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In a first step, we applied no model simplification, but rather searched the full model t-table for whether it contained a significant effect (P < 0.05).
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The proposed work flow does not automate model simplifications, but the identifiability ranking of parameters only provides hints on which parts of the model are too detailed.
Thus, model simplification slightly increases the type I error rate, but the difference is small for large sample sizes (N = 200 in our simulation).
As pointed out before [ 2], the model simplification step (step 4) cannot be carried out automatically, but requires insight into the model.
But the number of degrees of freedom is huge and some model simplification is required to compute a whole stack.
We did not perform any model simplification and assessed model fit based on χ2-statistics.
As model simplification results in multiple comparisons, we also tested the significance of each forest attribute in full models, which were not simplified.
We used both backward and forward stepwise model simplification, based on AIC to find a minimal adequate model for each response, using the function stepAIC (MASS package73).
The characteristics of the ELO model simplification to its original model are revealed.
Standard model simplification should be readily available so as to derive simplified models automatically.
In the applications, we show the interest of the new sensitivity indices for model simplification setting.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com