Goodness of fit by the one- and two-compartment models was compared with F statistics [23].
Goodness-of-fit by the compartment models was compared with F statistics [16].
The performance of the proposed models was compared with the MODIS 1 km GPP product.
The performance of these models was compared both visually and analytically.
The performance of the developed models was compared among themselves for 4 randomly generated test cases.
The performance of the developed artificial neural network models was compared with the RSM mathematical models of average cutting speed.
Model fit for all models was compared using the DIC fit statistic from WinBUGS (Spiegelhalter et al. [2002]).
The predictive capacity of the two models was compared based on R2, R2adj, RMS, MAE and AAD.
In this sense, the external predictive ability of the developed models was compared with respect to other methodologies by means of the multiple comparison tests.
The performance of all these models was compared using the coefficient of determination (R 2), Akaike information criterion (AIC), and root mean square of error (RMSE) (Sileshi 2014).
Using Area under the receiver operating characteristic curve (AuROC) analysis, the performance of the models was compared to SOFA and APACHE II.
