Docstrings

abstract type AbstractLikelihoodProblem{N, L}

Abstract type of a likelihood problem, where N is the number of parameters and L is the type of the likelihood function.

LikelihoodProblem{N,P,D,L,Θ,S} <: AbstractLikelihoodProblem

Struct representing a likelihood problem.

Fields

• problem::P

The associated OptimizationProblem.

• data::D

The argument p used in the log-likelihood function.

• log_likelihood_function::L

The log-likelihood function, taking the form ℓ(θ, p).

• θ₀::Θ

Initial estimates for the MLE θ.

• syms::S

Variable names for the parameters.

The extra parameter N is the number of parameters.

Constructors

Standard

LikelihoodProblem(loglik::Function, θ₀;
    syms=eachindex(θ₀), data=SciMLBase.NullParameters(),
    f_kwargs=nothing, prob_kwargs=nothing)

Constructor for the LikelihoodProblem.

Arguments

• loglik::Function: The log-likelihood function, taking the form ℓ(θ, p).
• θ₀: The estimates estimates for the MLEs.

Keyword Arguments

• syms=eachindex(θ₀): Names for each parameter.
• data=SciMLBase.NullParameters(): The parameter p in the log-likelihood function.
• f_kwargs=nothing: Keyword arguments, passed as a NamedTuple, for the OptimizationFunction.
• prob_kwargs=nothing: Keyword arguments, passed as a NamedTuple, for the OptimizationProblem.

Outputs

Returns the LikelihoodProblem problem object.

With arguments for a differential equation problem

LikelihoodProblem(loglik::Function, θ₀,
    ode_function, u₀, tspan;
    syms=eachindex(θ₀), data=SciMLBase.NullParameters(),
    ode_parameters=SciMLBase.NullParameters(), ode_alg,
    ode_kwargs=nothing, f_kwargs=nothing, prob_kwargs=nothing)

Constructor for the LikelihoodProblem for a differential equation problem.

Arguments

• loglik::Function: The log-likelihood function, taking the form ℓ(θ, p, integrator).
• θ₀: The estimates estimates for the MLEs.
• ode_function: The function f(du, u, p, t) or f(u, p, t) for the differential equation.
• u₀: The initial condition for the differential equation.
• tspan: The time-span to solve the differential equation over.

Keyword Arguments

• syms=eachindex(θ₀): Names for each parameter.
• data=SciMLBase.NullParameters(): The parameter p in the log-likelihood function.
• ode_parameters=SciMLBase.NullParameters(): The parameter p in ode_function.
• ode_alg: The algorithm used for solving the differential equatios.
• ode_kwargs=nothing: Extra keyword arguments, passed as a NamedTuple, to pass into the integrator; see construct_integrator.
• f_kwargs=nothing: Keyword arguments, passed as a NamedTuple, for the OptimizationFunction.
• prob_kwargs=nothing: Keyword arguments, passed as a NamedTuple, for the OptimizationProblem.

Outputs

Returns the LikelihoodProblem problem object.

With an integrator

LikelihoodProblem(loglik::Function, θ₀, integrator;
    syms=eachindex(θ₀), data=SciMLBase.NullParameters(),
    f_kwargs=nothing, prob_kwargs=nothing)

Constructor for the LikelihoodProblem for a differential equation problem with associated integrator.

Arguments

• loglik::Function: The log-likelihood function, taking the form ℓ(θ, p, integrator).
• θ₀: The estimates estimates for the MLEs.
• integrator: The integrator for the differential equation problem. See also construct_integrator.

Keyword Arguments

• syms=eachindex(θ₀): Names for each parameter.
• data=SciMLBase.NullParameters(): The parameter p in the log-likelihood function.
• f_kwargs=nothing: Keyword arguments, passed as a NamedTuple, for the OptimizationFunction.
• prob_kwargs=nothing: Keyword arguments, passed as a NamedTuple, for the OptimizationProblem.

Outputs

Returns the LikelihoodProblem problem object.

abstract type AbstractLikelihoodSolution{N, P}

Type representing the solution to a likelihood problem, where N is the number of parameters and P is the type of the likelihood problem.

struct LikelihoodSolution{Θ,P,M,R,A} <: AbstractLikelihoodSolution

Struct for a solution to a LikelihoodProblem.

Fields

• mle::Θ: The MLEs.
• problem::P: The LikelihoodProblem.
• optimiser::A: The algorithm used for solving the optimisation problem.
• maximum::M: The maximum likelihood.
• retcode::R: The SciMLBase.ReturnCode.

