Notes:

This table displays the principal components (PCs) or eigenvectors (note the similarity between Table 5 and Table 6: Tables 5 and 6 both stem from the PCA process, and their values are intrinsically linked: PCA overview: PCA is used to transform the original data variables into a set of new orthogonal variables, termed principal components. These components encapsulate the variance in the data, with the aim of reducing dimensionality while retaining as much information as possible. Eigenvectors vs loadings: Table 5 delineates the eigenvectors of each variable, reflecting the direction and magnitude of each variable’s contribution to the principal components. Conversely, Table 6 displays the loadings, signifying the correlation between the original variables and the principal components. Because of the nature of PCA, especially when standardized variables are used, the eigenvectors and loadings often coincide, leading to the observed similarity in values across the two tables. Incorporating unexplained variance: A distinguishing feature of Table 5 is the “Unexplained” column, which sheds light on any variance not captured by the principal components. In this dataset, the unexplained variance for all variables is zero, indicating that the PCA has comprehensively represented the variability of the standardized variables. In essence, the congruence between Tables 5 and 6 is anticipated and aligns with standard PCA outputs. The addition of the “Unexplained” column in Table 5 provides an extra layer of understanding, ensuring that readers grasp the full scope of the data’s dimensionality reduction. In the context of the table,” the value “0.5723” under “COMP1” for the variable “RC_STD” represents the eigenvector coefficient for that specific variable in relation to the first principal component [COMP1]) for each variable obtained from the PCA, along with any unexplained variance. The table depicts the direction and magnitude of each variable's contribution to each component (COMP1 to COMP6). These components are linear combinations of the original variables, and each represents a specific aspect of the total variance present in the original data. The unexplained variance for all variables is zero, indicating that the PCA model fully represents the variability of all standardized variables