Displays key data output from the principal component analysis (PCA) for the user to view.
This sub-tab provides a direct view of the core numerical results generated by the Principal Component Analysis, typically displayed in a terminal-like output format. It usually shows snippets of the key data tables for eigenvalues, variables, and individuals.
This section summarizes the variance associated with each principal component (dimension).
Variance: The eigenvalue for the corresponding dimension (principal component). Represents the amount of variance captured.
% of var. (Percent of Variance): The percentage of the total dataset variance explained by that single dimension.
Cumulative % of var. (Cumulative Percent of Variance): The running total of the variance explained by including the current dimension and all preceding ones.
This section shows PCA results for the individual samples (rows), typically displaying the first 10 individuals.
Dist: Often represents the squared distance of the individual from the origin (centroid) in the full principal component space. Can indicate how much an individual deviates overall.
Dim.X (e.g., Dim.1, Dim.2): The coordinate (score) of the individual on that specific principal component (dimension). This determines the sample's position in the PCA plots.
ctr (Contribution): The percentage contribution of that individual to the total variance of that specific principal component. High values indicate influential points for that dimension.
cos2 (Quality of Representation): The quality of representation of the individual on that specific principal component. A value closer to 1 means that dimension captures a large portion of that individual's variance. (Note: This is the cos2 per dimension, while the quality plots often show the sum across selected dimensions).
This section shows PCA results for the original variables (columns), typically displaying the first 10 variables.
Dim.X (e.g., Dim.1, Dim.2): The coordinate (loading) of the variable on that specific principal component (dimension). Indicates the correlation/relationship between the variable and the PC.
ctr (Contribution): The percentage contribution of that variable to the total variance of that specific principal component. High values indicate variables that strongly define that dimension.
cos2 (Quality of Representation): The quality of representation of the variable on that specific principal component. A value closer to 1 means that dimension captures a large portion of that variable's variance.
