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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.

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

8,468

Version

3.0-16

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

December 11th, 2018

Functions in miceadds (3.0-16)

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

Datasets from Enders' Missing Data Book

Datasets from Grahams Missing Data Book

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

Rfunction_include_argument_values

Utility Functions for Writing R Functions

Some Functionality for Strings and File Names

Rhat Convergence Statistic of a mice Imputation

Example Datasets for miceadds Package

R Utilities: Loading Rdata Files in a Convenient Way

data.smallscale

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

Indicator Function for Analyzing Coverage

R Utilities: Vector Based Versions of grep

ma_lme4_formula

Utility Functions for Working with lme4 Formula Objects

R Utilities: R Session Information

Simulating Normally Distributed Data

complete.miceadds

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

R Utilities: Removing CF Line Endings

mice.impute.grouped

Imputation of a Variable with Grouped Values

Dataset Internet

data.largescale

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

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

Arguments for mice::mice Function

Moves Files from One Directory to Another Directory

Simulating Univariate Data from Fleishman Power Normal Transformations

miceadds-defunct

Defunct miceadds Functions

R Utilities: Loading/Reading Data Files using miceadds

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)

Export mids object to MLwiN

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

miceadds-package

miceadds-utilities

Utility Functions in miceadds

Cohen's d Effect Size for Missingness Indicators

MCMC Estimation for Mixed Effects Model

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

VariableNames2String

Stringing Variable Names with Line Breaks

mice.impute.ml.lmer

Multilevel Imputation Using lme4

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

Calculation of Groupwise Descriptive Statistics for Matrices

cxxfunction.copy

R Utilities: Copy of an Rcpp File

Descriptive Statistics for a Vector or a Data Frame

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

Write a List of Multiply Imputed Datasets

Wald Test for Nested Multiply Imputed Datasets

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

mice.impute.plausible.values

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

Writing a Data Frame into SPSS Format Using PSPP Software

R Utilities: Save a Data Frame in Rdata Format

Reading and Writing Files in Fixed Width Format

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

Creates Objects of Class datlist or nested.datlist

Datasets from Allison's Missing Data Book

Kernel PLS Regression

library_install

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

Cluster Robust Standard Errors for Linear Models and General Linear Models

index.dataframe

R Utilities: Include an Index to a Data Frame

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

R Utilities: Formatting R Output on the R Console

Multiple Imputation by Chained Equations using One Chain

Principal Component Analysis with Ridge Regularization

R Utilities: Saving/Writing Data Files using miceadds

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

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

Standardization of a Matrix

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

Evaluates an Expression for (Nested) Multiply Imputed Datasets

mice.impute.2l.contextual.pmm

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

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

mice.impute.bygroup

Groupwise Imputation Function

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

nestedList2List

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

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

mice.impute.eap

Imputation of a Variable with a Known Posterior Distribution

Simulation of Multivariate Linearly Related Non-Normal Variables

Pooling for Nested Multiple Imputation

Statistical Inference for Multiply Imputed Datasets

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

Nested Multiple Imputation

sumpreserving.rounding

Sum Preserving Rounding

mice_impute_2l_lmer

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

Inference for Correlations and Covariances for Multiply Imputed Datasets

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

R Utilities: Scan a Character Vector

R Utilities: Various Strings Representing System Time

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

Two-Way Imputation

R Utilities: Evaluates a String as an Expression in R