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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@leibniz-ipn.de. Please always provide a minimal dataset, necessary to demonstrate the problem, a minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and all necessary information on the used librarys, the R version, and the OS it is run on, perhaps a sessionInfo().

Manual

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

CRAN version

The official version of miceadds is hosted on CRAN and can 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 from within R using:

devtools::install_github("alexanderrobitzsch/miceadds")

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Version

Install

install.packages('miceadds')

Monthly Downloads

4,432

Version

3.16-18

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 6th, 2023

Functions in miceadds (3.16-18)

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

Example Datasets for miceadds Package

Datasets from Allison's Missing Data Book

Converting an Object of class amelia

Datasets from Enders' Missing Data Book

Datasets from Grahams Missing Data Book

cxxfunction.copy

R Utilities: Copy of an Rcpp File

Dataset Internet

data.largescale

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

data.smallscale

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

R Utilities: Vector Based Versions of grep

Simulating Univariate Data from Fleishman Power Normal Transformations

Kernel PLS Regression

Indicator Function for Analyzing Coverage

Some Functionality for Strings and File Names

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

Moves Files from One Directory to Another Directory

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

index.dataframe

R Utilities: Include an Index to a Data Frame

Creates Objects of Class datlist or nested.datlist

R Utilities: Loading Rdata Files in a Convenient Way

R Utilities: Loading/Reading Data Files using miceadds

Standardization of a Matrix

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

ma_lme4_formula

Utility Functions for Working with lme4 Formula Objects

Cluster Robust Standard Errors for Linear Models and General Linear Models

library_install

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

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

Simulating Normally Distributed Data

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

mice.impute.2l.contextual.pmm

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

mice.impute.2lonly.function

Imputation at Level 2 (in miceadds)

mice.impute.bygroup

Groupwise Imputation Function

mice.impute.constant

Imputation Using a Fixed Vector

mice.impute.catpmm

Imputation of a Categorical Variable Using Multivariate Predictive Mean Matching

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

mice.impute.imputeR.lmFun

Wrapper Function to Imputation Methods in the imputeR Package

Cohen's d Effect Size for Missingness Indicators

mice.impute.2l.latentgroupmean.ml

Imputation of Latent and Manifest Group Means for Multilevel Data

Multiple Imputation by Chained Equations using One Chain

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

mice.impute.simputation

Wrapper Function to Imputation Methods in the simputation Package

mice.impute.rlm

Imputation of a Linear Model by Bayesian Bootstrap

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

mice.impute.plausible.values

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

mice.impute.synthpop

Using a synthpop Synthesizing Method in the mice Package

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.smcfcs

Substantive Model Compatible Multiple Imputation (Single Level)

mice.impute.ml.lmer

Multilevel Imputation Using lme4

miceadds-utilities

Utility Functions in miceadds

Inference for Correlations and Covariances for Multiply Imputed Datasets

miceadds-package

tools:::Rd_package_title("miceadds")

mice_imputation_2l_lmer

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

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

Arguments for mice::mice Function

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

miceadds-defunct

Defunct miceadds Functions

Nested Multiple Imputation

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

R Utilities: Save a Data Frame in Rdata Format

MCMC Estimation for Mixed Effects Model

R Utilities: Formatting R Output on the R Console

Export mids object to MLwiN

Statistical Inference for Multiply Imputed Datasets

nestedList2List

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

Pooling for Nested Multiple Imputation

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

Simulation of Multivariate Linearly Related Non-Normal Variables

Principal Component Analysis with Ridge Regularization

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

sumpreserving.rounding

Sum Preserving Rounding

R Utilities: Scan a Character Vector

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

Descriptive Statistics for a Vector or a Data Frame

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

R Utilities: Saving/Writing Data Files using miceadds

Synthesizing Method for Fixed Values by Design in synthpop

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

Synthesizing Method for synthpop Using a Formula Interface

Two-Way Imputation

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

Reading and Writing Files in Fixed Width Format

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

Using a mice Imputation Method in the synthpop Package

Generation of Synthetic Data Utilizing Data Augmentation

Constructs Synthetic Dataset with mice Imputation Methods

R Utilities: Various Strings Representing System Time

Write a List of Multiply Imputed Datasets

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Writing a Data Frame into SPSS Format Using PSPP Software

Calculation of Groupwise Descriptive Statistics for Matrices

Wald Test for Nested Multiply Imputed Datasets

R Utilities: R Session Information

VariableNames2String

Stringing Variable Names with Line Breaks

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

R Utilities: Evaluates a String as an Expression in R

complete.miceadds

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

R Utilities: Removing CF Line Endings

Rfunction_include_argument_values

Utility Functions for Writing R Functions

Rhat Convergence Statistic of a mice Imputation