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.

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Description file content

Package
missForest
Type
Package
Title
Nonparametric Missing Value Imputation using Random Forest
Version
1.4
Date
2013-12-31
Author
Daniel J. Stekhoven
Maintainer
Daniel J. Stekhoven
Depends
randomForest,foreach,itertools
Description
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.
License
GPL (>= 2)
URL
Packaged
2013-12-31 14:28:06 UTC; DSQuantik
NeedsCompilation
no
Repository
CRAN
Date/Publication
2013-12-31 16:17:04

install.packages('missForest')

1.4

05 years ago

http://www.r-project.org

Daniel J. Stekhoven

GPL (>= 2)

Depends on

randomForest,foreach,itertools

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