The function generates a number of missing values (NA) completely at random on a data matrix. Totally missing rows (i.e., rows with all NA) are avoided.
Two methods based on the Forward Imputation (ForImp) approach are implemented for the imputation of quantitative missing data. One method alternates the Nearest Neighbour Imputation (NNI) method and Principal Component Analysis (function ForImp.PCA), the other uses NNI with the Mahalanobis distance (function ForImp.Mahala). ForImp is a sequential distance-based approach that performs imputation of missing data in a forward, step-by-step process involving subsets of units according to their “completeness rate”. During the iterative process, the complete part of data is updated thus becoming larger and larger. No initialization of missing entries is required. ForImp is inherent in the nonparametric and exploratory-descriptive framework since it does not require a priori distribution assumptions on data. Two supplementary functions (missing.gen and missing.gen0) are also provided to generate Missing Completely At Random (MCAR) values on a data matrix.
This function imputes quantitative missing data by alternating Nearest Neighbour Imputation (NNI) method and Principal Component Analysis (PCA) in a forward and sequential step-by-step process that starts from the complete part of data.
This function imputes quantitative missing data by using Nearest Neighbour Imputation (NNI) with the Mahalanobis distance in a forward and sequential step-by-step process that starts from the complete part of data.
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