GenForImp: The Forward Imputation: A Sequential Distance-Based Approach for Imputing Missing Data

Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').

Author
Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
Date of publication
2015-02-27 19:31:42
Maintainer
Alessandro Barbiero <alessandro.barbiero@unimi.it>
License
GPL-3
Version
1.0

View on CRAN

Man pages

ForImp.Mahala
Imputation of missing data by using Nearest Neighbour...
ForImp.PCA
Imputation of missing data by alternating Nearest Neighbour...
GenForImp-package
The Forward Imputation: A Sequential Distance-Based Approach...
missing.gen
Generating random missing values on a data matrix
missing.gen0
Generating random missing values on a data matrix

Files in this package

GenForImp
GenForImp/NAMESPACE
GenForImp/R
GenForImp/R/ForImp.Mahala.R
GenForImp/R/missing.gen.R
GenForImp/R/ForImp.PCA.R
GenForImp/R/missing.gen0.R
GenForImp/MD5
GenForImp/DESCRIPTION
GenForImp/man
GenForImp/man/missing.gen0.Rd
GenForImp/man/missing.gen.Rd
GenForImp/man/GenForImp-package.Rd
GenForImp/man/ForImp.PCA.Rd
GenForImp/man/ForImp.Mahala.Rd