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I use Simbiology for population PK-PD model development. During the model fitting of data, I understand that the model diagnostics play a major decisive role in selecting the suitable model. Hence would like to make it clear regarding the interpretation of the model diagnostics.

If for example, I have two models. First model: DFE= 411, LogLikelihood = - 807.6 (minus 807.6), AIC = 1633.2 , BIC = 1647.2 and RMSE = 1.92 Second model: DFE= 410, LogLikelihood = - 888.8 (minus 888.8), AIC = 1797.6 , BIC = 1813.2 and RMSE = 0.34 Which among the model is better and why? What are the individual interpretation of DFE, LogLikelihood, AIC, BIC and RMSE?

In PK-PD research paper generally, they take Objective Function value as decisive model diagnostics. What is the Objective Function Value in Simbiology? I did some literature search and found that Objective Function Value is -2 times LogLikelihood value? So should I multiply the LogLikelihood value given in Simbiology by - 2 to obtain Objective Function Value? Moreover, if the LogLikelihood value is multiplied with -2 then the entire interpretation will be changing (as minus will reverse the direction). So please guide in this regard and give your valuable inputs.