miceadds v3.2-48

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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 included. 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>) and substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>).

Readme

miceadds

Some Additional Multiple Imputation Functions, Especially for 'mice'

If you use miceadds and have suggestions for improvement or have found bugs, please email me at robitzsch@ipn.uni-kiel.de.

CRAN version

!--- [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version-last-release/miceadds)](https://cran.r-project.org/package=miceadds)    --

The official version of miceadds is hosted on CRAN and may be found here. The CRAN version can be installed from within R using:

utils::install.packages("miceadds")

GitHub version

The version hosted here is the development version of miceadds. The GitHub version can be installed using devtools as:

devtools::install_github("alexanderrobitzsch/miceadds")

Functions in miceadds

Name Description
complete.miceadds Creates Imputed Dataset from a mids.nmi or mids.1chain Object
crlrem R Utilities: Removing CF Line Endings
Rsessinfo R Utilities: R Session Information
VariableNames2String Stringing Variable Names with Line Breaks
files_move Moves Files from One Directory to Another Directory
fleishman_sim Simulating Univariate Data from Fleishman Power Normal Transformations
ma_lme4_formula Utility Functions for Working with lme4 Formula Objects
ma_rmvnorm Simulating Normally Distributed Data
data.internet Dataset Internet
data.largescale Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)
ma.scale2 Standardization of a Matrix
mice.impute.bygroup Groupwise Imputation Function
mice.impute.grouped Imputation of a Variable with Grouped Values
mice.nmi Nested Multiple Imputation
ma.wtd.statNA Some Multivariate Descriptive Statistics for Weighted Data in miceadds
mice.1chain Multiple Imputation by Chained Equations using One Chain
NestedImputationList Functions for Analysis of Nested Multiply Imputed Datasets
Reval R Utilities: Evaluates a String as an Expression in R
mice_imputation_2l_lmer Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using lme4 or blme
save.data R Utilities: Saving/Writing Data Files using miceadds
mice.impute.2l.contextual.pmm Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables
data.ma Example Datasets for miceadds Package
mice.impute.pmm3 Imputation by Predictive Mean Matching (in miceadds)
subset_datlist Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
scale_datlist Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets
sumpreserving.rounding Sum Preserving Rounding
mice.impute.smcfcs Substantive Model Compatible Multiple Imputation (Single Level)
data.smallscale Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables)
kernelpls.fit2 Kernel PLS Regression
GroupMean Calculation of Groupwise Descriptive Statistics for Matrices
library_install R Utilities: Loading a Package or Installation of a Package if Necessary
mi.anova Analysis of Variance for Multiply Imputed Data Sets (Using the \(D_2\) Statistic)
mi_dstat Cohen's d Effect Size for Missingness Indicators
stats0 Descriptive Statistics for a Vector or a Data Frame
str_C.expand.grid R Utilities: String Paste Combined with expand.grid
ml_mcmc MCMC Estimation for Mixed Effects Model
mids2mlwin Export mids object to MLwiN
mice.impute.plausible.values Plausible Value Imputation using Classical Test Theory and Based on Individual Likelihood
NMIwaldtest Wald Test for Nested Multiply Imputed Datasets
cxxfunction.copy R Utilities: Copy of an Rcpp File
Rfunction_include_argument_values Utility Functions for Writing R Functions
data.allison Datasets from Allison's Missing Data Book
mice.impute.pls Imputation using Partial Least Squares for Dimension Reduction
grep.vec R Utilities: Vector Based Versions of grep
Rhat.mice Rhat Convergence Statistic of a mice Imputation
write.datlist Write a List of Multiply Imputed Datasets
in_CI Indicator Function for Analyzing Coverage
write.fwf2 Reading and Writing Files in Fixed Width Format
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)
data.enders Datasets from Enders' Missing Data Book
datlist2mids Converting a List of Multiply Imputed Data Sets into a mids Object
mice.impute.tricube.pmm Imputation by Tricube Predictive Mean Matching
micombine.F Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximation
micombine.chisquare Combination of Chi Square Statistics of Multiply Imputed Datasets
data.graham Datasets from Grahams Missing Data Book
mice.impute.weighted.pmm Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression
datlist_create Creates Objects of Class datlist or nested.datlist
round2 R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)
save.Rdata R Utilities: Save a Data Frame in Rdata Format
write.mice.imputation Export Multiply Imputed Datasets from a mids Object
write.pspp Writing a Data Frame into SPSS Format Using PSPP Software
index.dataframe R Utilities: Include an Index to a Data Frame
filename_split Some Functionality for Strings and File Names
lm.cluster Cluster Robust Standard Errors for Linear Models and General Linear Models
draw.pv.ctt Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory)
output.format1 R Utilities: Formatting R Output on the R Console
jomo2datlist Converts a jomo Data Frame in Long Format into a List of Datasets or an Object of Class mids
pca.covridge Principal Component Analysis with Ridge Regularization
visitSequence.determine Automatic Determination of a Visit Sequence in mice
load.Rdata R Utilities: Loading Rdata Files in a Convenient Way
with.miceadds Evaluates an Expression for (Nested) Multiply Imputed Datasets
load.data R Utilities: Loading/Reading Data Files using miceadds
lmer_vcov Statistical Inference for Fixed and Random Structure for Fitted Models in lme4
mice.impute.hotDeck Imputation of a Variable Using Probabilistic Hot Deck Imputation
mice_inits Arguments for mice::mice Function
mids2datlist Converting a mids, mids.1chain or mids.nmi Object in a Dataset List
micombine.cor Inference for Correlations and Covariances for Multiply Imputed Datasets
miceadds-defunct Defunct miceadds Functions
scan.vec R Utilities: Scan a Character Vector
source.all R Utilities: Source all R or Rcpp Files within a Directory
mice.impute.ml.lmer Multilevel Imputation Using lme4
miceadds-utilities Utility Functions in miceadds
miceadds-package miceadds
nestedList2List Converting a Nested List into a List (and Vice Versa)
nnig_sim Simulation of Multivariate Linearly Related Non-Normal Variables
pool_mi Statistical Inference for Multiply Imputed Datasets
pool.mids.nmi Pooling for Nested Multiple Imputation
systime R Utilities: Various Strings Representing System Time
tw.imputation Two-Way Imputation
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