Model validation and error estimation in multi-block partial least squares regression

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• 作者：Sahar Hassani ; Harald Martens ; El Mostafa Qannari ; Mohamed Hanafi ; Achim Kohler
• 关键词：Multi-block methods ; Multi-block Partial Least Squares Regression (MBPLSR) ; Validation tools ; Cross validation ; Number of components
• 刊名：Chemometrics and Intelligent Laboratory Systems
• 出版年：2012
• 出版时间：1 August, 2012
• 年：2012
• 卷：117
• 期：Complete
• 页码：42-53
• 全文大小：868 K

文摘

While validation of Partial Least Squares Regression (PLSR) models has been discussed extensively, validation tools that are tailored to Multi-block Partial Least Squares Regression (MBPLSR) have not been discussed in literature yet. This paper introduces validation tools for estimating predictive ability and model stability in MBPLSR models on block level and on global level. Predictive ability on the block level and global level are estimated by calculating the predictive power of block and global parameters. Model stability is estimated by checking the stability of block model parameters and global parameters. By comparing error plots for model stability and predictive ability the user can decide on the number of component to be used. The number of components to be chosen depends on the data set and the purpose of the investigation.