Constructors

LikelihoodSolution(sol::SciMLBase.OptimizationSolution, prob::AbstractLikelihoodProblem; alg=sol.alg)

Constructs the likelihood solution from a solution to an OptimizationProblem with a given LikelihoodProblem.

mle(prob::LikelihoodProblem, alg, args...; kwargs...)
mle(prob::LikelihoodProblem, alg::Tuple, args...; kwargs...)

Given the likelihood problem prob and an optimiser alg, finds the MLEs and returns a LikelihoodSolution object. Extra arguments and keyword arguments for solve can be passed through args... and kwargs....

If alg is a Tuple, then the problem is re-optimised after each algorithm with the next element in alg, starting from alg[1], with initial estimate coming from the solution with the previous algorithm (starting with get_initial_estimate(prob)).

ProfileLikelihoodSolution{I,V,LP,LS,Spl,CT,CF,OM}

Struct for the normalised profile log-likelihood. See profile for a constructor.

Fields

• parameter_values::Dict{I, V}

This is a dictionary such that parameter_values[i] gives the parameter values used for the normalised profile log-likelihood of the ith variable.

• profile_values::Dict{I, V}

This is a dictionary such that profile_values[i] gives the values of the normalised profile log-likelihood function at the corresponding values in θ[i].

• likelihood_problem::LP

The original LikelihoodProblem.

• likelihood_solution::LS

The solution to the full problem.

• splines::Dict{I, Spl}

This is a dictionary such that splines[i] is a spline through the data (parameter_values[i], profile_values[i]). This spline can be evaluated at a point ψ for the ith variable by calling an instance of the struct with arguments (ψ, i). See also spline_profile.

• confidence_intervals::Dict{I,ConfidenceInterval{CT,CF}}

This is a dictonary such that confidence_intervals[i] is a confidence interval for the ith parameter.

• other_mles::OM

This is a dictionary such that other_mles[i] gives the vector for the MLEs of the other parameters not being profiled, for each datum.

Spline evaluation

This struct is callable. We define the method

(prof::ProfileLikelihoodSolution)(θ, i)

that evaluates the spline through the ith profile at the point θ.

struct ConfidenceInterval{T, F}

Struct representing a confidence interval.

Fields

• lower::T

The lower bound of the confidence interval.

• upper::T

The upper bound of the confidence interval.

• level::F

The level of the confidence interval.

profile(prob::LikelihoodProblem, sol::LikelihoodSolution, n=1:number_of_parameters(prob);
    alg=get_optimiser(sol),
    conf_level::F=0.95,
    confidence_interval_method=:spline,
    threshold=get_chisq_threshold(conf_level),
    resolution=200,
    param_ranges=construct_profile_ranges(sol, get_lower_bounds(prob), get_upper_bounds(prob), resolution),
    min_steps=10,
    normalise::Bool=true,
    spline_alg=FritschCarlsonMonotonicInterpolation,
    extrap=Line,
    parallel=false,
    next_initial_estimate_method = :prev,
    kwargs...)

Computes profile likelihoods for the parameters from a likelihood problem prob with MLEs sol.

See also replace_profile! which allows you to re-profile a parameter in case you are not satisfied with the results. For plotting, see the plot_profiles function (requires that you have loaded a backend of Makie.jl).

Arguments

• prob::LikelihoodProblem: The LikelihoodProblem.
• sol::LikelihoodSolution: The LikelihoodSolution. See also mle.
• n=1:number_of_parameters(prob): The parameter indices to compute the profile likelihoods for.

