Performs the Principal Component Partial R-squared method (PC-PR2) on omics data and corresponding metadata. PC-PR2 is a statistical method, developed by Fages et al. (2014), that investigates sources of variability in metabolomics or other omics data. In brief, it combines features of principal component analysis and multivariable linear regression. The input is X, a complete matrix of omics data and Z, a corresponding data frame of subject metadata. The output is a set of Rpartial2 values, or the proportion of variation in the omics data attributed to each Z-variable.
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