Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 154, Issue -, Pages 93-100Publisher
ELSEVIER
DOI: 10.1016/j.chemolab.2016.03.019
Keywords
Missing data; Imputation; PCA model building
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Funding
- Spanish Ministry of Science and Innovation and FEDER funds from the European Union [DPI2011-28112-C04-02, DPI2014-55276-C5-1 R]
- Spanish Ministry of Economy and Competitiveness [ECO2013-43353-R]
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Here we introduce a graphical user-friendly interface to deal with missing values called Missing Data Imputation (MDI) Toolbox. This MATLAB toolbox allows imputing missing values, following missing completely at random patterns, exploiting the relationships among variables. In this way, principal component analysis (PCA) models are fitted iteratively to impute the missing data until convergence. Different methods, using PCA internally, are included in the toolbox: trimmed scores regression (TSR), known data regression (KDR), KDR with principal component regression (KDR-PCR), KDR with partial least squares regression (KDR-PLS), projection to the model plane (PMP), iterative algorithm (IA), modified nonlinear iterative partial least squares regression algorithm (NIPALS) and data augmentation (DA). MDI Toolbox presents a general procedure to impute missing data, thus can be used to infer PCA models with missing data, to estimate the covariance structure of incomplete data matrices, or to impute the missing values as a preprocessing step of other methodologies. (C) 2016 Elsevier B.V. All rights reserved.
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