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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

4,785

Version

2.10-14

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Repository

https://github.com/alexanderrobitzsch/miceadds

Maintainer

Alexander Robitzsch

Last Published

March 29th, 2018

Functions in miceadds (2.10-14)

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

R Utilities: Evaluates a String as an Expression in R

R Utilities: Removing CF Line Endings

complete.miceadds

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

Calculation of Groupwise Descriptive Statistics for Matrices

R Utilities: R Session Information

Wald Test for Nested Multiply Imputed Datasets

Rfunction_include_argument_values

Utility Functions for Writing R Functions

VariableNames2String

Stringing Variable Names with Line Breaks

Rhat Convergence Statistic of a mice Imputation

Datasets from Enders' Missing Data Book

data.largescale

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

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

Example Datasets for miceadds Package

Dataset Internet

Creates Objects of Class datlist or nested.datlist

data.smallscale

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

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

Datasets from Allison's Missing Data Book

Kernel PLS Regression

Datasets from Grahams Missing Data Book

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

Some Functionality for Strings and File Names

Moves Files from One Directory to Another Directory

R Utilities: Vector Based Version of grep

R Utilities: Loading/Reading Data Files using miceadds

R Utilities: Loading Rdata Files in a Convenient Way

Indicator Function for Analyzing Coverage

mice.impute.2l.contextual.pmm

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

Simulating Normally Distributed Data

index.dataframe

R Utilities: Include an Index to a Data Frame

Multiple Imputation by Chained Equations using One Chain

Analysis of Variance for Multiply Imputed Data Sets (Using the

D_{2}

library_install

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

Standardization of a Matrix

Cluster Robust Standard Errors for Linear Models and General Linear Models

Cohen's d Effect Size for Missingness Indicators

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

mice.impute.eap

Imputation of a Variable with a Known Posterior Distribution

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

mice.impute.2l.latentgroupmean.ml

Imputation of Latent and Manifest Group Means for Multilevel Data

mice.impute.bygroup

Groupwise Imputation Function

mice.impute.ml.lmer

Multilevel Imputation in lme4

mice.impute.grouped

Imputation of a Variable with Grouped Values

mice.impute.plausible.values

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

mice.impute.2lonly.function

Imputation at Level 2 (in miceadds)

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

miceadds-defunct

Defunct miceadds Functions

Nested Multiple Imputation

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

Inference for Correlations and Covariances for Multiply Imputed Datasets

miceadds-package

Some Additional Multiple Imputation Functions, Especially for mice

mice_impute_2l_lmer

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

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

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

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

Export mids object to MLwiN

nestedList2List

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

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

Pooling for Nested Multiple Imputation

Principal Component Analysis with Ridge Regularization

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

R Utilities: Saving/Writing Data Files using miceadds

Statistical Inference for Multiply Imputed Datasets

R Utilities: Scan a Character Vector

R Utilities: Formatting R Output on the R Console

R Utilities: Save a Data Frame in Rdata Format

Descriptive Statistics for a Vector or a Data Frame

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Two-Way Imputation

sumpreserving.rounding

Sum Preserving Rounding

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

R Utilities: Source All R Files Within a Directory

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

R Utilities: Various Strings Representing System Time

Writing a Data Frame into SPSS Format Using PSPP Software

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

Write a List of Multiply Imputed Datasets

Reading and Writing Files in Fixed Width Format

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object