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Related Experiment Videos

Multicollinearity in canonical correlation analysis in maize.

B M Alves1, A Cargnelutti Filho2, C Burin1

  • 1Departamento de Fitotecnia, Universidade Federal de Santa Maria, Santa Maria, RS, Brasil.

Genetics and Molecular Research : GMR
|April 1, 2017
PubMed
Summary

Multicollinearity in canonical correlation analysis can overestimate results. Eliminating variables effectively addresses this issue in maize (Zea mays L.) crop studies.

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Area of Science:

  • Agricultural Science
  • Biometrics
  • Genetics

Background:

  • Multicollinearity poses challenges in statistical analyses, potentially inflating canonical coefficients.
  • Canonical correlation analysis (CCA) is used to explore relationships between variable sets.

Purpose of the Study:

  • To assess multicollinearity effects in CCA for maize (Zea mays L.) crop data.
  • To compare CCA with and without variable elimination for managing multicollinearity.

Main Methods:

  • Evaluated 76 maize genotypes across three experiments using a randomized block design.
  • Measured 29 variables: 11 agronomic, 12 protein-nutritional, and 6 energetic-nutritional.
  • Performed multicollinearity diagnosis (variance inflation factor, condition number) and CCA with/without variable elimination.

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Main Results:

  • CCA without variable elimination overestimated canonical coefficient variability when multicollinearity was present.
  • Variable elimination proved effective in mitigating multicollinearity's impact on CCA.

Conclusions:

  • Multicollinearity significantly affects CCA results in maize breeding.
  • Variable elimination is a recommended strategy to ensure reliable CCA outcomes in the presence of multicollinearity.