Partial least squares regression and principal component analysis: similarity and differences between two popular variable reduction approaches

In many statistical applications, composite variables are constructed to reduce the number of variables and improve the performances of statistical analyses of these variables, especially when some of the variables are highly correlated. Principal component analysis (PCA) and factor analysis (FA) ar...

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Bibliographic Details
Published in:General psychiatry 2022-01, Vol.35 (1), p.e100662-e100662
Main Authors: Liu, Chenyu, Zhang, Xinlian, Nguyen, Tanya T, Liu, Jinyuan, Wu, Tsungchin, Lee, Ellen, Tu, Xin M
Format: Article
Language:English
Subjects:
Biostatistical Methods in Psychiatry
biostatistics
Eigenvalues
linear models
Loneliness
Principal components analysis
Regression analysis
Sample variance
statistics as topic
Variables
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Summary:In many statistical applications, composite variables are constructed to reduce the number of variables and improve the performances of statistical analyses of these variables, especially when some of the variables are highly correlated. Principal component analysis (PCA) and factor analysis (FA) are generally used for such purposes. If the variables are used as explanatory or independent variables in linear regression analysis, partial least squares (PLS) regression is a better alternative. Unlike PCA and FA, PLS creates composite variables by also taking into account the response, or dependent variable, so that they have higher correlations with the response than composites from their PCA and FA counterparts. In this report, we provide an introduction to this useful approach and illustrate it with data from a real study.
ISSN:2517-729X
2096-5923
2517-729X
DOI:10.1136/gpsych-2021-100662