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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.

Manual

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

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

9,001

Version

3.8-9

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/alexanderrobitzsch/miceadds

Homepage

https://sites.google.com/site/alexanderrobitzsch2/software

Maintainer

Alexander Robitzsch

Last Published

February 17th, 2020

Functions in miceadds (3.8-9)

VariableNames2String

Stringing Variable Names with Line Breaks

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

Kernel PLS Regression

R Utilities: Evaluates a String as an Expression in R

cxxfunction.copy

R Utilities: Copy of an Rcpp File

library_install

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

complete.miceadds

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

Standardization of a Matrix

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

Rfunction_include_argument_values

Utility Functions for Writing R Functions

Rhat Convergence Statistic of a mice Imputation

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

Example Datasets for miceadds Package

Some Functionality for Strings and File Names

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

R Utilities: Removing CF Line Endings

data.smallscale

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

Creates Objects of Class datlist or nested.datlist

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.plausible.values

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

ma_lme4_formula

Utility Functions for Working with lme4 Formula Objects

Simulating Normally Distributed Data

Datasets from Allison's Missing Data Book

Datasets from Grahams Missing Data Book

Wald Test for Nested Multiply Imputed Datasets

Datasets from Enders' Missing Data Book

Calculation of Groupwise Descriptive Statistics for Matrices

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

Moves Files from One Directory to Another Directory

R Utilities: Vector Based Versions of grep

Cluster Robust Standard Errors for Linear Models and General Linear Models

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

Indicator Function for Analyzing Coverage

mice.impute.2l.contextual.pmm

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

Multiple Imputation by Chained Equations using One Chain

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

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

Nested Multiple Imputation

Simulation of Multivariate Linearly Related Non-Normal Variables

Descriptive Statistics for a Vector or a Data Frame

R Utilities: Formatting R Output on the R Console

Simulating Univariate Data from Fleishman Power Normal Transformations

mice.impute.bygroup

Groupwise Imputation Function

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

R Utilities: Loading/Reading Data Files using miceadds

R Utilities: Loading Rdata Files in a Convenient Way

mice.impute.2l.latentgroupmean.ml

Imputation of Latent and Manifest Group Means for Multilevel Data

mice.impute.grouped

Imputation of a Variable with Grouped Values

miceadds-defunct

Defunct miceadds Functions

mice.impute.2lonly.function

Imputation at Level 2 (in miceadds)

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

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

mice.impute.smcfcs

Substantive Model Compatible Multiple Imputation (Single Level)

miceadds-package

sumpreserving.rounding

Sum Preserving Rounding

MCMC Estimation for Mixed Effects Model

R Utilities: Save a Data Frame in Rdata Format

nestedList2List

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

R Utilities: Saving/Writing Data Files using miceadds

R Utilities: Various Strings Representing System Time

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

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

mice_imputation_2l_lmer

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

Arguments for mice::mice Function

Principal Component Analysis with Ridge Regularization

Inference for Correlations and Covariances for Multiply Imputed Datasets

Pooling for Nested Multiple Imputation

Reading and Writing Files in Fixed Width Format

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

Cohen's d Effect Size for Missingness Indicators

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

mice.impute.ml.lmer

Multilevel Imputation Using lme4

Export mids object to MLwiN

Statistical Inference for Multiply Imputed Datasets

mice.impute.rlm

Imputation of a Linear Model by Bayesian Bootstrap

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

miceadds-utilities

Utility Functions in miceadds

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

R Utilities: Scan a Character Vector

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

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

Two-Way Imputation

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

R Utilities: R Session Information

data.largescale

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

Dataset Internet