miceadds3.17-44 package

Some Additional Multiple Imputation Functions, Especially for 'mice'

miceadds-defunct

Defunct miceadds Functions

mice_inits

Arguments for mice::mice Function

miceadds-package

tools:::Rd_package_title("miceadds")

miceadds-utilities

Utility Functions in miceadds

nestedList2List

Converting a Nested List into a List (and Vice Versa)

NMIwaldtest

Wald Test for Nested Multiply Imputed Datasets

nnig_sim

Simulation of Multivariate Linearly Related Non-Normal Variables

output.format1

Utilities: Formatting R Output on the Console

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

micombine.cor

Inference for Correlations and Covariances for Multiply Imputed Datase...

micombine.F

Combination of F Statistics for Multiply Imputed Datasets Using a Chi ...

mids2datlist

Converting a mids, mids.1chain or mids.nmiObject in a Dataset Li...

mids2mlwin

Export mids object to MLwiN

ml_mcmc

MCMC Estimation for Mixed Effects Model

mice.impute.imputeR.lmFun

Wrapper Function to Imputation Methods in the imputeR Package

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

mice.impute.ml.lmer

Multilevel Imputation Using lme4

mice.impute.plausible.values

Plausible Value Imputation using Classical Test Theory and Based on In...

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

mice.impute.rlm

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)

complete.miceadds

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

crlrem

Utilities: Removing CF Line Endings

cxxfunction.copy

Utilities: Copy of an Rcpp File

data.allison

Datasets from Allison's Missing Data Book

data.enders

Datasets from Enders' Missing Data Book

data.graham

Datasets from Grahams Missing Data Book

data.ma

Example Datasets for miceadds Package

datalist2mids

Converting a List of Multiply Imputed Data Sets into a mids Object

datlist_create

Creates Objects of Class datlist or nested.datlist

datlist2Amelia

Converting an Object of class amelia

draw.pv.ctt

Plausible Value Imputation Using a Known Measurement Error Variance (B...

filename_split

Some Functionality for Strings and File Names

files_move

Moves Files from One Directory to Another Directory

fleishman_sim

Simulating Univariate Data from Fleishman Power Normal Transformations

grep.vec

Utilities: Vector Based Versions of grep

GroupMean

Calculation of Groupwise Descriptive Statistics for Matrices

in_CI

Indicator Function for Analyzing Coverage

index.dataframe

Utilities: Include an Index to a Data Frame

jomo2datlist

Converts a jomo Data Frame in Long Format into a List of Datasets or...

kernelpls.fit2

Kernel PLS Regression

library_install

Utilities: Loading a Package or Installation of a Package if Necessary

lm.cluster

Cluster Robust Standard Errors for Linear Models and General Linear Mo...

lmer_vcov

Statistical Inference for Fixed and Random Structure for Fitted Models...

load.data

Utilities: Loading/Reading Data Files using miceadds

loadata.Rd

Utilities: Loading Rdata Files in a Convenient Way

ma.scale2

Standardization of a Matrix

ma.wtd.statNA

Some Multivariate Descriptive Statistics for Weighted Data in `miceadd...

ma_lme4_formula_terms

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 D2D_2 S...

mi_dstat

Cohen's d Effect Size for Missingness Indicators

mice.1chain

Multiple Imputation by Chained Equations using One Chain

mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching or Normal Linear Regression wit...

mice.impute.2l.latentgroupmean.ml

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 Mea...

mice.impute.constant

Imputation Using a Fixed Vector

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

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

Imputation by Weighted Predictive Mean Matching or Weighted Normal Lin...

mice.nmi

Nested Multiple Imputation

mice_imputation_2l_lmer

Imputation of a Continuous or a Binary Variable From a Two-Level Regre...

pca.covridge

Principal Component Analysis with Ridge Regularization

pool.mids.nmi

Pooling for Nested Multiple Imputation

pool_mi

Statistical Inference for Multiply Imputed Datasets

Reval

Utilities: Evaluates a String as an Expression in

Rfunction_include_argument_values

Utility Functions for Writing Functions

Rhat.mice

Rhat Convergence Statistic of a mice Imputation

round2

Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

Rsessinfo

Utilities: Session Information

save.data

Utilities: Saving/Writing Data Files using miceadds

saveata.Rd

Utilities: Save a Data Frame in Rdata Format

scale_datlist

Adding a Standardized Variable to a List of Multiply Imputed Datasets ...

scan.vector

Utilities: Scan a Character Vector

source.all

Utilities: Source all R or Rcpp Files within a Directory

stats0

Descriptive Statistics for a Vector or a Data Frame

str_C.expand.grid

Utilities: String Paste Combined with expand.grid

subset_datlist

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datas...

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

Utilities: Various Strings Representing System Time

tw.imputation

Two-Way Imputation

VariableNames2String

Stringing Variable Names with Line Breaks

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

with.miceadds

Evaluates an Expression for (Nested) Multiply Imputed Datasets

write.datlist

Write a List of Multiply Imputed Datasets

write.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

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>).

  • Maintainer: Alexander Robitzsch
  • License: GPL (>= 2)
  • Last published: 2024-01-09