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1 Parkinson’s Disease example

Employ Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Independent component analysis(ICA), Factor analysis (FA) to reduce the dimensionality of the PD data. Interpret the results.

2 Allometric Relations in Plants example

2.1 Load data

Load Allometric Relations in Plants data and select proper covariates.

2.2 Dimension reduction

  • Apply Principal Component Analysis protocol.
    • Generate a data summary
    • Apply prcomp
    • Report the rotations (scores)
    • Display screen plot
    • Select the number of PCs and employ a bootstrap test
    • Apply factoextra to draw biplot and grouped by Province/Sites
  • Perform SVD and ICA and compare the results of PCA.
    • Use these three variables L, M, D to perform ICA and show pair plots before ICA and after ICA. [Hint: scatter3dplot() may be helpful, which you saw in chapter 4.]
  • Perform factor analysis
    • Use require(nFactors) to determine the number of the factors and show a scree plot as stated in notes
    • Use factanal() to apply FA and compare the rotation varimax and promax
    • Report the loadings and consider an appropriate visualization method.
  • Interpret the findings in the context of the case-study.

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