Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction (250 Patients)



A few unmeasured factors, otherwise called latent factors, are identified to explain a much larger number of measured factors, e.g., highly expressed chromosome-clustered genes. Unlike factor analysis, partial least squares (PLS) identifies not only exposure (x-value), but also outcome (y-value) variables. This chapter is to assess, whether factor analysis/PLS is better than traditional analysis for regression data with multiple exposure and outcome variables.

Supplementary material

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department Medicine Albert Schweitzer HospitalDordrechtThe Netherlands
  2. 2.Academic Medical CenterDepartment Biostatistics and EpidemiologyAmsterdamThe Netherlands

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