Package: miceadds 3.17-44

Alexander Robitzsch

miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).

Authors:Alexander Robitzsch [aut,cre], Simon Grund [aut], Thorsten Henke [ctb]

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miceadds.pdf |miceadds.html✨
miceadds/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/alexanderrobitzsch/miceadds/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

6.60 score 1 stars 9 packages 618 scripts 4.8k downloads 20 mentions 191 exports 63 dependencies

Last updated 11 months agofrom:e0814b42dd. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-linux-x86_64OKNov 05 2024

Exports:create.designMatrices.waldtestcrlremcwccxxfunction.copydatalist2midsdatlist_createdatlist2Ameliadatlist2midsdatlist2nested.datlistdraw.pv.cttfast.groupmeanfast.groupsumfilename_splitfilename_split_vecfiles_movefleishman_coeffleishman_simglm.clustergmgrep_leadinggrep.vecgrepvecgrepvec_leadingGroupMeanGroupSDGroupSumin_CIindex.dataframejomo2datlistjomo2midskernelpls.fit2library_installList2nestedListlm.clusterlmer_poollmer_pool2lmer_vcovlmer_vcov2load.dataload.filesload.Rdataload.Rdata2ma_existsma_exists_getma_lme4_formula_design_matricesma_lme4_formula_termsma_rmvnormma.scale2ma.wtd.corNAma.wtd.covNAma.wtd.kurtosisNAma.wtd.meanNAma.wtd.quantileNAma.wtd.sdNAma.wtd.skewnessNAmax0mean0mi_dstatmi.anovamice_imputation_get_statesmice_initsmice.1chainmice.impute.2l.binarymice.impute.2l.contextual.normmice.impute.2l.contextual.pmmmice.impute.2l.continuousmice.impute.2l.groupmeanmice.impute.2l.groupmean.elimmice.impute.2l.latentgroupmean.mcmcmice.impute.2l.latentgroupmean.mlmice.impute.2l.plausible.valuesmice.impute.2l.plsmice.impute.2l.pls2mice.impute.2l.pmmmice.impute.2lonly.functionmice.impute.2lonly.norm2mice.impute.2lonly.pmm2mice.impute.bygroupmice.impute.catpmmmice.impute.constantmice.impute.hotDeckmice.impute.imputeR.cFunmice.impute.imputeR.lmFunmice.impute.lmmice.impute.lm_funmice.impute.lqsmice.impute.ml.lmermice.impute.plausible.valuesmice.impute.plsmice.impute.pmm3mice.impute.pmm4mice.impute.pmm5mice.impute.pmm6mice.impute.rlmmice.impute.simputationmice.impute.smcfcsmice.impute.synthpopmice.impute.tricube.pmmmice.impute.tricube.pmm2mice.impute.weighted.normmice.impute.weighted.pmmmice.nmimiceadds_rcpp_ml_mcmc_compute_xtxmiceadds_rcpp_ml_mcmc_compute_ztzmiceadds_rcpp_ml_mcmc_predict_fixedmiceadds_rcpp_ml_mcmc_predict_fixed_randommiceadds_rcpp_ml_mcmc_predict_randommiceadds_rcpp_ml_mcmc_predict_random_listmiceadds_rcpp_ml_mcmc_probit_category_probmiceadds_rcpp_ml_mcmc_sample_betamiceadds_rcpp_ml_mcmc_sample_latent_probitmiceadds_rcpp_ml_mcmc_sample_psimiceadds_rcpp_ml_mcmc_sample_sigma2miceadds_rcpp_ml_mcmc_sample_thresholdsmiceadds_rcpp_ml_mcmc_sample_umiceadds_rcpp_ml_mcmc_subtract_fixedmiceadds_rcpp_ml_mcmc_subtract_randommiceadds_rcpp_pnormmiceadds_rcpp_qnormmiceadds_rcpp_rtnormmicombine.chisquaremicombine.cormicombine.covmicombine.FMIcombine.NestedImputationResultListmids2datlistmin0MIwaldtestml_mcmcml_mcmc_fitnested.datlist_createnested.datlist2datlistNestedImputationListnestedList2ListNMIcombineNMIextractNMIwaldtestnnig_coefnnig_simoutput.format1pca.covridgepool_mipool_nmipool.mids.nmiprop_missquantile0rcpp_create_header_fileRcppfunction_remove_classesread.fwf2RevalRevalprRevalpr_maxabsRevalpr_roundRevalprstrRfunction_include_argument_valuesRfunction_output_list_result_functionRhat.miceround2Rsessinfosave.datasave.Rdatascale_datlistscan.vecscan.vectorscan0sd0source.allsource.Rcpp.allstats0str_C.expand.gridstring_extract_partstring_to_matrixsubset_datlistsumpreserving.roundingsyn_dasyn_micesyn.constantsyn.micesystimetw.imputationtw.mcmc.imputationvar0VariableNames2StringvisitSequence.determinewithin.imputationListwithPool_MIwithPool_NMIwrite.datlistwrite.fwf2write.mice.imputationwrite.pspp

Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayonDBIdplyrfansiforcatsforeachgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmiceminqamitmlmitoolsnlmenloptrnnetnumDerivordinalpanpillarpkgconfigprettyunitsprogresspurrrR6RcppRcppArmadilloRcppEigenreadrrlangrpartshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr

Readme and manuals

Help Manual

Help pageTopics
Some Additional Multiple Imputation Functions, Especially for 'mice'miceadds-package miceadds
Creates Imputed Dataset from a 'mids.nmi' or 'mids.1chain' Objectcomplete.mids.1chain complete.mids.nmi
R Utilities: Removing CF Line Endingscrlrem
R Utilities: Copy of an 'Rcpp' Filecxxfunction.copy
Datasets from Allison's _Missing Data_ Bookdata.allison data.allison.gssexp data.allison.hip data.allison.usnews
Datasets from Enders' _Missing Data_ Bookdata.enders data.enders.depression data.enders.eatingattitudes data.enders.employee
Datasets from Grahams _Missing Data_ Bookdata.graham data.graham.ex3 data.graham.ex6 data.graham.ex8a data.graham.ex8b data.graham.ex8c
Dataset Internetdata.internet
Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)data.largescale
Example Datasets for 'miceadds' Packagedata.ma data.ma01 data.ma02 data.ma03 data.ma04 data.ma05 data.ma06 data.ma07 data.ma08 data.ma09
Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables)data.smallscale
Creates Objects of Class 'datlist' or 'nested.datlist'datlist2nested.datlist datlist_create nested.datlist2datlist nested.datlist_create print.datlist print.nested.datlist
Converting an Object of class 'amelia'datlist2Amelia
Converting a List of Multiply Imputed Data Sets into a 'mids' Objectdatalist2mids datlist2mids
Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory)draw.pv.ctt
Some Functionality for Strings and File Namesfilename_split filename_split_vec string_extract_part string_to_matrix
Moves Files from One Directory to Another Directoryfiles_move
Simulating Univariate Data from Fleishman Power Normal Transformationsfleishman_coef fleishman_sim
R Utilities: Vector Based Versions of 'grep'grep.vec grepvec grepvec_leading grep_leading
Calculation of Groupwise Descriptive Statistics for Matricescwc gm GroupMean GroupSD GroupSum
Indicator Function for Analyzing Coveragein_CI
R Utilities: Include an Index to a Data Frameindex.dataframe
Converts a 'jomo' Data Frame in Long Format into a List of Datasets or an Object of Class 'mids'jomo2datlist jomo2mids
Kernel PLS Regressionkernelpls.fit2 predict.kernelpls.fit2
R Utilities: Loading a Package or Installation of a Package if Necessarylibrary_install
Cluster Robust Standard Errors for Linear Models and General Linear Modelscoef.glm.cluster coef.lm.cluster glm.cluster lm.cluster summary.glm.cluster summary.lm.cluster vcov.glm.cluster vcov.lm.cluster
Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'coef.lmer_vcov lmer_pool lmer_pool2 lmer_vcov lmer_vcov2 summary.lmer_pool summary.lmer_vcov vcov.lmer_vcov
R Utilities: Loading/Reading Data Files using 'miceadds'load.data load.files
R Utilities: Loading 'Rdata' Files in a Convenient Wayload.Rdata load.Rdata2
Utility Functions for Working with 'lme4' Formula Objectsma_lme4_formula ma_lme4_formula_design_matrices ma_lme4_formula_terms
Simulating Normally Distributed Datama_rmvnorm
Standardization of a Matrixma.scale2
Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'ma.wtd.corNA ma.wtd.covNA ma.wtd.kurtosisNA ma.wtd.meanNA ma.wtd.quantileNA ma.wtd.sdNA ma.wtd.skewnessNA ma.wtd.statNA
