Two different datasets by means of maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) performance are analyzed.
We give the median error, the mean error, and the root mean squared error for the X and Y dimensions as well as the Euclidean prediction errors, the 90'th percentile error, (the max error after dropping 10'th percentile of spots with the worst error,) and the root mean square error of the 90'th percentile of spots with the least error.
The mean estimated and measured mass are given as well as the percent root mean square error (%RMSE), defined as 100 × (|estimated concentration – measured concentration|/measured concentration).
Finally, the root mean-square error of approximation (RMSEA = 0.08), as well as the standardised root mean square residual (SRMR = 0.02), confirmed a good fit of the SEM.
In the case of GFRFDG, total error was taken as square root of the squared sum of all contributing errors, which was the standard error from just described reproducibility checks as well as the standard error from the linear fit of the Patlak plot.
As well as AIC, the root mean squared error (RMSE) of the model predictions for the blind test data is also given.
If you vary the value of alpha by hand in this Excel model, you can observe the effect on the time series and autocorrelation plots of the errors, as well as on the root-mean-squared error, which will be illustrated below.
The model's performance was assessed by the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) as well as the coefficient of determination (R2) between measured and predicted data as recommended by many researchers.
Configural invariance was assessed by global evaluation of model accuracy using chi-test as well as the model fit indices Comparative fit index (CFI) and Root mean square error of approximation (RMSEA).
Goodness-of-fit is evaluated by using those fit indicators as well as the comparative fit index (CFI), nonnormed fit index (NNFI/TLI), and the root mean square error of approximation (RMSEA) [ 46, 47].
The obtained simulation results were then analysed using Kolmogrov-Smirnov method as well as root mean-square error (RMSE) and coefficient of determination (R2).
