Package: miceadds 3.18-20

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://alexanderrobitzsch.r-universe.dev', 'https://cloud.r-project.org'))

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

data.allison.gssexp - Datasets from Allison's _Missing Data_ Book
data.allison.hip - Datasets from Allison's _Missing Data_ Book
data.allison.usnews - Datasets from Allison's _Missing Data_ Book
data.enders.depression - Datasets from Enders' _Missing Data_ Book
data.enders.eatingattitudes - Datasets from Enders' _Missing Data_ Book
data.enders.employee - Datasets from Enders' _Missing Data_ Book
data.graham.ex3 - Datasets from Grahams _Missing Data_ Book
data.graham.ex6 - Datasets from Grahams _Missing Data_ Book
data.graham.ex8a - Datasets from Grahams _Missing Data_ Book
data.graham.ex8b - Datasets from Grahams _Missing Data_ Book
data.graham.ex8c - Datasets from Grahams _Missing Data_ Book
data.internet - Dataset Internet
data.largescale - Large-scale Dataset for Testing Purposes
data.ma01 - Example Datasets for 'miceadds' Package
data.ma02 - Example Datasets for 'miceadds' Package
data.ma03 - Example Datasets for 'miceadds' Package
data.ma04 - Example Datasets for 'miceadds' Package
data.ma05 - Example Datasets for 'miceadds' Package
data.ma06 - Example Datasets for 'miceadds' Package
data.ma07 - Example Datasets for 'miceadds' Package
data.ma08 - Example Datasets for 'miceadds' Package
data.ma09 - Example Datasets for 'miceadds' Package
data.smallscale - Small-Scale Dataset for Testing Purposes

On CRAN:

missing-data multiple-imputation

191 exports 16 stars 3.07 score 63 dependencies 9 dependents 20 mentions 4.6k downloads

