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miceadds (version 1.5-0)

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

1.5-0

License

GPL (>= 2)

Maintainer

Alexander Robitzsch

Last Published

November 22nd, 2015

Functions in miceadds (1.5-0)

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

Rhat Convergence Statistic of a mice Imputation

RUtilities: Loading Rdata Files in a Convenient Way

complete.miceadds

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

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching

Nested Multiple Imputation

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

mice.impute.2lonly.pmm2

Imputation at Level 2 (in miceadds)

data.smallscale

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

mice.impute.2l.eap

Imputation of a Variable with a Known Posterior Distribution

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

miceadds-package

Some Additional Multiple Imputation Functions, Especially for mice

cxxfunction.copy

RUtilities: Copy of an Rcpp File

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

index.dataframe

RUtilities: Include an Index to a Data Frame

Wald Test for Nested Multiply Imputed Datasets

Datasets from Allison's Missing Data Book

mice.impute.2l.contextual.norm

Imputation by Normal Linear Regression with Contextual Variables

Calculation of Groupwise Descriptive Statistics for Matrices

data.largescale

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

RUtilities: Rounding DIN 1333 (Kaufmaennisches Runden)

RUtilities: Vector Based Version of grep

RUtilities: Evaluates a String as an Expression in R

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

Standardization of a Matrix

mice.impute.weighted.norm

Imputation by a Weighted Linear Normal Regression

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

Combination of Correlations for Multiply Imputed Data Sets

RUtilities: Loading/Reading Data Files using miceadds

Two-Way Imputation

RUtilities: Various Strings Representing System Time

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

Cluster Robust Standard Errors for Linear Models and General Linear Models

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

RUtilities: Source All R Files Within a Directory

Evaluates an Expression for (Nested) Multiply Imputed Datasets

sumpreserving.rounding

Sum Preserving Rounding

Export mids object to MLwiN

Principal Component Analysis with Ridge Regularization

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

Writing a Data Frame into SPSS Format Using PSPP Software

RUtilities: RSession Information

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

mice.impute.grouped

Imputation of a Variable with Grouped Values

Datasets from Grahams Missing Data Book

Multiple Imputation by Chained Equations using One Chain

mice.impute.2l.latentgroupmean

Imputation of Latent and Manifest Group Means for Multilevel Data

Example Datasets for miceadds Package

RUtilities: Scan a Character Vector

Dataset Internet

mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching with Contextual Variables

RUtilities: Saving/Writing Data Files using miceadds

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

Kernel PLS Regression

mice.impute.2l.pls2

Imputation using Partial Least Squares for Dimension Reduction

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

str_C.expand.grid

RUtilities: String Paste Combined with expand.grid

RUtilities: Removing CF Line Endings

Datasets from Enders' Missing Data Book

library_install

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

RUtilities: Formatting R Output on the RConsole

Pooling for Nested Multiple Imputation

RUtilities: Save a Data Frame in Rdata Format