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miceadds (version 2.2-0)

Some Additional Multiple Imputation Functions, Especially for 'mice'

Description

Contains some auxiliary functions for multiple imputation which complements existing functionality in R. In addition to some utility functions, main features include plausible value imputation, multilevel imputation functions, imputation using partial least squares (PLS) for high dimensional predictors, nested multiple imputation.

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Version

Install

install.packages('miceadds')

Monthly Downloads

9,450

Version

2.2-0

License

GPL (>= 2)

Maintainer

Alexander Robitzsch

Last Published

January 11th, 2017

Functions in miceadds (2.2-0)

Datasets from Enders' Missing Data Book

Example Datasets for miceadds Package

Datasets from Grahams Missing Data Book

Datasets from Allison's Missing Data Book

cxxfunction.copy

R Utilities: Copy of an Rcpp File

data.largescale

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

data.smallscale

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

complete.miceadds

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

R Utilities: Removing CF Line Endings

Dataset Internet

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

Creates Objects of Class datlist or nested.datlist

Converts a jomo Data Frame in Long Format into a List of Datasets

index.dataframe

R Utilities: Include an Index to a Data Frame

Splits a File Name into Parts

R Utilities: Vector Based Version of grep

Calculation of Groupwise Descriptive Statistics for Matrices

Kernel PLS Regression

Moves Files from One Directory to Another Directory

library_install

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

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

mice.impute.2lonly.function

Imputation at Level 2 (in miceadds)

Cohen's d Effect Size for Missingness Indicators

mice.impute.bygroup

Groupwise Imputation Function

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

mice.impute.2l.latentgroupmean.ML

Imputation of Latent and Manifest Group Means for Multilevel Data

Multiple Imputation by Chained Equations using One Chain

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

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

mice.impute.grouped

Imputation of a Variable with Grouped Values

mice.impute.eap

Imputation of a Variable with a Known Posterior Distribution

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching

mice.impute.weighted.norm

Imputation by a Weighted Linear Normal Regression

Wald Test for Nested Multiply Imputed Datasets

R Utilities: Formatting R Output on the R Console

R Utilities: Loading Rdata Files in a Convenient Way

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

Standardization of a Matrix

miceadds-package

Some Additional Multiple Imputation Functions, Especially for mice

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

mice.impute.2l.contextual.norm

Imputation by Normal Linear Regression with Contextual Variables

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

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching with Contextual Variables

Export mids object to MLwiN

sumpreserving.rounding

Sum Preserving Rounding

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

R Utilities: Various Strings Representing System Time

VariableNames2String

Stringing Variable Names with Line Breaks

R Utilities: Loading/Reading Data Files using miceadds

Cluster Robust Standard Errors for Linear Models and General Linear Models

Nested Multiple Imputation

miceadds-defunct

Defunct miceadds Functions

Principal Component Analysis with Ridge Regularization

R Utilities: Save a Data Frame in Rdata Format

Statistical Inference for Multiply Imputed Datasets

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

Rhat Convergence Statistic of a mice Imputation

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

Descriptive Statistics for a Vector or a Data Frame

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Pooling for Nested Multiple Imputation

R Utilities: Evaluates a String as an Expression in R

R Utilities: Scan a Character Vector

R Utilities: Source All R Files Within a Directory

Write a List of Multiply Imputed Datasets

Reading and Writing Files in Fixed Width Format

R Utilities: Saving/Writing Data Files using miceadds

R Utilities: R Session Information

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

Writing a Data Frame into SPSS Format Using PSPP Software

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

nestedList2List

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