Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots

Displays of latent variable regression models in variable and object space are provided to reveal model parameters useful for interpretation and to reveal the most influential x ‐variables with respect to the predicted response. Although the target projected (TP) component obtained from a standard partial least square, or equivalently, the predictive component from orthogonal partial least squares (OPLS) or partial least squares + similarity transform (PLS + ST) is maximally co‐varying with the response, the corresponding loadings are not necessarily the best choice for model interpretation and disclosure of the most important variables with respect to explaining the response. Selectivity ratio plot represents a bridge from co‐variance‐based TP loadings to correlation‐like localized information suitable for interpretation.