Learn R Programming

⚠️There's a newer version (1.10.1) of this package.Take me there.

micemd (version 1.6.0)

Multiple Imputation by Chained Equations with Multilevel Data

Description

Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) , the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.

Copy Link

Version

Install

install.packages('micemd')

Monthly Downloads

738

Version

1.6.0

License

GPL-2 | GPL-3

Maintainer

Vincent Audigier

Last Published

July 9th, 2019

Functions in micemd (1.6.0)

mice.impute.2l.glm.bin

Imputation of univariate missing data using a Bayesian logistic mixed model based on non-informative prior distributions

mice.impute.2l.jomo

Imputation of univariate missing data by a Bayesian multivariate generalized model based on conjugate priors

Multiple Imputation by Chained Equations with Multilevel Data

Graphical investigation for the number of generated datasets

Overimputation diagnostic plot

Parallel calculations for Multivariate Imputation by Chained Equations

mice.impute.2l.2stage.bin

Imputation by a two-level logistic model based on a two-stage estimator

mice.impute.2l.2stage.pmm

Predictive mean matching imputation for two-level variable

A simulated Individual Patient Data (IPD) meta-analysis with missing values.

mice.impute.2l.2stage.pois

Imputation by a two-level Poisson model based on a two-stage estimator

An incomplete two-level dataset which consists of A/AS-level examination data from England

find.defaultMethod

Suggestion of conditional imputation models to use accordingly to the incomplete dataset

mice.impute.2l.2stage.norm

Imputation by a two-level heteroscedastic normal model based on a two-stage estimator

mice.impute.2l.glm.norm

Imputation of univariate missing data using a Bayesian linear mixed model based on non-informative prior distributions

mice.impute.2l.glm.pois

Imputation of count variable using a Bayesian mixed model based on non-informative prior distributions