets with various missing data patternspattern pattern1 pattern2 pattern3 pattern4
Plot the trace lines of the MICE algorithmplot.mids
Combine estimates by pooling rulespool pool.syn
Compare two nested models fitted to imputed datapool.compare
Pools R^2 of m models fitted to multiply-imputed datapool.r.squared
Multiple imputation pooling: univariate versionpool.scalar pool.scalar.syn
Combines estimates from a tidy tablepool.table
Hox pupil popularity data with missing popularity scorespopmis
Project on preterm and small for gestational age infants (POPS)pops pops.pred
Potthoff-Roy datapotthoffroy
Print a 'mads' objectprint.mads
Print a 'mids' objectprint.mice.anova print.mice.anova.summary print.mids print.mira
Quick selection of predictors from the dataquickpred
Self-reported and measured BMImgg selfreport
Squeeze the imputed values to be within specified boundaries.squeeze
Stripplot of observed and imputed datastripplot stripplot.mids
Summary of a 'mira' objectsummary.mads summary.mice.anova summary.mids summary.mira
Supports semi-transparent foreground colors?supports.transparent transparent
Terneuzen birth cohorttbc tbc.target terneuzen
Toenail datatoenail
Toenail datatoenail2
Echoes the package version numberversion
Walking disability datawalking
Subset of Irish wind speed datawindspeed
Evaluate an expression in multiple imputed datasetswith.mids
Scatterplot of amputed and non-amputed data against weighted sum scoresxyplot.mads
Scatterplot of observed and imputed dataxyplot xyplot.mids