RfEmpImp

Description

An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.

Downloads

Trend: +27.4% (30d vs prior 30d)

CRAN Check Status

Maintainer: ‘Shangzhi Hong <shangzhi-hong@hotmail.com>’

The Description field contains
  <arXiv:2004.14823>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Shangzhi Hong <shangzhi-hong@hotmail.com>’

The Description field contains
  <arXiv:2004.14823>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Check History

Maintainer: ‘Shangzhi Hong <shangzhi-hong@hotmail.com>’

The Description field contains
  <arXiv:2004.14823>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Shangzhi Hong <shangzhi-hong@hotmail.com>’

The Description field contains
  <arXiv:2004.14823>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Dependency Network

Version History