Measure retained information from ranked components accurately. Compare cutoffs, targets, and scree behavior instantly now. Choose optimal components using clear cumulative variance evidence wisely.
Enter ranked eigenvalues, choose a target variance threshold, and submit the form. Your result will appear here above the calculator.
This example shows how retained variance grows as more principal components are included.
| Component | Eigenvalue | Explained Variance (%) | Cumulative Variance (%) |
|---|---|---|---|
| PC1 | 4.80 | 48.00 | 48.00 |
| PC2 | 2.60 | 26.00 | 74.00 |
| PC3 | 1.40 | 14.00 | 88.00 |
| PC4 | 0.70 | 7.00 | 95.00 |
| PC5 | 0.30 | 3.00 | 98.00 |
| PC6 | 0.20 | 2.00 | 100.00 |
Explained Variance for Component i
Explained Variance (%) = (Eigenvaluei / Sum of All Eigenvalues) × 100
Cumulative Variance up to Component k
Cumulative Variance (%) = Sum of explained variance percentages from component 1 through component k
This method is widely used in principal component analysis to identify how many components preserve a desired share of total information.
It shows the running percentage of total variance captured as you include more ordered components. It helps decide how many components preserve enough information for analysis or modeling.
Each eigenvalue represents the amount of variance captured by one component. Larger eigenvalues mean stronger components and greater influence on total retained variance.
Common thresholds are 80%, 90%, and 95%. The best choice depends on your tolerance for information loss, model complexity, and downstream task requirements.
Yes. Components should normally be assessed from highest to lowest eigenvalue. This calculator automatically sorts values in descending order before computing cumulative results.
It counts components with eigenvalues at least equal to one. In many PCA workflows, those components are considered meaningful because they explain at least as much variance as one standardized variable.
The elbow point approximates where explained variance gains start slowing sharply. It is a helpful screening signal, not a strict rule, when selecting components.
Yes, the variance logic is similar, but interpretation depends on the extraction method and rotation choices. Confirm your workflow before making final decisions.
Zero eigenvalues are allowed. They simply add no explained variance. However, the combined total of all eigenvalues must still be greater than zero.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.