Missing Data Imputation Toolbox for MATLAB

• Published in: Chemometrics and intelligent laboratory systems 2016-05, Vol.154, p.93-100
• Main Authors: Folch-Fortuny, Abel, Arteaga, Francisco, Ferrer, Alberto
• Format: Article
• Language: English
• Subjects: Imputation; Missing data; PCA model building
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2016.03.019

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. •A graphical user-friendly MATLAB interface is presented here: the Missing Data Imputation (MDI) Toolbox.•MDI Toolbox allows imputing incomplete datasets, following missing completely at random pattern.•Different state-of-the-art methods are included in the toolbox, such as trimmed scores regression and data augmentation.•MDI toolbox is freely available at http://mseg.webs.upv.es under a GNU license.