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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.

Manual

The manual may be found here https://alexanderrobitzsch.github.io/miceadds/

CRAN version

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")

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Version

Install

install.packages('miceadds')

Monthly Downloads

5,266

Version

3.11-6

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Repository

https://github.com/alexanderrobitzsch/miceadds

Homepage

https://sites.google.com/site/alexanderrobitzsch2/software

Maintainer

Alexander Robitzsch

Last Published

January 21st, 2021

Functions in miceadds (3.11-6)

R Utilities: Removing CF Line Endings

Wald Test for Nested Multiply Imputed Datasets

VariableNames2String

Stringing Variable Names with Line Breaks

Rhat Convergence Statistic of a mice Imputation

Calculation of Groupwise Descriptive Statistics for Matrices

Rfunction_include_argument_values

Utility Functions for Writing R Functions

complete.miceadds

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

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

R Utilities: R Session Information

R Utilities: Evaluates a String as an Expression in R

Example Datasets for miceadds Package

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

Datasets from Grahams Missing Data Book

Datasets from Enders' Missing Data Book

Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory)

data.smallscale

Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables)

Some Functionality for Strings and File Names

data.largescale

Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)

Dataset Internet

Indicator Function for Analyzing Coverage

R Utilities: Vector Based Versions of grep

index.dataframe

R Utilities: Include an Index to a Data Frame

Creates Objects of Class datlist or nested.datlist

Cluster Robust Standard Errors for Linear Models and General Linear Models

Statistical Inference for Fixed and Random Structure for Fitted Models in lme4

Simulating Normally Distributed Data

ma_lme4_formula

Utility Functions for Working with lme4 Formula Objects

Moves Files from One Directory to Another Directory

Cohen's d Effect Size for Missingness Indicators

Analysis of Variance for Multiply Imputed Data Sets (Using the \(D_2\) Statistic)

Converts a jomo Data Frame in Long Format into a List of Datasets or an Object of Class mids

mice.impute.grouped

Imputation of a Variable with Grouped Values

R Utilities: Loading/Reading Data Files using miceadds

R Utilities: Loading Rdata Files in a Convenient Way

Simulating Univariate Data from Fleishman Power Normal Transformations

mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables

Multiple Imputation by Chained Equations using One Chain

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.imputeR.lmFun

Wrapper Function to Imputation Methods in the imputeR Package

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

mice.impute.ml.lmer

Multilevel Imputation Using lme4

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

mice.impute.constant

Imputation Using a Fixed Vector

mice.impute.bygroup

Groupwise Imputation Function

mice.impute.rlm

Imputation of a Linear Model by Bayesian Bootstrap

cxxfunction.copy

R Utilities: Copy of an Rcpp File

miceadds-defunct

Defunct miceadds Functions

miceadds-package

Datasets from Allison's Missing Data Book

Kernel PLS Regression

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

library_install

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

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

Nested Multiple Imputation

Standardization of a Matrix

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

Export mids object to MLwiN

mice.impute.simputation

Wrapper Function to Imputation Methods in the simputation Package

mice_imputation_2l_lmer

Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using lme4 or blme

mice.impute.plausible.values

Plausible Value Imputation using Classical Test Theory and Based on Individual Likelihood

Arguments for mice::mice Function

mice.impute.smcfcs

Substantive Model Compatible Multiple Imputation (Single Level)

Principal Component Analysis with Ridge Regularization

Pooling for Nested Multiple Imputation

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.synthpop

Using a synthpop Synthesizing Method in the mice Package

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

Synthesizing Method for Fixed Values by Design in synthpop

sumpreserving.rounding

Sum Preserving Rounding

nestedList2List

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

MCMC Estimation for Mixed Effects Model

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

Using a mice Imputation Method in the synthpop Package

Synthesizing Method for synthpop Using a Formula Interface

Inference for Correlations and Covariances for Multiply Imputed Datasets

Statistical Inference for Multiply Imputed Datasets

Two-Way Imputation

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

miceadds-utilities

Utility Functions in miceadds

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

Reading and Writing Files in Fixed Width Format

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

R Utilities: Save a Data Frame in Rdata Format

R Utilities: Saving/Writing Data Files using miceadds

Simulation of Multivariate Linearly Related Non-Normal Variables

R Utilities: Formatting R Output on the R Console

R Utilities: Scan a Character Vector

Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

R Utilities: Source all R or Rcpp Files within a Directory

Writing a Data Frame into SPSS Format Using PSPP Software

Descriptive Statistics for a Vector or a Data Frame

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Write a List of Multiply Imputed Datasets

Constructs Synthetic Dataset with mice Imputation Methods

R Utilities: Various Strings Representing System Time