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Subsequently, the search for improved model performances was widened by further applying the model to obtain forecasts for set partition index values of ( vhat{sigma } pm i ) ( { i = 1,2, ldots,widehat{sigma } - 1} ).

Comparisons of model performances were based on visual assessments of stand-level trajectories.

Model performances are summarized in Table 1.

Comparing PLS with MCR-ALS, similar model performances were reached.

In contrast, D21_Ca model performances were poor in all intervals.

The best model performances are emboldened and a * flags models performances that are significantly different from HME3M (α = 0.05).

Model performances were tested using cross-validated subsets (60% training, 40% testing) and ROC analysis.

Model' performances were evaluated on the basis of AUC values across five-fold ten-runs cross-validation.

Model derivation and assessment of model performance was performed by using R software version 2.6.0.

Further, the model performance was evaluated using various performance criteria.

Model performance was assessed using Monte Carlo techniques.