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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

5,032

Version

3.17-44

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Repository

https://github.com/alexanderrobitzsch/miceadds

Homepage

https://sites.google.com/view/alexander-robitzsch/software

Maintainer

Alexander Robitzsch

Last Published

January 9th, 2024

Functions in miceadds (3.17-44)

Rfunction_include_argument_values

Utility Functions for Writing R Functions

R Utilities: Removing CF Line Endings

Calculation of Groupwise Descriptive Statistics for Matrices

Wald Test for Nested Multiply Imputed Datasets

Example Datasets for miceadds Package

data.smallscale

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

Kernel PLS Regression

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

cxxfunction.copy

R Utilities: Copy of an Rcpp File

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

R Utilities: R Session Information

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

R Utilities: Evaluates a String as an Expression in R

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

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

Datasets from Allison's Missing Data Book

R Utilities: Vector Based Versions of grep

VariableNames2String

Stringing Variable Names with Line Breaks

Moves Files from One Directory to Another Directory

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

mice.impute.2l.latentgroupmean.ml

Imputation of Latent and Manifest Group Means for Multilevel Data

R Utilities: Loading/Reading Data Files using miceadds

Datasets from Grahams Missing Data Book

Dataset Internet

Some Functionality for Strings and File Names

data.largescale

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

Standardization of a Matrix

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

Converting an Object of class amelia

Rhat Convergence Statistic of a mice Imputation

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

Indicator Function for Analyzing Coverage

mice.impute.catpmm

Imputation of a Categorical Variable Using Multivariate Predictive Mean Matching

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

index.dataframe

R Utilities: Include an Index to a Data Frame

complete.miceadds

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

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

Export mids object to MLwiN

mice.impute.bygroup

Groupwise Imputation Function

mice.impute.2lonly.function

Imputation at Level 2 (in miceadds)

sumpreserving.rounding

Sum Preserving Rounding

Datasets from Enders' Missing Data Book

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

Two-Way Imputation

R Utilities: Loading Rdata Files in a Convenient Way

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

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

nestedList2List

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

Pooling for Nested Multiple Imputation

mice.impute.smcfcs

Substantive Model Compatible Multiple Imputation (Single Level)

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

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

Inference for Correlations and Covariances for Multiply Imputed Datasets

Creates Objects of Class datlist or nested.datlist

mice.impute.constant

Imputation Using a Fixed Vector

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

MCMC Estimation for Mixed Effects Model

R Utilities: Scan a Character Vector

Nested Multiple Imputation

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

Synthesizing Method for Fixed Values by Design in synthpop

Simulating Normally Distributed Data

mice_imputation_2l_lmer

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

Cohen's d Effect Size for Missingness Indicators

Multiple Imputation by Chained Equations using One Chain

mice.impute.ml.lmer

Multilevel Imputation Using lme4

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

Principal Component Analysis with Ridge Regularization

ma_lme4_formula

Utility Functions for Working with lme4 Formula Objects

Simulation of Multivariate Linearly Related Non-Normal Variables

R Utilities: Formatting R Output on the R Console

Arguments for mice::mice Function

miceadds-defunct

Defunct miceadds Functions

Generation of Synthetic Data Utilizing Data Augmentation

Synthesizing Method for synthpop Using a Formula Interface

miceadds-package

tools:::Rd_package_title("miceadds")

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

Writing a Data Frame into SPSS Format Using PSPP Software

mice.impute.imputeR.lmFun

Wrapper Function to Imputation Methods in the imputeR Package

Statistical Inference for Multiply Imputed Datasets

miceadds-utilities

Utility Functions in miceadds

mice.impute.rlm

Imputation of a Linear Model by Bayesian Bootstrap

Using a mice Imputation Method in the synthpop Package

mice.impute.simputation

Wrapper Function to Imputation Methods in the simputation Package

Constructs Synthetic Dataset with mice Imputation Methods

Descriptive Statistics for a Vector or a Data Frame

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

R Utilities: Various Strings Representing System Time

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

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

R Utilities: Save a Data Frame in Rdata Format

Reading and Writing Files in Fixed Width Format

R Utilities: Saving/Writing Data Files using miceadds

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

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Write a List of Multiply Imputed Datasets

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object