Function reference • miceadds

All functions
`complete(<mids.nmi>)` `complete(<mids.1chain>)`	Creates Imputed Dataset from a `mids.nmi` or `mids.1chain` Object
`crlrem()`	R Utilities: Removing CF Line Endings
`cxxfunction.copy()`	R Utilities: Copy of an Rcpp File
`data.allison.gssexp` `data.allison.hip` `data.allison.usnews`	Datasets from Allison's Missing Data Book
`data.enders.depression` `data.enders.eatingattitudes` `data.enders.employee`	Datasets from Enders' Missing Data Book
`data.graham.ex3` `data.graham.ex6` `data.graham.ex8a` `data.graham.ex8b` `data.graham.ex8c`	Datasets from Grahams Missing Data Book
`data.internet`	Dataset Internet
`data.largescale`	Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)
`data.ma01` `data.ma02` `data.ma03` `data.ma04` `data.ma05` `data.ma06` `data.ma07` `data.ma08`	Example Datasets for miceadds Package
`data.smallscale`	Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables)
`datlist2mids()` `datalist2mids()`	Converting a List of Multiply Imputed Data Sets into a `mids` Object
`datlist2Amelia()`	Converting an Object of class `amelia`
`datlist_create()` `nested.datlist_create()` `print(<datlist>)` `print(<nested.datlist>)` `nested.datlist2datlist()` `datlist2nested.datlist()`	Creates Objects of Class `datlist` or `nested.datlist`
`draw.pv.ctt()`	Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory)
`filename_split()` `filename_split_vec()` `string_extract_part()` `string_to_matrix()`	Some Functionality for Strings and File Names
`files_move()`	Moves Files from One Directory to Another Directory
`fleishman_sim()` `fleishman_coef()`	Simulating Univariate Data from Fleishman Power Normal Transformations
`grep.vec()` `grepvec()` `grep_leading()` `grepvec_leading()`	R Utilities: Vector Based Versions of `grep`
`GroupMean()` `GroupSum()` `GroupSD()` `gm()` `cwc()`	Calculation of Groupwise Descriptive Statistics for Matrices
`index.dataframe()`	R Utilities: Include an Index to a Data Frame
`in_CI()`	Indicator Function for Analyzing Coverage
`jomo2datlist()` `jomo2mids()`	Converts a jomo Data Frame in Long Format into a List of Datasets or an Object of Class `mids`
`kernelpls.fit2()` `predict(<kernelpls.fit2>)`	Kernel PLS Regression
`library_install()`	R Utilities: Loading a Package or Installation of a Package if Necessary
`lm.cluster()` `glm.cluster()` `summary(<lm.cluster>)` `summary(<glm.cluster>)` `coef(<lm.cluster>)` `coef(<glm.cluster>)` `vcov(<lm.cluster>)` `vcov(<glm.cluster>)`	Cluster Robust Standard Errors for Linear Models and General Linear Models
`lmer_vcov()` `summary(<lmer_vcov>)` `coef(<lmer_vcov>)` `vcov(<lmer_vcov>)` `lmer_vcov2()` `lmer_pool()` `summary(<lmer_pool>)` `lmer_pool2()`	Statistical Inference for Fixed and Random Structure for Fitted Models in lme4
`load.data()` `load.files()`	R Utilities: Loading/Reading Data Files using miceadds
`load.Rdata()` `load.Rdata2()`	R Utilities: Loading `Rdata` Files in a Convenient Way
`ma.scale2()`	Standardization of a Matrix
`ma.wtd.meanNA()` `ma.wtd.sdNA()` `ma.wtd.covNA()` `ma.wtd.corNA()` `ma.wtd.skewnessNA()` `ma.wtd.kurtosisNA()` `ma.wtd.quantileNA()`	Some Multivariate Descriptive Statistics for Weighted Data in miceadds
`ma_lme4_formula_terms()` `ma_lme4_formula_design_matrices()`	Utility Functions for Working with lme4 Formula Objects
`ma_rmvnorm()`	Simulating Normally Distributed Data
`mi.anova()`	Analysis of Variance for Multiply Imputed Data Sets (Using the \(D_2\) Statistic)
`mice.1chain()` `summary(<mids.1chain>)` `print(<mids.1chain>)` `plot(<mids.1chain>)`	Multiple Imputation by Chained Equations using One Chain