Keyword Arguments

• alg=get_optimiser(sol): The optimiser to use for solving each optimisation problem.
• conf_level::F=0.95: The level to use for the ConfidenceIntervals.
• confidence_interval_method=:spline: The method to use for computing the confidence intervals. See also get_confidence_intervals!. The default :spline uses rootfinding on the spline through the data, defining a continuous function, while the alternative :extrema simply takes the extrema of the values that exceed the threshold.
• threshold=get_chisq_threshold(conf_level): The threshold to use for defining the confidence intervals.
• resolution=200: The number of points to use for evaluating the profile likelihood in each direction starting from the MLE (giving a total of 2resolution points). - resolution=200: The number of points to use for defining grids below, giving the number of points to the left and right of each interest parameter. This can also be a vector, e.g. resolution = [20, 50, 60] will use 20 points for the first parameter, 50 for the second, and 60 for the third.
• param_ranges=construct_profile_ranges(sol, get_lower_bounds(prob), get_upper_bounds(prob), resolution): The ranges to use for each parameter.
• min_steps=10: The minimum number of steps to allow for the profile in each direction. If fewer than this number of steps are used before reaching the threshold, then the algorithm restarts and computes the profile likelihood a number min_steps of points in that direction. See also min_steps_fallback.
• min_steps_fallback=:replace: Method to use for updating the profile when it does not reach the minimum number of steps, min_steps. See also reach_min_steps!. If :replace, then the profile is completely replaced and we use min_steps equally spaced points to replace it. If :refine, we just fill in some of the space in the grid so that a min_steps number of points are reached. Note that this latter option will mean that the spacing is no longer constant between parameter values. You can use :refine_parallel to apply :refine in parallel.
• normalise::Bool=true: Whether to optimise the normalised profile log-likelihood or not.
• spline_alg=FritschCarlsonMonotonicInterpolation: The interpolation algorithm to use for computing a spline from the profile data. See Interpolations.jl.
• extrap=Line: The extrapolation algorithm to use for computing a spline from the profile data. See Interpolations.jl.
• parallel=false: Whether to use multithreading. If true, will use multithreading so that multiple parameters are profiled at once, and the steps to the left and right are done at the same time.
• next_initial_estimate_method = :prev: Method for selecting the next initial estimate when stepping forward when profiling. :prev simply uses the previous solution, but you can also use :interp to use linear interpolation. See also set_next_initial_estimate!.
• kwargs...: Extra keyword arguments to pass into solve for solving the OptimizationProblem. See also the docs from Optimization.jl.

Output

Returns a ProfileLikelihoodSolution.

replace_profile!(prof::ProfileLikelihoodSolution, n);
    alg=get_optimiser(prof.likelihood_solution),
    conf_level::F=0.95,
    confidence_interval_method=:spline,
    threshold=get_chisq_threshold(conf_level),
    resolution=200,
    param_ranges=construct_profile_ranges(prof.likelihood_solution, get_lower_bounds(prof.likelihood_problem), get_upper_bounds(prof.likelihood_problem), resolution),
    min_steps=10,
    min_steps_fallback=:replace,
    normalise::Bool=true,
    spline_alg=FritschCarlsonMonotonicInterpolation,
    extrap=Line,
    parallel=false,
    next_initial_estimate_method=:prev,
    kwargs...) where {F}

Given an existing prof::ProfileLikelihoodSolution, replaces the profile results for the parameters in n by re-profiling. The keyword arguments are the same as for profile.

refine_profile!(prof::ProfileLikelihoodSolution, n;
    alg=get_optimiser(prof.likelihood_solution),
    conf_level::F=0.95,
    confidence_interval_method=:spline,
    threshold=get_chisq_threshold(conf_level),
    target_number=10,
    normalise::Bool=true,
    spline_alg=FritschCarlsonMonotonicInterpolation,
    extrap=Line,
    parallel=false,
    kwargs...) where {F}

Given an existing prof::ProfileLikelihoodSolution, refines the profile results for the parameters in n by adding more points. The keyword arguments are the same as for profile. target_number is the total number of points that should be included in the end (not how many more are added).

set_next_initial_estimate!(sub_cache, param_vals, other_mles, prob, θₙ; next_initial_estimate_method=Val(:prev))

Method for selecting the next initial estimate for the optimisers. sub_cache is the cache vector for placing the initial estimate into, param_vals is the current list of parameter values for the interest parameter, and other_mles is the corresponding list of previous optimisers. prob is the OptimizationProblem. The value θₙ is the next value of the interest parameter.

The available methods are:

• next_initial_estimate_method = Val(:prev): If this is selected, simply use other_mles[end], i.e. the previous optimiser.
• next_initial_estimate_method = Val(:interp): If this is selected, the next optimiser is determined via linear interpolation using the data (param_vals[end-1], other_mles[end-1]), (param_vals[end], other_mles[end]). If the new approximation is outside of the parameter bounds, falls back to next_initial_estimate_method = :prev.

get_confidence_intervals!(confidence_intervals, method, n, param_vals, profile_vals, threshold, spline_alg, extrap, mles, conf_level)

Method for computing the confidence intervals.

Arguments

• confidence_intervals: The dictionary storing the confidence intervals.

