Scheme of the F-test selection process. Note that depending on the data different combinations have to be tested [in this case (Table IV, P5) the combination was necessary].
Time activity curves for blood together with the best fit curves (Tables III and IV) for both model selection criteria.
Regression diagnostics of the best model (for and no weighting) selected using AICc.
Regression diagnostics of the best model (for and no weighting) selected using the F-test.
Akaike weights for weighting and no weighting. (Note: The Akaike weights indicate the probability that the model is the best among the whole set of considered models. The highest weight, i.e., the best model, is presented in bold.)
values for weighting with and no weighting. [Note: The F-test compares the models and rejects the simpler model if the value is below the significant level (here ). It is not sufficient to compare models most similar in complexity (Fig. 2). In two cases it was not necessary (NN) to state a value, because the increase in model complexity (more parameters) did not improve the fitting.]
Frequency of selected models with AICc applying the Jackknife method. (Note: Nine times one data point was deleted and the AICc was determined with the remaining eight points. The best function with the original data is in bold. The AICc value for the “” function could not be calculated, because of too few data points.)
Frequency of selected models with F-test applying the Jackknife method. (Note: Nine times one data point was deleted and the F-test was determined with the remaining 8 points. The best function with the original data is in bold.)
Mean values of the pharmacokinetic parameters (11 patients). (Note: “Shared” means that these parameters were calculated for all patients together. “All nonshared” correspondingly means that the four parameters are calculated for each patient separately. All other combinations of shared and nonshared parameters have Akaike weights smaller than . The given standard deviations of the parameters represent the variations between patients and not the uncertainties due to the fitting procedure. The standard deviation of the Akaike weights reflects the different results deduced by the Jackknife crossvalidation, which is based on leaving one patient out 11 times.)
Article metrics loading...
Full text loading...