PCA eigenvalues
| Component | Eigenvalue | Difference | Proportion | Cumulative |
|---|---|---|---|---|
| COMP1 | 1.33838 | 0.305182 | 0.2231 | 0.2231 |
| COMP2 | 1.0332 | 0.0285048 | 0.1722 | 0.3953 |
| COMP3 | 1.00469 | 0.0437661 | 0.1674 | 0.5627 |
| COMP4 | 0.960927 | 0.0455382 | 0.1602 | 0.7229 |
| COMP5 | 0.915389 | 0.167977 | 0.1526 | 0.8754 |
| COMP6 | 0.747412 | . | 0.1246 | 1 |
| Component | Eigenvalue | Difference | Proportion | Cumulative |
|---|---|---|---|---|
| COMP1 | 1.33838 | 0.305182 | 0.2231 | 0.2231 |
| COMP2 | 1.0332 | 0.0285048 | 0.1722 | 0.3953 |
| COMP3 | 1.00469 | 0.0437661 | 0.1674 | 0.5627 |
| COMP4 | 0.960927 | 0.0455382 | 0.1602 | 0.7229 |
| COMP5 | 0.915389 | 0.167977 | 0.1526 | 0.8754 |
| COMP6 | 0.747412 | . | 0.1246 | 1 |
Notes:
This table presents the eigenvalues obtained from the PCA. It showcases six components (COMP1 to COMP6), their respective eigenvalues, the difference in eigenvalues between successive components, the proportion of the total variance explained by each component and the cumulative proportion of explained variance up to each component. The table provides an overview of how much each component contributes to the total variability of the data. The cumulative proportion column gives a quick way to see how much total variance is accounted for as we consider more components. By the end of COMP6, all the variance in the data (100%) has been accounted for
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