Last updated 2 months agofrom:565d5546b0

Dependencies:backports bit bit64 boot broom cli clipr codetools cpp11 crayon DBI dplyr fansi forcats foreach generics glmnet glue haven hms iterators jomo lattice lifecycle lme4 magrittr MASS Matrix mice minqa mitml mitools nlme nloptr nnet numDeriv ordinal pan pillar pkgconfig prettyunits progress purrr R6 Rcpp RcppArmadillo RcppEigen readr rlang rpart shape stringi stringr survival tibble tidyr tidyselect tzdb ucminf utf8 vctrs vroom withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Some Additional Multiple Imputation Functions, Especially for 'mice'	miceadds-package miceadds
Creates Imputed Dataset from a 'mids.nmi' or 'mids.1chain' Object	complete.mids.1chain complete.mids.nmi
R Utilities: Removing CF Line Endings	crlrem
R Utilities: Copy of an 'Rcpp' File	cxxfunction.copy
Datasets from Allison's _Missing Data_ Book	data.allison data.allison.gssexp data.allison.hip data.allison.usnews
Datasets from Enders' _Missing Data_ Book	data.enders data.enders.depression data.enders.eatingattitudes data.enders.employee
Datasets from Grahams _Missing Data_ Book	data.graham data.graham.ex3 data.graham.ex6 data.graham.ex8a data.graham.ex8b data.graham.ex8c
Dataset Internet	data.internet
Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)	data.largescale
Example Datasets for 'miceadds' Package	data.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' Object	datalist2mids datlist2mids
Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory)	draw.pv.ctt
Some Functionality for Strings and File Names	filename_split filename_split_vec string_extract_part string_to_matrix
Moves Files from One Directory to Another Directory	files_move
Simulating Univariate Data from Fleishman Power Normal Transformations	fleishman_coef fleishman_sim
R Utilities: Vector Based Versions of 'grep'	grep.vec grepvec grepvec_leading grep_leading
Calculation of Groupwise Descriptive Statistics for Matrices	cwc gm GroupMean GroupSD GroupSum
Indicator Function for Analyzing Coverage	in_CI
R Utilities: Include an Index to a Data Frame	index.dataframe
Converts a 'jomo' Data Frame in Long Format into a List of Datasets or an Object of Class 'mids'	jomo2datlist jomo2mids
Kernel PLS Regression	kernelpls.fit2 predict.kernelpls.fit2
R Utilities: Loading a Package or Installation of a Package if Necessary	library_install
Cluster Robust Standard Errors for Linear Models and General Linear Models	coef.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 Way	load.Rdata load.Rdata2
Utility Functions for Working with 'lme4' Formula Objects	ma_lme4_formula ma_lme4_formula_design_matrices ma_lme4_formula_terms
Simulating Normally Distributed Data	ma_rmvnorm
Standardization of a Matrix	ma.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 Indicators	mi_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' Function	mice_inits
Multiple Imputation by Chained Equations using One Chain	mice.1chain plot.mids.1chain print.mids.1chain summary.mids.1chain
Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables	mice.impute.2l.contextual.norm mice.impute.2l.contextual.pmm
Imputation of Latent and Manifest Group Means for Multilevel Data	mice.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 Function	mice.impute.bygroup
Imputation of a Categorical Variable Using Multivariate Predictive Mean Matching	mice.impute.catpmm
Imputation Using a Fixed Vector	mice.impute.constant
Imputation of a Variable Using Probabilistic Hot Deck Imputation	mice.impute.hotDeck
Wrapper Function to Imputation Methods in the 'imputeR' Package	mice.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 Likelihood	mice.impute.plausible.values
Imputation using Partial Least Squares for Dimension Reduction	mice.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 Bootstrap	mice.impute.lm mice.impute.lm_fun mice.impute.lqs mice.impute.rlm
Wrapper Function to Imputation Methods in the 'simputation' Package	mice.impute.simputation
Substantive Model Compatible Multiple Imputation (Single Level)	mice.impute.smcfcs
Using a 'synthpop' Synthesizing Method in the 'mice' Package	mice.impute.synthpop
Imputation by Tricube Predictive Mean Matching	mice.impute.tricube.pmm
Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression	mice.impute.weighted.norm mice.impute.weighted.pmm
Nested Multiple Imputation	mice.nmi print.mids.nmi summary.mids.nmi
Defunct 'miceadds' Functions	fast.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 Datasets	micombine.chisquare
Inference for Correlations and Covariances for Multiply Imputed Datasets	micombine.cor micombine.cov
Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximation	micombine.F
Converting a 'mids', 'mids.1chain' or 'mids.nmi' Object in a Dataset List	mids2datlist
Export 'mids' object to MLwiN	mids2mlwin
MCMC Estimation for Mixed Effects Model	coef.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 Datasets	MIcombine.NestedImputationResultList NestedImputationList print.NestedImputationList
Converting a Nested List into a List (and Vice Versa)	List2nestedList nestedList2List
Wald Test for Nested Multiply Imputed Datasets	create.designMatrices.waldtest MIwaldtest NMIwaldtest summary.MIwaldtest summary.NMIwaldtest
Simulation of Multivariate Linearly Related Non-Normal Variables	nnig_coef nnig_sim
R Utilities: Formatting R Output on the R Console	output.format1
Principal Component Analysis with Ridge Regularization	pca.covridge
Statistical Inference for Multiply Imputed Datasets	coef.pool_mi pool_mi summary.pool_mi vcov.pool_mi
Pooling for Nested Multiple Imputation	coef.mipo.nmi NMIcombine NMIextract pool.mids.nmi pool_nmi summary.mipo.nmi vcov.mipo.nmi
R Utilities: Evaluates a String as an Expression in R	Reval Revalpr Revalprstr Revalpr_maxabs Revalpr_round
Utility Functions for Writing R Functions	Rcppfunction Rcppfunction_remove_classes Rfunction Rfunction_include_argument_values Rfunction_output_list_result_function
Rhat Convergence Statistic of a 'mice' Imputation	Rhat.mice
R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)	round2
R Utilities: R Session Information	Rsessinfo
R Utilities: Saving/Writing Data Files using 'miceadds'	save.data
R Utilities: Save a Data Frame in 'Rdata' Format	save.Rdata
Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets	scale_datlist
R Utilities: Scan a Character Vector	scan.vec scan.vector scan0
R Utilities: Source all R or 'Rcpp' Files within a Directory	rcpp_create_header_file source.all source.Rcpp.all
Descriptive Statistics for a Vector or a Data Frame	max0 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 Datasets	subset.datlist subset.imputationList subset.mids subset.mids.1chain subset.nested.datlist subset.NestedImputationList subset_datlist subset_nested.datlist
Sum Preserving Rounding	sumpreserving.rounding
Generation of Synthetic Data Utilizing Data Augmentation	syn_da
Constructs Synthetic Dataset with 'mice' Imputation Methods	syn_mice
Synthesizing Method for Fixed Values by Design in 'synthpop'	syn.constant
Synthesizing Method for 'synthpop' Using a Formula Interface	syn.formula
Using a 'mice' Imputation Method in the 'synthpop' Package	syn.mice
R Utilities: Various Strings Representing System Time	systime
Two-Way Imputation	tw.imputation tw.mcmc.imputation
Stringing Variable Names with Line Breaks	VariableNames2String
Automatic Determination of a Visit Sequence in 'mice'	visitSequence.determine
Evaluates an Expression for (Nested) Multiply Imputed Datasets	summary.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 Datasets	write.datlist
Reading and Writing Files in Fixed Width Format	read.fwf2 write.fwf2
Export Multiply Imputed Datasets from a 'mids' Object	write.mice.imputation
Writing a Data Frame into SPSS Format Using PSPP Software	write.pspp