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miceadds (version 1.4-0)

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, and two-way imputation.

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Version

Install

install.packages('miceadds')

Monthly Downloads

9,001

Version

1.4-0

License

GPL (>= 2)

Maintainer

Alexander Robitzsch

Last Published

November 4th, 2015

Functions in miceadds (1.4-0)

data.smallscale

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

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

Datasets from Allison's Missing Data Book

Wald Test for Nested Multiply Imputed Datasets

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

Cluster Robust Standard Errors for Linear Models and General Linear Models

mice.impute.2l.latentgroupmean

Imputation of Latent and Manifest Group Means for Multilevel Data

library_install

RUtilities: Loading a Package or Installation of a Package if Necessary

RUtilities: Removing CF Line Endings

complete.miceadds

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

Datasets from Enders' Missing Data Book

Example Datasets for miceadds Package

Dataset Internet

Two-Way Imputation

RUtilities: RSession Information

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

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

data.largescale

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

mice.impute.grouped

Imputation of a Variable with Grouped Values

sumpreserving.rounding

Sum Preserving Rounding

RUtilities: Saving/Writing Data Files using miceadds

Datasets from Grahams Missing Data Book

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

Nested Multiple Imputation

mice.impute.2l.pls2

Imputation using Partial Least Squares for Dimension Reduction

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

Calculation of Groupwise Descriptive Statistics for Matrices

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

index.dataframe

RUtilities: Include an Index to a Data Frame

Pooling for Nested Multiple Imputation

mice.impute.2l.eap

Imputation of a Variable with a Known Posterior Distribution

mice.impute.weighted.norm

Imputation by a Weighted Linear Normal Regression

RUtilities: Save a Data Frame in Rdata Format

Principal Component Analysis with Ridge Regularization

RUtilities: Rounding DIN 1333 (Kaufmaennisches Runden)

RUtilities: Evaluates a String as an Expression in R

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

mice.impute.2l.plausible.values

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

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

miceadds-package

Some Additional Multiple Imputation Functions, Especially for mice

Combination of Correlations for Multiply Imputed Data Sets

RUtilities: Vector Based Version of grep

RUtilities: Various Strings Representing System Time

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

mice.impute.2l.contextual.norm

Imputation by Normal Linear Regression with Contextual Variables

Standardization of a Matrix

Rhat Convergence Statistic of a mice Imputation

Export mids object to MLwiN

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

mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching with Contextual Variables

RUtilities: Source All R Files Within a Directory

mice.impute.2lonly.pmm2

Imputation at Level 2 (in miceadds)

Kernel PLS Regression

RUtilities: Loading Rdata Files in a Convenient Way

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

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

str_C.expand.grid

RUtilities: String Paste Combined with expand.grid

RUtilities: Formatting R Output on the RConsole

Writing a Data Frame into SPSS Format Using PSPP Software

cxxfunction.copy

RUtilities: Copy of an Rcpp File

RUtilities: Loading/Reading Data Files using miceadds

RUtilities: Scan a Character Vector

Multiple Imputation by Chained Equations using One Chain

Evaluates an Expression for (Nested) Multiply Imputed Datasets