missForest: Nonparametric Missing Value Imputation using Random Forest
The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and nonlinear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation.
| Version: |
1.3 |
| Depends: |
randomForest |
| Published: |
2012-06-26 |
| Author: |
Daniel J. Stekhoven, |
| Maintainer: |
Daniel J. Stekhoven, <stekhoven at stat.math.ethz.ch> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
missForest results |
Downloads: