Multivariate Exploratory Data Analysis (MEDA) Toolbox for Matlab

The Multivariate Exploratory Data Analysis (MEDA) Toolbox in Matlab is a set of multivariate analysis tools for the exploration of data sets. In the MEDA Toolbox, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading and resi...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems 2015-04, Vol.143, p.49-57
Main Authors: Camacho, José, Pérez-Villegas, Alejandro, Rodríguez-Gómez, Rafael A., Jiménez-Mañas, Elena
Format: Article
Language:English
Subjects:
Big Data
Exploratory Data Analysis
Matlab
Partial Least Squares
Principal Component Analysis
Software
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Summary:The Multivariate Exploratory Data Analysis (MEDA) Toolbox in Matlab is a set of multivariate analysis tools for the exploration of data sets. In the MEDA Toolbox, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading and residual plots, are combined with new methods like MEDA, oMEDA and SVI plots. The latter are aimed at solving some of the limitations found in the former to adequately extract conclusions from a data set. Also, other useful tools such as cross-validation algorithms, Multivariate Statistical Process Control (MSPC) charts and data simulation/approximation algorithms (ADICOV) are included in the toolbox. Finally, most of the exploratory tools are extended for their use with very large data sets (Big Data), with unlimited number of observations.
ISSN:0169-7439
1873-3239
DOI:10.1016/j.chemolab.2015.02.016