mixOmics provides robust support for missing values using the NIPALS (Non-linear Iterative Partial Least Squares) algorithm, which is integrated into (s)PLS, (s)PLS-DA, and (s)PCA for dimension reduction. The nipals() function allows direct handling of missing data, while impute.nipals() enables missing value imputation by reconstructing the dataset using principal components. This ensures reliable analysis even when data contains NAs. Use this page to learn how to implement NIPALS-based missing value handling with practical examples in mixOmics.
Data used on this page:liver.toxicity
Key functions used on this page:nipals()
impute.nipals()