SOCR ≫ DSPA ≫ Topics ≫

1 Parkinson’s Disease example

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

2 Allometric Relations in Plants example

2.1 Load data

Load Allometric Relations in Plants data and perform proper type conversion, e.g., convert “Province” and “Born”.

2.2 Apply Principal Component Analysis:

  • Generate a data summary
  • Apply factoextra and compare it to the results of prcomp
  • Report the rotations (scores)
  • Show the scree plot
  • Select the number of PCs and employ a bootstrap test
  • Perform SVD and ICA and compare the results of PCA.
    • Use these three variables “L”,“M”,“D” to perform ICA and show pair-plots of before-ICA and after-ICA scatter in the data. 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.

SOCR Resource Visitor number Dinov Email