Apply your Method

Once you’ve selected a mixOmics method, follow this general workflow:

1. Pre-process your data – Handle missing values or repeated measurements (see below).
2. Build an initial model – Apply the method to your data.
3. Tune model parameters – Optimize settings for better performance.
4. Train the final model – Use the best parameters for an optimized model.
5. Assess performance – Evaluate how well your model works.
6. Predict new samples – If applicable, use the model to predict outcomes.

Not all steps are necessary. For example, you can manually select parameters instead of tuning, or if using an unsupervised method, prediction (Step 6) may not apply.

👉 For detailed examples of how to apply your specific method, refer to the case studies and the vignette.

Pre-processing

Missing values

Most mixOmics methods support missing values using the NIPALS algorithm. Check the method’s documentation for any exceptions.
More details: Missing Values

Repeated Measures & Cross-over Designs

For repeated measurements, mixOmics includes multilevel analysis, which accounts for multiple samples per individual. Use the multilevel parameter in most functions or the withinVariation() function.
More details: Multilevel