The R script used for all the analysis in this case study is available here.
For background information on the (s)IPCA method, refer to the IPCA Methods Page.
More information on Plots
For a more in depth explanation of how to use and interpret the plots seen, refer to the following pages:
– `plotVar()` – Correlation Circle Plot
Additional Notes
Kurtosis
The kurtosis measure is used to order the Independent Principal Components (IPCs). A value of zero indicates the variable has a Gaussian distribution. Increasing the magnitude indicates a greater deviation from a Gaussian distribution. Greater deviations are desirable due to the non-Gaussian nature of IPCA.
It has been shown that the kurtosis value is a good post-hoc indicator of the number of components to choose, as a sudden drop in the values corresponds to irrelevant components.
Davies Bouldin Index
This value is the ratio of the intracluster (within-cluster) scatter and intercluster (between-cluster) scatter. Low values indicate good clustering, such that points within one cluster are tight and differing clusters are well-defined from one another.