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The performance of these combined models is evaluated under predefined trajectory for lanekeeping maneuver at constant speed.
The differential equations are solved for three different profiles and the result of the combined models is compared to experimental data.
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While combined models are complex, they can be programmed using modern statistical software.
Neither the individual nor the combined models were substantially improved by using additional predictors.
Combined models were obtained by multiplying a linear model for column type, C8 or C18, by quadratic or special cubic mixture models.
In addition, combined models were run: first, all habitats combined, with preference groups separated, producing a single mean for each preference group across all habitats; and second, preference groups combined but habitats separated.
There was no evidence that any of the combined models were not well calibrated.
In addition, two PM/IM combined models were also assessed for any association with BCSS and OS.
Results of analyses for individual traits and combined models are provided in Additional file 1: Tables S5 and S6.
These studies showed that the predictabilities of combined models were generally better than those of models composed only of diagnosis-based scores [ 29, 40, 41].
Similar studies in patients with MCI are underway to determine whether these combined models are capable of identifying distinct subgroups of MCI patients.
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CEO of Professional Science Editing for Scientists @ prosciediting.com