Cohen's d Effect Size for Missingness Indicatorsmi_dstat
Analysis of Variance for Multiply Imputed Data Sets (Using the D_2 Statistic)mi.anova
Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using 'lme4' or 'blme'mice.impute.2l.binary mice.impute.2l.continuous mice.impute.2l.pmm
Arguments for 'mice::mice' Functionmice_inits
Multiple Imputation by Chained Equations using One Chainmice.1chain plot.mids.1chain print.mids.1chain summary.mids.1chain
Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variablesmice.impute.2l.contextual.norm mice.impute.2l.contextual.pmm
Imputation of Latent and Manifest Group Means for Multilevel Datamice.impute.2l.groupmean mice.impute.2l.groupmean.elim mice.impute.2l.latentgroupmean.mcmc mice.impute.2l.latentgroupmean.ml
Imputation at Level 2 (in 'miceadds')mice.impute.2lonly.function
Groupwise Imputation Functionmice.impute.bygroup
Imputation of a Categorical Variable Using Multivariate Predictive Mean Matchingmice.impute.catpmm
Imputation Using a Fixed Vectormice.impute.constant
Imputation of a Variable Using Probabilistic Hot Deck Imputationmice.impute.hotDeck
Wrapper Function to Imputation Methods in the 'imputeR' Packagemice.impute.imputeR.cFun mice.impute.imputeR.lmFun
Multilevel Imputation Using 'lme4'mice.impute.ml.lmer
Plausible Value Imputation using Classical Test Theory and Based on Individual Likelihoodmice.impute.plausible.values
Imputation using Partial Least Squares for Dimension Reductionmice.impute.2l.pls2 mice.impute.pls
Imputation by Predictive Mean Matching (in 'miceadds')mice.impute.pmm3 mice.impute.pmm4 mice.impute.pmm5 mice.impute.pmm6
Imputation of a Linear Model by Bayesian Bootstrapmice.impute.lm mice.impute.lm_fun mice.impute.lqs mice.impute.rlm
Wrapper Function to Imputation Methods in the 'simputation' Packagemice.impute.simputation
Substantive Model Compatible Multiple Imputation (Single Level)mice.impute.smcfcs
Using a 'synthpop' Synthesizing Method in the 'mice' Packagemice.impute.synthpop
Imputation by Tricube Predictive Mean Matchingmice.impute.tricube.pmm
Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regressionmice.impute.weighted.norm mice.impute.weighted.pmm
Nested Multiple Imputationmice.nmi print.mids.nmi summary.mids.nmi
Defunct 'miceadds' Functionsfast.groupmean fast.groupsum mice.impute.2l.plausible.values mice.impute.2l.pls mice.impute.2lonly.norm2 mice.impute.2lonly.pmm2 mice.impute.tricube.pmm2 miceadds-defunct
Utility Functions in 'miceadds'ma_exists ma_exists_get miceadds-utilities mice_imputation_get_states
Combination of Chi Square Statistics of Multiply Imputed Datasetsmicombine.chisquare
Inference for Correlations and Covariances for Multiply Imputed Datasetsmicombine.cor micombine.cov
Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximationmicombine.F
Converting a 'mids', 'mids.1chain' or 'mids.nmi' Object in a Dataset Listmids2datlist
Export 'mids' object to MLwiNmids2mlwin
MCMC Estimation for Mixed Effects Modelcoef.ml_mcmc miceadds_rcpp_ml_mcmc_compute_xtx miceadds_rcpp_ml_mcmc_compute_ztz miceadds_rcpp_ml_mcmc_predict_fixed miceadds_rcpp_ml_mcmc_predict_fixed_random miceadds_rcpp_ml_mcmc_predict_random miceadds_rcpp_ml_mcmc_predict_random_list miceadds_rcpp_ml_mcmc_probit_category_prob miceadds_rcpp_ml_mcmc_sample_beta miceadds_rcpp_ml_mcmc_sample_latent_probit miceadds_rcpp_ml_mcmc_sample_psi miceadds_rcpp_ml_mcmc_sample_sigma2 miceadds_rcpp_ml_mcmc_sample_thresholds miceadds_rcpp_ml_mcmc_sample_u miceadds_rcpp_ml_mcmc_subtract_fixed miceadds_rcpp_ml_mcmc_subtract_random miceadds_rcpp_pnorm miceadds_rcpp_qnorm miceadds_rcpp_rtnorm ml_mcmc ml_mcmc_fit plot.ml_mcmc summary.ml_mcmc vcov.ml_mcmc