`mice.impute.2l.contextual.pmm()` `mice.impute.2l.contextual.norm()`	Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables
`mice.impute.2l.latentgroupmean.ml()` `mice.impute.2l.latentgroupmean.mcmc()` `mice.impute.2l.groupmean()` `mice.impute.2l.groupmean.elim()`	Imputation of Latent and Manifest Group Means for Multilevel Data
`mice.impute.2lonly.function()`	Imputation at Level 2 (in miceadds)
`mice.impute.bygroup()`	Groupwise Imputation Function
`mice.impute.catpmm()`	Imputation of a Categorical Variable Using Multivariate Predictive Mean Matching
`mice.impute.constant()`	Imputation Using a Fixed Vector
`mice.impute.hotDeck()`	Imputation of a Variable Using Probabilistic Hot Deck Imputation
`mice.impute.imputeR.lmFun()` `mice.impute.imputeR.cFun()`	Wrapper Function to Imputation Methods in the imputeR Package
`mice.impute.ml.lmer()`	Multilevel Imputation Using lme4
`mice.impute.plausible.values()`	Plausible Value Imputation using Classical Test Theory and Based on Individual Likelihood
`mice.impute.pls()` `mice.impute.2l.pls2()`	Imputation using Partial Least Squares for Dimension Reduction
`mice.impute.pmm3()` `mice.impute.pmm4()` `mice.impute.pmm5()` `mice.impute.pmm6()`	Imputation by Predictive Mean Matching (in miceadds)
`mice.impute.lm()` `mice.impute.rlm()` `mice.impute.lqs()` `mice.impute.lm_fun()`	Imputation of a Linear Model by Bayesian Bootstrap
`mice.impute.simputation()`	Wrapper Function to Imputation Methods in the simputation Package
`mice.impute.smcfcs()`	Substantive Model Compatible Multiple Imputation (Single Level)
`mice.impute.synthpop()`	Using a synthpop Synthesizing Method in the mice Package
`mice.impute.tricube.pmm()`	Imputation by Tricube Predictive Mean Matching
`mice.impute.weighted.pmm()` `mice.impute.weighted.norm()`	Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression
`mice.nmi()` `summary(<mids.nmi>)` `print(<mids.nmi>)`	Nested Multiple Imputation
`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()`	Defunct miceadds Functions
`miceadds-package` `miceadds`	Some Additional Multiple Imputation Functions, Especially for 'mice'
`ma_exists_get()` `ma_exists()` `mice_imputation_get_states()`	Utility Functions in miceadds
`mice.impute.2l.continuous()` `mice.impute.2l.pmm()` `mice.impute.2l.binary()`	Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using lme4 or blme
`mice_inits()`	Arguments for `mice::mice` Function
`micombine.chisquare()`	Combination of Chi Square Statistics of Multiply Imputed Datasets
`micombine.cor()` `micombine.cov()`	Inference for Correlations and Covariances for Multiply Imputed Datasets
`micombine.F()`	Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximation
`mids2datlist()`	Converting a `mids`, `mids.1chain` or `mids.nmi` Object in a Dataset List
`mids2mlwin()`	Export `mids` object to MLwiN
`mi_dstat()`	Cohen's d Effect Size for Missingness Indicators
`ml_mcmc()` `summary(<ml_mcmc>)` `plot(<ml_mcmc>)` `coef(<ml_mcmc>)` `vcov(<ml_mcmc>)` `ml_mcmc_fit()` `miceadds_rcpp_ml_mcmc_sample_beta()` `miceadds_rcpp_ml_mcmc_sample_u()` `miceadds_rcpp_ml_mcmc_sample_psi()` `miceadds_rcpp_ml_mcmc_sample_sigma2()` `miceadds_rcpp_ml_mcmc_sample_latent_probit()` `miceadds_rcpp_ml_mcmc_sample_thresholds()` `miceadds_rcpp_ml_mcmc_predict_fixed_random()` `miceadds_rcpp_ml_mcmc_predict_random_list()` `miceadds_rcpp_ml_mcmc_predict_random()` `miceadds_rcpp_ml_mcmc_predict_fixed()` `miceadds_rcpp_ml_mcmc_subtract_fixed()` `miceadds_rcpp_ml_mcmc_subtract_random()` `miceadds_rcpp_ml_mcmc_compute_ztz()` `miceadds_rcpp_ml_mcmc_compute_xtx()` `miceadds_rcpp_ml_mcmc_probit_category_prob()` `miceadds_rcpp_pnorm()` `miceadds_rcpp_qnorm()` `miceadds_rcpp_rtnorm()`	MCMC Estimation for Mixed Effects Model
