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

  • Ton J. Cleophas
  • Aeilko H. Zwinderman
Chapter
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Abstract

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

333106_2_En_22_MOESM1_ESM.sav (13 kb)
optscalingfactorplscanonical (SAV 12 kb)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ton J. Cleophas
    • 1
  • Aeilko H. Zwinderman
    • 2
  1. 1.Department Medicine Albert Schweitzer HospitalDordrechtThe Netherlands
  2. 2.Academic Medical CenterDepartment Biostatistics and EpidemiologyAmsterdamThe Netherlands

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