missForest-package

<p>'missForest' is used to impute missing values particularly in the case
  of mixed-type data. 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. 
  Moreover, it can be run parallel to save computation time.</p>

 nonparametric

 package

 classes 

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.

Package:	missForest
Type:	Package
Version:	1.4
Date:	2013-12-31
License:	GPL (>= 2)
LazyLoad:	yes

missForest-package: Nonparametric Missing Value Imputation using Random Forest

Description

Arguments

Details

References