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Fig. 15 Model selection accuracy.

We focus on model selection accuracy without reporting parameter estimates.

Here we explore the model selection accuracy for varying parameter values including the weight, stimulus dissimilarity, stimulus strength and number of spike trains.

Model selection accuracy was measured by Z-scores calculated from TMscore.

There is very little difference (<10%) between the model selection accuracy when model selection was carried out using a full ML tree search using each available model and the models selected by the quicker NJ-JTT method (see Table 2).

We conducted a simulation study of one of these approaches, implemented in the Python module δaδi, to compare parameter estimation and model selection accuracy given different sample sizes under one- and two-population models.

Model selection and accuracy estimation were performed using a 2-level cross-validation strategy available from Partek.

When three generations of selection were made without calibration of the GS model, the selection accuracy kept decreasing and this occurred at a higher pace for RRGS_PAR than for RRGS_HYB (Fig.  2).

Our purpose in doing this is to understand how model selection affects the accuracy of the decision support system.

To determine the effect of the choice of model selection on phylogenetic accuracy we designed a model misspecification analysis test.

To test for the effect of ad hoc model selection on tree accuracy, we performed an analysis on both simulated and real data.