bristlecone


Diagnostics

Diagnostic statistics are essential to ensure that the model-fitting and model-selection results are valid and complete.

Convergence Statistics

For Monte-Carlo based optimisation techniques (such as the random walk MCMC or the Filzbach-style MCMC) convergence statistics are useful to assess whether multiple independent chains have identified the same minimum -log likelihood in parameter space, which may be the global minimum.

Bristlecone includes the Gelman-Rubin convergence statistic (Rhat), which can be calculated on a per-parameter basis.

open Bristlecone
open Bristlecone.Diagnostics
open Bristlecone.Data

let saveDirectory = "/some/save/dir"

fun listOfResults ->

    // Calculate Gelman-Rubin statistic for all parameters 
    // across multiple model runs
    let stat = Convergence.gelmanRubinAll 1000 listOfResults

    // Save results to a single file
    Convergence.save saveDirectory stat

Logging individual model components

Sometimes it is helpful for diagnostic or visualisation purposes to see the values of individual parts of a mathematical model during computation, or at the minimum -log likelihood.

Bristlecone includes the ability to log out the values of individual model components through time. An worked example is given below of how to obtain computed values across the time-series for components within a model system. First, you must set up a model that has a parameter of the IComponentLogger<float>) type:

[ NB TODO: This function must be refactored to work with the new Bristlecone Language ]

open Bristlecone
open Bristlecone.Language
open Bristlecone.Diagnostics.ModelComponents

// let hypothesis (cLog:IComponentLogger<ModelExpression>) =

//     let ``dN/dt`` = 
//         Parameter "r" * This * cLog.StoreValue "log this thing" (Constant 1. - (This / Parameter "K"))

//     Model.empty
//     |> Model.addEquation       "N"      ``dN/dt``
//     |> Model.estimateParameter "r"      noConstraints 0.10 5.00 
//     |> Model.estimateParameter "K"      noConstraints 50.0 150.
//     |> Model.useLikelihoodFunction      (ModelLibrary.Likelihood.sumOfSquares [ "N" ])
//     |> Model.compile


// fun engine singleResult ->
//     // A wrapper around the Bristlecone.fit function
//     let fitFn ts m e = Bristlecone.fit e (Optimisation.EndConditions.afterIteration 1) ts m
//     let logs = ModelComponents.calculateComponents fitFn engine singleResult
//     ()
namespace Bristlecone
Multiple items
module Bristlecone from Bristlecone

--------------------
namespace Bristlecone
module Diagnostics from Bristlecone
<summary> Diagnostic techniques for determining the suitability of results obtained with Bristlecone. </summary>
namespace Bristlecone.Data
val saveDirectory : string
val listOfResults : ModelSelection.ResultSet.ResultSet<string,ModelSystem.ModelSystem> list
val stat : seq<seq<Convergence.ConvergenceStatistic>>
Multiple items
module Convergence from Bristlecone.Data

--------------------
module Convergence from Bristlecone.Diagnostics
<summary> Convergence diagnostics for monte-carlo markov chain (MCMC) analyses. </summary>
val gelmanRubinAll : n:int -> results:ModelSelection.ResultSet.ResultSet<string,ModelSystem.ModelSystem> list -> seq<seq<Convergence.ConvergenceStatistic>>
<summary> Calculate the Gelman-Rubin statistic for each parameter in all of the given `ResultSet`. The statistic tends downwards to one, with one indicating perfect convergence between all chains. </summary>
val save : directory:string -> result:seq<seq<Convergence.ConvergenceStatistic>> -> unit
module Language from Bristlecone
<summary> An F# Domain Specific Language (DSL) for scripting with Bristlecone. </summary>
module ModelComponents from Bristlecone.Diagnostics
<summary> Logging functions to output the internal dynamics of model systems. </summary>