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commented
5 months ago Hi
I am happy the package is useful!
Bootstrapping after model averaging is non-trivial and not something I have done myself. And it is beyond my level of statistical expertise! There remains "no generally reliable analytical methods to calculate frequentist confidence intervals (or P-values) on model-averaged predictions", which may limit you here!
I would probably recommend if you have several equally good models looking at where they differ and choosing a single model to go forward if you want to bootstrap, or to do the model average approach and accept there is no bootstrap. If you're best model correlate strongly in terms of their fit, it probably hints that choosing a "best" model from that set is not going to impact the uncertainty predictions very much, as they predict the curve the same.
If you want to read more, here are some links that might be helpful:
- https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1309
- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213715#sec011
- https://www.sciencedirect.com/science/article/pii/S0167947312001144
Sorry I cannot be more helpful.
mhasoba
juanharagon
Hello! First of all congratulations on this excellent code and thank you so much for sharing it with the community. rTPC is making this kind of analysis better for everyone, so it is an incredible contribution to science.
I am currently working with spider thermal performance curves using climbing speed as a proxy. I usually have between 5 and 6 different experimental temperatures, including CTmax and CTmin.
I ran into a problem trying to combine some concepts. I wanted to do model averaging and Bootstrap. Is this useful in your opinion? if so, would it be better to bootstrap all models before averaging, or to do an average model and later bootstrap it? each model should also contains several curves since we are comparing among species with several replicates within each one.
Thank you