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').
GenForImp: The Forward Imputation : A Sequential Distance-Based Approach for Imputing Missing Data [Software] / N. Solaro, A. Barbiero, G. Manzi, P.A. Ferrari. - [s.l] : R foundation, 2015 Feb 27.
GenForImp: The Forward Imputation : A Sequential Distance-Based Approach for Imputing Missing Data
A. Barbiero;G. Manzi;P.A. Ferrari
2015
Abstract
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').
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