Model Selection

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.

Akaike Weights

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 =
        |> ModelSelection.weights

    // Save the weights into a csv file
    |> resultsDirectory
namespace Bristlecone
val results : seq<ModelSelection.ResultSet.ResultSet<string,'a>>
val resultsDirectory : string
val weights : (string * string * ModelSystem.EstimationResult * ModelSelection.Akaike.AkaikeWeight) list
module ModelSelection from Bristlecone
val weights : results:seq<ModelSelection.ResultSet.ResultSet<'a,'b>> -> ('a * string * ModelSystem.EstimationResult * ModelSelection.Akaike.AkaikeWeight) list (requires equality)
Multiple items
namespace Bristlecone.Data

namespace Microsoft.FSharp.Data
module ModelSelection from Bristlecone.Data
val save : directory:string -> result:seq<string * string * ModelSystem.EstimationResult * ModelSelection.Akaike.AkaikeWeight> -> unit