Genotypic descriptors as empirical functions of the rate of clonality
To assess the relation between c and the genotypic descriptors, we explored the mean results of simulations as a function of c . Depending on the shape of the curves obtained with simulated data, we tested the fit with basic functions (for example, simple sigmoids and exponentially decreasing distributions) as well as with sigmoid and parabolic curves. To assess the accuracy of our empirically inferred formula to describe the relationships, we computed the mean absolute error (MAE) and the root-mean-square deviation (RMSD) between pseudo-observed simulated values and fitted formulae. These two deviation measures aggregate the magnitudes of the errors of predictions into a single measure of predictive accuracy. This measure represents the mean deviation of predicted values with respect to the observed values and has the advantage of sharing the same units as the model variable under evaluation. Lower deviation measures indicate higher accuracy of an analytical formula in the prediction of data. These measures must be interpreted at the same scale as the mean value of the studied parameter (PiƱeiro, Perelman, Guerschman, & Paruelo, 2008).
\begin{equation} MAE=\frac{1}{n}.\sum_{1}^{n}\left|y_{s}-y_{f}\right|\nonumber \\ \end{equation}\begin{equation} RMSD=\sqrt{\frac{1}{n}.\sum_{1}^{n}\left(y_{s}-y_{f}\right)^{2}}\nonumber \\ \end{equation}
where n is the number of pseudo-observed simulations per scenario, ys is the simulated value of the genotypic descriptor under consideration, and yfis the calculated value of the genotypic descriptor using the fitted formula.