Through Model Selection, alternative model hypothesis results are competed to determine which hypothesis is best explained by the underlying data. Before conducting model selection, you should be familiar with the benefits and limitations of alternative model selection statistics.
To calculate Akaike weights for a set of hypotheses, you must first obtain
your results by either loading in saved result files, or running models directly.
Once you have obtained your results, weights can be saved after calculation
by using the functions within the
Bristlecone.Data namespace as below:
open Bristlecone fun results -> let resultsDirectory = "some/results/directory/" let weights = results |> ModelSelection.weights // Save the weights into a csv file weights |> Data.ModelSelection.save resultsDirectory