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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

5,266

Version

3.1-37

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

March 18th, 2019

Functions in miceadds (3.1-37)

complete.miceadds

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

Dataset Internet

data.largescale

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

R Utilities: R Session Information

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

VariableNames2String

Stringing Variable Names with Line Breaks

Simulating Univariate Data from Fleishman Power Normal Transformations

Moves Files from One Directory to Another Directory

Some Functionality for Strings and File Names

Cluster Robust Standard Errors for Linear Models and General Linear Models

R Utilities: Loading Rdata Files in a Convenient Way

R Utilities: Loading/Reading Data Files using miceadds

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

Multiple Imputation by Chained Equations using One Chain

index.dataframe

R Utilities: Include an Index to a Data Frame

mice.impute.ml.lmer

Multilevel Imputation Using lme4

mice.impute.2l.contextual.pmm

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

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

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.tricube.pmm

Imputation by Tricube Predictive Mean Matching

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

Export mids object to MLwiN

Arguments for mice::mice Function

miceadds-defunct

Defunct miceadds Functions

Cohen's d Effect Size for Missingness Indicators

mice.impute.bygroup

Groupwise Imputation Function

MCMC Estimation for Mixed Effects Model

mice.impute.eap

Imputation of a Variable with a Known Posterior Distribution

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

Pooling for Nested Multiple Imputation

Statistical Inference for Multiply Imputed Datasets

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

Writing a Data Frame into SPSS Format Using PSPP Software

Rfunction_include_argument_values

Utility Functions for Writing R Functions

Rhat Convergence Statistic of a mice Imputation

Inference for Correlations and Covariances for Multiply Imputed Datasets

Creates Objects of Class datlist or nested.datlist

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

mice.impute.2l.latentgroupmean.ml

Imputation of Latent and Manifest Group Means for Multilevel Data

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

R Utilities: Saving/Writing Data Files using miceadds

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

mice.impute.2lonly.function

Imputation at Level 2 (in miceadds)

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

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

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

R Utilities: Evaluates a String as an Expression in R

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Calculation of Groupwise Descriptive Statistics for Matrices

Wald Test for Nested Multiply Imputed Datasets

R Utilities: Various Strings Representing System Time

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

Descriptive Statistics for a Vector or a Data Frame

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

Two-Way Imputation

Example Datasets for miceadds Package

Datasets from Enders' Missing Data Book

data.smallscale

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

R Utilities: Vector Based Versions of grep

Indicator Function for Analyzing Coverage

Datasets from Grahams Missing Data Book

Standardization of a Matrix

library_install

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

Kernel PLS Regression

ma_lme4_formula

Utility Functions for Working with lme4 Formula Objects

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

Simulating Normally Distributed Data

miceadds-utilities

Utility Functions in miceadds

miceadds-package

mice.impute.grouped

Imputation of a Variable with Grouped Values

nestedList2List

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

Simulation of Multivariate Linearly Related Non-Normal Variables

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

R Utilities: Save a Data Frame in Rdata Format

Nested Multiple Imputation

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

mice_impute_2l_lmer

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

sumpreserving.rounding

Sum Preserving Rounding

Principal Component Analysis with Ridge Regularization

R Utilities: Formatting R Output on the R Console

R Utilities: Scan a Character Vector

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

Write a List of Multiply Imputed Datasets

Reading and Writing Files in Fixed Width Format

R Utilities: Removing CF Line Endings

cxxfunction.copy

R Utilities: Copy of an Rcpp File

Datasets from Allison's Missing Data Book