Functions for Analysis of Nested Multiply Imputed DatasetsMIcombine.NestedImputationResultList NestedImputationList print.NestedImputationList
Converting a Nested List into a List (and Vice Versa)List2nestedList nestedList2List
Wald Test for Nested Multiply Imputed Datasetscreate.designMatrices.waldtest MIwaldtest NMIwaldtest summary.MIwaldtest summary.NMIwaldtest
Simulation of Multivariate Linearly Related Non-Normal Variablesnnig_coef nnig_sim
R Utilities: Formatting R Output on the R Consoleoutput.format1
Principal Component Analysis with Ridge Regularizationpca.covridge
Statistical Inference for Multiply Imputed Datasetscoef.pool_mi pool_mi summary.pool_mi vcov.pool_mi
Pooling for Nested Multiple Imputationcoef.mipo.nmi NMIcombine NMIextract pool.mids.nmi pool_nmi summary.mipo.nmi vcov.mipo.nmi
R Utilities: Evaluates a String as an Expression in RReval Revalpr Revalprstr Revalpr_maxabs Revalpr_round
Utility Functions for Writing R FunctionsRcppfunction Rcppfunction_remove_classes Rfunction Rfunction_include_argument_values Rfunction_output_list_result_function
Rhat Convergence Statistic of a 'mice' ImputationRhat.mice
R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)round2
R Utilities: R Session InformationRsessinfo
R Utilities: Saving/Writing Data Files using 'miceadds'save.data
R Utilities: Save a Data Frame in 'Rdata' Formatsave.Rdata
Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasetsscale_datlist
R Utilities: Scan a Character Vectorscan.vec scan.vector scan0
R Utilities: Source all R or 'Rcpp' Files within a Directoryrcpp_create_header_file source.all source.Rcpp.all
Descriptive Statistics for a Vector or a Data Framemax0 mean0 min0 prop_miss quantile0 sd0 stats0 var0
R Utilities: String Paste Combined with 'expand.grid'str_C.expand.grid
Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasetssubset.datlist subset.imputationList subset.mids subset.mids.1chain subset.nested.datlist subset.NestedImputationList subset_datlist subset_nested.datlist
Sum Preserving Roundingsumpreserving.rounding
Generation of Synthetic Data Utilizing Data Augmentationsyn_da
Constructs Synthetic Dataset with 'mice' Imputation Methodssyn_mice
Synthesizing Method for Fixed Values by Design in 'synthpop'syn.constant
Synthesizing Method for 'synthpop' Using a Formula Interfacesyn.formula
Using a 'mice' Imputation Method in the 'synthpop' Packagesyn.mice
R Utilities: Various Strings Representing System Timesystime
Two-Way Imputationtw.imputation tw.mcmc.imputation
Stringing Variable Names with Line BreaksVariableNames2String
Automatic Determination of a Visit Sequence in 'mice'visitSequence.determine
Evaluates an Expression for (Nested) Multiply Imputed Datasetssummary.mira.nmi with.datlist with.mids.1chain with.mids.nmi with.nested.datlist with.NestedImputationList within.datlist within.imputationList within.nested.datlist within.NestedImputationList withPool_MI withPool_NMI
Write a List of Multiply Imputed Datasetswrite.datlist
Reading and Writing Files in Fixed Width Formatread.fwf2 write.fwf2
Export Multiply Imputed Datasets from a 'mids' Objectwrite.mice.imputation
Writing a Data Frame into SPSS Format Using PSPP Softwarewrite.pspp