All functions

complete.mids.nmi() complete.mids.1chain()

Creates Imputed Dataset from a mids.nmi or mids.1chain Object


R Utilities: Removing CF Line Endings


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


Dataset Internet


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


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

datlist_create() nested.datlist_create() print(<datlist>) print(<nested.datlist>) nested.datlist2datlist() datlist2nested.datlist()

Creates Objects of Class datlist or nested.datlist


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


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


R Utilities: Include an Index to a Data Frame


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


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.files()

R Utilities: Loading/Reading Data Files using miceadds

load.Rdata() load.Rdata2()

R Utilities: Loading Rdata Files in a Convenient Way


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


Simulating Normally Distributed Data


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.mcmc() mice.impute.2l.groupmean() mice.impute.2l.groupmean.elim()

Imputation of Latent and Manifest Group Means for Multilevel Data


Imputation at Level 2 (in miceadds)


Groupwise Imputation Function


Imputation Using a Fixed Vector


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

Multilevel Imputation Using lme4


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


Wrapper Function to Imputation Methods in the simputation Package


Substantive Model Compatible Multiple Imputation (Single Level)


Using a synthpop Synthesizing Method in the mice Package


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


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


Arguments for mice::mice Function


Combination of Chi Square Statistics of Multiply Imputed Datasets

micombine.cor() micombine.cov()

Inference for Correlations and Covariances for Multiply Imputed Datasets


Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximation


Converting a mids, mids.1chain or mids.nmi Object in a Dataset List


Export mids object to MLwiN


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


R Utilities: Formatting R Output on the R Console


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 Convergence Statistic of a mice Imputation


R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)


R Utilities: R Session Information

R Utilities: Saving/Writing Data Files using miceadds


R Utilities: Save a Data Frame in Rdata Format


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


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


Sum Preserving Rounding


Synthesizing Method for Fixed Values by Design in synthpop


Synthesizing Method for synthpop Using a Formula Interface


Using a mice Imputation Method in the synthpop Package


Constructs Synthetic Dataset with mice Imputation Methods


R Utilities: Various Strings Representing System Time

tw.imputation() tw.mcmc.imputation()

Two-Way Imputation


Stringing Variable Names with Line Breaks


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 a List of Multiply Imputed Datasets

write.fwf2() read.fwf2()

Reading and Writing Files in Fixed Width Format


Export Multiply Imputed Datasets from a mids Object


Writing a Data Frame into SPSS Format Using PSPP Software