Sentence examples for each model was calculated from inspiring English sources

The root mean square error of prediction (RMSEP) for each model was calculated using {text{RMSEP}} = sqrt {frac{{mathop sum nolimits_{i = 1}^{n} (y_{i - } hat{y}_{i} )^{2} }}{n}},where y i is the measured and (hat{y}_{i}) is the predicted value.

The log-likelihood of each model was calculated as the sum of the log-likelihoods of their component functions.

This process was repeated 1000 times and the deviance of each model was calculated to determine whether the deviance of the observed model exceeded that of the simulated data.

Second, the cumulative Spearman rank correlation between sensitivity results generated using the ODE15s, BDF3 and FD methods for each model was calculated as a function of the number of parameter sets sampled.

Furthermore, the acceptance rate of each model was calculated.

The likelihood for each model was calculated with BayesDiscrete in the BayesTraits package [ 66], using the branch length estimates and character coding as above.

RMSE for each model is calculated and compared.

At each moment, the output error of each model is calculated and used to achieve their weighting coefficients.

Von Mises stress, the maximum and minimum principal stress on each model were calculated using finite element structural analysis software.

The values of strain from each model are calculated by considering Youngs modulus Ehkl to be 127GPa.

With the help of the results in Table 1, the RSI value of each model is calculated in Table 2.