How to Analyze a Loading Plot in PLS Analysis?
PLS (Partial Least Squares) analysis is a statistical method primarily used for model construction and analyzing relationships between dependent variables. In PLS analysis, the loading plot helps visualize the relationship between latent variables and observed variables. The analysis process generally includes the following steps:
1. Observe the Coordinate System
The loading plot is typically displayed in a two-dimensional coordinate system, where the x-axis represents the first principal component (Component 1), and the y-axis represents the second principal component (Component 2).
2. Identify Observed Variables
Observed variables are represented as points or labels on the coordinate system. Each observed variable on the loading plot is associated with latent variables.
3. Determine Latent Variables
Latent variables are represented as vectors originating from the coordinate system's origin and pointing towards the direction of observed variables. The angles between latent variable vectors indicate their correlation—smaller angles suggest a higher correlation, while larger angles indicate a lower correlation.
4. Analyze Relationships Among Observed Variables
The positions of observed variables on the loading plot help interpret their relationships. Variables positioned closely together exhibit a strong correlation, whereas those positioned farther apart have weaker correlations.
5. Calculate Loading Values
Loading values measure the relationship between observed and latent variables. They typically range from -1 to 1, where values close to 1 indicate a strong positive correlation, values close to -1 indicate a strong negative correlation, and values near 0 indicate little to no correlation. Loading values can be obtained through analytical software for further interpretation.
6. Evaluate the Model’s Interpretability
Observing the loading plot helps assess the explanatory power of the PLS model. If observed variables cluster together, the model effectively explains their relationships. If the variables are widely dispersed, the model's interpretability may be weak.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
Related Services
How to order?
