Bayesian Bootstrap Predictive Mean Matching - Multiple and Single Imputation for Discrete Data

Included are two variants of Bayesian Bootstrap Predictive Mean Matching to multiply impute missing data. The first variant is a variable-by-variable imputation combining sequential regression and Predictive Mean Matching (PMM) that has been extended for unordered categorical data. The Bayesian Bootstrap allows for generating approximately proper multiple imputations. The second variant is also based on PMM, but the focus is on imputing several variables at the same time. The suggestion is to use this variant, if the missing-data pattern resembles a data fusion situation, or any other missing-by-design pattern, where several variables have identical missing-data patterns. Both variants can be run as 'single imputation' versions, in case the analysis objective is of a purely descriptive nature.


News

Changes in version BaBooN 0.2-0 (2015-06-15)

  • BBPMM.row: Implementation of Predictive Mean Matching via RcppArmadillo. stepmod replaces the old argument stepwise. Changes in code structure. Additional return values.
  • BBPMM: maxit.multi and maxit.glm replace the old argument maxit. stepmod replaces the old argument stepwise. Changes in code structure. Additional return values.
  • rowimpprep: Additional return values.
  • DESCRIPTION: Forces byte compilation.
  • NAMESPACE: exports more S3 methods, imports only for specific functions
  • dmi (new): Auxiliary function for BBPMM. Creates Data monotonicity index for missing values in order to regulate the numbers of iterations (if BBPMM argument nIter is set to "autolin").
  • impdiagnosticconversion (new): Conversion from BaBooN's BBPMM to coda's mcmc or mcmc.list object, or to mice's mids object.

Reference manual

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install.packages("BaBooN")

0.2-0 by Florian Meinfelder, 4 years ago


http://www.r-project.org


Browse source code at https://github.com/cran/BaBooN


Authors: Florian Meinfelder [aut, cre] , Thorsten Schnapp [aut]


Documentation:   PDF Manual  


Task views: Bayesian Inference, Missing Data


GPL (>= 2) license


Imports Hmisc, MASS, nnet, coda

Depends on Rcpp

Suggests mice, lattice, norm

Linking to Rcpp, RcppArmadillo


See at CRAN