The performance of models structure has been evaluated using root mean square error and coefficient of determination.
Model accuracy was evaluated using root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), and index of agreement (d) statistics.
Spreadsheet programs were developed for analyzing pumping test data, and their accuracy was evaluated by root mean square error (RMSE) and correlation coefficient (R).
We evaluated the root mean square errors and significance of biases at the pixel level in order to determine the optimal parameters.
The influence of parameters is evaluated by root mean square errors (RMSEs) between the results of Monte Carlo simulation using heterogeneous parameters and the results of the Fokker Planck equations using average parameters.
The tracking performance is evaluated as root mean square of positioning errors (RMSE) given by: RMSE = 1 N · K ∑ i = 1 N ∑ k = 1 K p ̂ k i − p k i 2, (17).
Docking protocol was evaluated through re-docking process in which the co-crystallized compound was extracted and re-docked into the binding cavity, and the quality of docking protocol was evaluated through root mean square deviation (RMSD ≤2 Å is considered as best).
Using structural-equation modeling, several model-fit indices were evaluated: the Root Mean Square Error of Approximation (RMSEA), Incremental Fit Index (IFI), Comparative Fit Index (CFI), Nonnormed Fit Index (NNFI), Goodness of Fit Index (GFI), and Standardized Root Mean Square Residual (SRMR).
To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals.
Next, we evaluated the root mean square error (RMSE) of growth rate estimates based on a linear regression with different outlier detection methods and under different normalization (cf. Figure 2 and Figure S4). Figure 2A shows that even if no outliers are present in the data, the outlier detection methods DFBETA and COOK improve the estimates.
