Hello everyone.
I have a question about VIP score. I am currently working on microbiota data, and I am using the mixOmics package on R to get VIP scores so that I can filter the most "important" taxa.
The thing is, I get one VIP score for each component of my PLS-DA, and I am confused about which score I should use in the end.
My first component is the one explaining the most variation, but I have been told the last component score takes into account the previous ones, so I should use this one.
On other studies, I saw they used the sum from each component to get a final VIP score.
In the end, I am quite confused about which one should be used.
Thanks for your answers.
The VIP (variable importance in the projection) score is a measure of the contribution of each feature (i.e., taxa in your case) to the overall model performance. The choice of which component's VIP score to use can depend on the specific research question and the data being analyzed.
In general, the first component of a PLS-DA (partial least squares discriminant analysis) model typically explains the most variation in the data and thus has the highest VIP scores. However, subsequent components can also contribute to the overall model performance and therefore be important.
One approach is to use the sum of the VIP scores across all components to get a final VIP score. This approach considers the contribution of each feature to the entire model and can give a more comprehensive view of the feature importance.
Another approach is to use the VIP score of the last component, which takes into account the information from previous components. This approach may be useful when you want to focus on the most relevant features for the final component, which may be more biologically meaningful.
Ultimately, the choice of which component's VIP score to use depends on your specific research question and what you hope to achieve with your analysis. It may also be beneficial to try multiple approaches and compare the results to see which gives the most meaningful insights.
- Badges
- Science topic
- Similar topics
- Gels