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miceadds (version 1.6-0)

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

1.6-0

License

GPL (>= 2)

Maintainer

Alexander Robitzsch

Last Published

January 30th, 2016

Functions in miceadds (1.6-0)

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

RUtilities: Removing CF Line Endings

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

mice.impute.grouped

Imputation of a Variable with Grouped Values

mice.impute.2l.contextual.norm

Imputation by Normal Linear Regression with Contextual Variables

Datasets from Allison's Missing Data Book

miceadds-package

Some Additional Multiple Imputation Functions, Especially for mice

Principal Component Analysis with Ridge Regularization

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

Evaluates an Expression for (Nested) Multiply Imputed Datasets

Rhat Convergence Statistic of a mice Imputation

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

complete.miceadds

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

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

cxxfunction.copy

RUtilities: Copy of an Rcpp File

Calculation of Groupwise Descriptive Statistics for Matrices

Writing a Data Frame into SPSS Format Using PSPP Software

Datasets from Grahams Missing Data Book

RUtilities: Saving/Writing Data Files using miceadds

data.smallscale

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

Creates Objects of Class datlist or nested.datlist

Export mids object to MLwiN

Kernel PLS Regression

RUtilities: Vector Based Version of grep

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching

mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching with Contextual Variables

RUtilities: Loading/Reading Data Files using miceadds

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

Nested Multiple Imputation

Datasets from Enders' Missing Data Book

Dataset Internet

str_C.expand.grid

RUtilities: String Paste Combined with expand.grid

mice.impute.weighted.norm

Imputation by a Weighted Linear Normal Regression

Cluster Robust Standard Errors for Linear Models and General Linear Models

mice.impute.2l.pls2

Imputation using Partial Least Squares for Dimension Reduction

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

RUtilities: Scan a Character Vector

Pooling for Nested Multiple Imputation

index.dataframe

RUtilities: Include an Index to a Data Frame

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

mice.impute.2l.plausible.values

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

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

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

RUtilities: Evaluates a String as an Expression in R

Wald Test for Nested Multiply Imputed Datasets

Two-Way Imputation

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

RUtilities: Source All R Files Within a Directory

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

library_install

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

sumpreserving.rounding

Sum Preserving Rounding

RUtilities: Various Strings Representing System Time

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

RUtilities: Formatting R Output on the RConsole

RUtilities: Rounding DIN 1333 (Kaufmaennisches Runden)

Combination of Correlations for Multiply Imputed Data Sets

Standardization of a Matrix

data.largescale

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

mice.impute.2lonly.pmm2

Imputation at Level 2 (in miceadds)

Multiple Imputation by Chained Equations using One Chain

RUtilities: RSession Information

Example Datasets for miceadds Package

mice.impute.2l.latentgroupmean

Imputation of Latent and Manifest Group Means for Multilevel Data

RUtilities: Loading Rdata Files in a Convenient Way

RUtilities: Save a Data Frame in Rdata Format

mice.impute.2l.eap

Imputation of a Variable with a Known Posterior Distribution