`NestedImputationList()` `print(<NestedImputationList>)` `MIcombine(<NestedImputationResultList>)`	Functions for Analysis of Nested Multiply Imputed Datasets
`nestedList2List()` `List2nestedList()`	Converting a Nested List into a List (and Vice Versa)
`NMIwaldtest()` `MIwaldtest()` `summary(<NMIwaldtest>)` `summary(<MIwaldtest>)` `create.designMatrices.waldtest()`	Wald Test for Nested Multiply Imputed Datasets
`nnig_coef()` `nnig_sim()`	Simulation of Multivariate Linearly Related Non-Normal Variables
`output.format1()`	R Utilities: Formatting R Output on the R Console
`pca.covridge()`	Principal Component Analysis with Ridge Regularization
`pool.mids.nmi()` `NMIcombine()` `pool_nmi()` `NMIextract()` `summary(<mipo.nmi>)` `coef(<mipo.nmi>)` `vcov(<mipo.nmi>)`	Pooling for Nested Multiple Imputation
`pool_mi()` `summary(<pool_mi>)` `coef(<pool_mi>)` `vcov(<pool_mi>)`	Statistical Inference for Multiply Imputed Datasets
`Reval()` `Revalpr()` `Revalprstr()` `Revalpr_round()` `Revalpr_maxabs()`	R Utilities: Evaluates a String as an Expression in R
`Rfunction_include_argument_values()` `Rfunction_output_list_result_function()` `Rcppfunction_remove_classes()`	Utility Functions for Writing R Functions
`Rhat.mice()`	Rhat Convergence Statistic of a `mice` Imputation
`round2()`	R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)
`Rsessinfo()`	R Utilities: R Session Information
`save.data()`	R Utilities: Saving/Writing Data Files using miceadds
`save.Rdata()`	R Utilities: Save a Data Frame in `Rdata` Format
`scale_datlist()`	Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets
`scan.vec()` `scan.vector()` `scan0()`	R Utilities: Scan a Character Vector
`source.all()` `source.Rcpp.all()` `rcpp_create_header_file()`	R Utilities: Source all R or Rcpp Files within a Directory
`stats0()` `max0()` `mean0()` `min0()` `quantile0()` `sd0()` `var0()` `prop_miss()`	Descriptive Statistics for a Vector or a Data Frame
`str_C.expand.grid()`	R Utilities: String Paste Combined with `expand.grid`
`subset_datlist()` `subset(<datlist>)` `subset(<imputationList>)` `subset(<mids>)` `subset(<mids.1chain>)` `subset_nested.datlist()` `subset(<nested.datlist>)` `subset(<NestedImputationList>)`	Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
`sumpreserving.rounding()`	Sum Preserving Rounding
`syn.constant()`	Synthesizing Method for Fixed Values by Design in synthpop
`syn.formula()`	Synthesizing Method for synthpop Using a Formula Interface
`syn.mice()`	Using a mice Imputation Method in the synthpop Package
`syn_da()`	Generation of Synthetic Data Utilizing Data Augmentation
`syn_mice()`	Constructs Synthetic Dataset with mice Imputation Methods
`systime()`	R Utilities: Various Strings Representing System Time
`tw.imputation()` `tw.mcmc.imputation()`	Two-Way Imputation
`VariableNames2String()`	Stringing Variable Names with Line Breaks
`visitSequence.determine()`	Automatic Determination of a Visit Sequence in `mice`
`with(<mids.1chain>)` `with(<datlist>)` `with(<mids.nmi>)` `with(<nested.datlist>)` `with(<NestedImputationList>)` `within(<datlist>)` `within(<imputationList>)` `within(<nested.datlist>)` `within(<NestedImputationList>)` `withPool_MI()` `withPool_NMI()` `summary(<mira.nmi>)`	Evaluates an Expression for (Nested) Multiply Imputed Datasets
`write.datlist()`	Write a List of Multiply Imputed Datasets
`write.fwf2()` `read.fwf2()`	Reading and Writing Files in Fixed Width Format
`write.mice.imputation()`	Export Multiply Imputed Datasets from a `mids` Object
`write.pspp()`	Writing a Data Frame into SPSS Format Using PSPP Software

Reference

All functions