Is It Better for Each R²X and R²Y to Exceed 0.5 or for Their Cumulative Values? How to Interpret the Q² Intercept
For the two-dimensional plots of PLS-DA or OPLS-DA, it is generally considered more favorable when the cumulative values of R²X and R²Y across all principal components exceed 0.5, indicating that the model explains more than half of the total variance. Specifically, R²X quantifies the proportion of variance in the predictor variables (X) that is explained by the model, while R²Y quantifies the model’s ability to account for variance in the response variables (Y). A cumulative value above 0.5 suggests that the model possesses satisfactory explanatory and predictive performance.
The Q² intercept serves as a metric to evaluate the model’s predictive reliability, particularly its performance under cross-validation. A Q² intercept close to zero or slightly positive implies robust predictive ability. In contrast, a markedly negative Q² intercept may indicate model overfitting, raising concerns about its generalizability.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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