Loading Bar Chart VIP Exclusive
📊 What is this?
The Loading Plot shows the distribution of variables on components using scatter points, which is good for seeing structural relationships.
The Loading Bar Chart uses a more direct expression: it lays out the loading values of all variables on one specified component as bars.
💡 One-line distinction: the loading scatter plot answers "what is the relationship between these variables"; the Loading Bar Chart answers "on this one component, which is heavy and which is light".
🧐 How to read?
Bar direction and length
| Feature | Meaning |
|---|---|
| Bar pointing up (positive value) | The variable is positively correlated with the score on this component |
| Bar pointing down (negative value) | The variable is negatively correlated with the score on this component |
| The longer the bar | The greater the variable's contribution to this component |
| Bar close to 0 | The variable hardly plays any role on this component |
Use it in contrast with the score plot
This is the key idea for understanding the loading bar chart:
The score plot looks at samples, the loading plot looks at variables. A sample with a very high score on PC1 means that it also has extreme values on those variables whose "absolute loading on PC1 is large".
🛠️ How to use?
Configuration items
| Configuration item | Description |
|---|---|
| Number of components | Choose the principal component / latent variable to view (from 1 to the optimal number of components) |
Click Draw to generate. You can switch between components and view them one by one.
Typical uses
| Purpose | Method |
|---|---|
| Interpret the meaning of a principal component | Find the variables with the largest loadings on that component → together they define the business meaning of the component |
| Variable screening | Look across multiple components; if a variable is close to 0 on all components → consider removing it |
| Find collinear variables | Two variables with highly similar loadings on all components → redundant information |
| Validate business intuition | Check whether the dominant variables match process common sense; if not, go back and check the data |
🎯 Division of labor with other loading-related charts
The platform provides three loading-related views, each with a different purpose:
| Chart | Presentation | Best for |
|---|---|---|
| Loading Plot | Scatter (p1 vs p2) | Seeing the structural relationships between variables |
| X+Y Loading Plot | Scatter (X vs Y) | Seeing the direction of association between X and Y |
| Loading Bar Chart | Bars (single component) | Seeing which variable matters most on a given component |
💡 Workflow suggestion: first use the scatter loading plot to see the overall structure and lock onto the directions of interest; then use the Loading Bar Chart to dig into each component, quantifying "exactly which few variables are dominating".
⚠️ Notes
- Only supervised models are supported (PLS / PLS-DA / OPLS / OPLS-DA)
- The comparability of loading values depends on the preprocessing method: whether the data is standardized and whether the scaling method is consistent both affect the magnitude of the loadings; be careful when comparing across models
- The special nature of OPLS models: their loadings reflect the predictive component after orthogonal correction, and are not directly comparable with PLS loadings
- The choice of component count affects the conclusions——it is recommended to view within the optimal component range; components too far back are mostly noise