Although there was some disparity between the predictive methods used, the more developed methods indicated fatigue lives that appeared reasonable, taking into account the vehicles tested and our knowledge of spinal degeneration.
Drawing on the audit findings, a framework for determining the likely accuracy of ZVI predictions is then developed based on the relationship between the precision of the predictive method and the complexity of the landscape setting.
The predictive methods are mainly to predict SSD depth.
Table 1 presents a comparative analysis between the different predictive methods, according to the outcome and predictive accuracy.
The results indicate that, depending on geometry, differences exceeding 30% can exist between predictive method and experimental results.
Analysis of simulated data suggests that methods based on mixture models provide better predictive ability than methods that assume all the SNP have predictive value [ 15], while analysis of field data tends to demonstrate relatively little difference between alternative methods, and some inconsistencies appear from trait to trait as to which is the most predictive method [ 41, 42].
In order to simulate the decision between available alternative predictive methods and highlight the possible problems and transferability issues to specific contexts, different approaches are considered.
This has not to be intended as an assessment of the considered methods themselves, but rather as a simulation of the decision process between different predictive methods to be used by practitioners, in case of presence of locally available models.
The specific matter of choice between available predictive methods (locally derived or HSM-derived) and an example of comparison between outputs of different methods is addressed in next sub-section.
If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended.
