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 non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.

Author
Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
Date of publication
2013-12-31 16:17:04
Maintainer
Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
License
GPL (>= 2)
Version
1.4
URLs

View on CRAN

Man pages

missForest
Nonparametric Missing Value Imputation using Random Forest
missForest-package
Nonparametric Missing Value Imputation using Random Forest
mixError
Compute Imputation Error for Mixed-type Data
nrmse
Normalized Root Mean Squared Error
prodNA
Introduce Missing Values Completely at Random
varClass
Extract Variable Types from a Dataframe

Files in this package

missForest
missForest/inst
missForest/inst/CITATION
missForest/NAMESPACE
missForest/R
missForest/R/nrmse.R
missForest/R/prodNA.R
missForest/R/missForest.R
missForest/R/varClass.R
missForest/R/mixError.R
missForest/README.md
missForest/MD5
missForest/DESCRIPTION
missForest/man
missForest/man/mixError.Rd
missForest/man/missForest-package.Rd
missForest/man/prodNA.Rd
missForest/man/varClass.Rd
missForest/man/nrmse.Rd
missForest/man/missForest.Rd