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Thus, stepwise model selection was then repeated for all remaining variables.
Three different stepwise model selection techniques were applied, all yielding identical results, underlining the robustness of our results.
The stepwise model estimation procedure is chosen because the total number of potential models is computationally prohibitive.
The same two calculations are performed for the stepwise model.
We simulated microsatellite loci following a generalized stepwise model [34] [35].
In the multivariate analysis, stepwise model comparison was used to determine the best model.
However, a stepwise model involving multiple insertions is both simple and consistent with the data.
This model is closer to what is generally observed in terms of microsatellite mutation [34] than a simple stepwise model.
For each model, we applied stepwise model selection to identify the subset of traits that best predicted each behavioural variable.
Stepwise model simplification then helped to identify the minimal adequate model that necessitates the lowest number of parameter estimates [46].
Stepwise model selection revealed that bulbar onset and diagnostic delay were the best predictors of class membership (Table S4).
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