• method: The method to use for computing the confidence interval. The available methods are:

  • method = Val(:spline): Fits a spline to (param_vals, profile_vals) and finds where the continuous spline equals threshold.
  • method = Val(:extrema): Takes the first and last values in param_vals whose corresponding value in profile_vals exceeds threshold.

• n: The parameter being profiled.

• param_vals: The parameter values.

• profile_vals: The profile values.

• threshold: The threshold for the confidence interval.

• spline_alg: The algorithm to use for fitting a spline.

• extrap: The extrapolation algorithm used for the spline.

• mles: The MLEs.

• conf_level: The confidence level for the confidence interval.

Outputs

There are no outputs - confidence_intervals[n] gets the ConfidenceInterval put into it.

reach_min_steps!(param_vals, profile_vals, other_mles, param_range,
    restricted_prob, n, cache, alg, sub_cache, ℓmax, normalise,
    threshold, min_steps, mles; min_steps_fallback=Val(:replace), next_initial_estimate_method=Val(:interp), kwargs...)

Updates the results from the side of a profile likelihood (e.g. left or right side, see find_endpoint!) to meet the minimum number of steps min_steps.

Arguments

• param_vals: The parameter values.
• profile_vals: The profile values.
• other_mles: The other MLEs, i.e. the optimised parameters for the corresponding fixed parameter values in param_vals.
• param_range: The vector of parameter values.
• restricted_prob: The optimisation problem, restricted to the nth parameter.
• n: The parameter being profiled.
• cache: A cache for the complete parameter vector.
• alg: The algorithm used for optimising.
• sub_cache: A cache for the parameter vector excluding the nth parameter.
• ℓmax: The maximum likelihood.
• normalise: Whether the optimisation problem is normalised.
• threshold: The threshold for the confidence interval.
• min_steps: The minimum number of steps to reach.
• mles: The MLEs.

Keyword Arguments

• min_steps_fallback=Val(:interp): The method used for reaching the minimum number of steps. The available methods are:

  • min_steps_fallback = Val(:replace): This method completely replaces the profile, defining a grid from the MLE to the computed endpoint with min_steps points. No information is re-used.
  • min_steps_fallback = Val(:refine): This method just adds more points to the profile, filling in enough points so that the total number of points is min_steps. The initial estimates in this case come from a spline from other_mles.
  • min_steps_fallback = Val(:parallel_refine): This applies the method above, except in parallel.

• next_initial_estimate_method=Val(:replace): The method used for obtaining initial estimates. See also set_next_initial_estimate!.

BivariateProfileLikelihoodSolution{I,V,LP,LS,Spl,CT,CF,OM}

Struct for the normalised bivariate profile log-likelihood. See bivariate_profile for a constructor.

Arguments

• parameter_values::Dict{I, G}

Maps the tuple (i, j) to the grid values used for this parameter pair. The result is a Tuple, with the first element the grid for the ith parameter, and the second element the grid for the jth parameter. The grids are given as OffsetVectors, with the 0th index the MLE, negative indices to the left of the MLE, and positive indices to the right of the MLE.

• profile_values::Dict{I, V}

Maps the tuple (i, j) to the matrix used for this parameter pair. The result is a OffsetMatrix, with the (k, ℓ) entry the profile at (parameter_values[(i, j)][1][k], parameter_values[(i, j)][2][k]), and particularly the (0, 0) entry is the profile at the MLEs.

• likelihood_problem::LP

The original likelihood problem.

• likelihood_solution::LS

The original likelihood solution.

• interpolants::Dict{I,Spl}

Maps the tuple (i, j) to the interpolant for that parameter pair's profile. This interpolant also uses linear extrapolation.

• confidence_regions::Dict{I,ConfidenceRegion{CT,CF}}

Maps the tuple (i, j) to the confidence region for that parameter pair's confidence region. See also ConfidenceRegion.

• other_mles::OM

Maps the tuple (i, j) to an OffsetMatrix storing the solutions for the nuisance parameters at the corresponding grid values.

Interpolant evaluation

This struct is callable. We define the method

(prof::BivariateProfileLikelihoodSolution)(θ, ψ, i, j)

that evaluates the interpolant through the (i, j)th profile at the point (θ, ψ).

struct ConfidenceRegion{T, F}

Struct representing a confidence region.

Fields

• x::T

The x-coordinates for the region's boundary.

• y::T

The y-coordinates for the region's boundary.

• level::F

The level of the confidence region.

bivariate_profile(prob::LikelihoodProblem, sol::LikelihoodSolution, n::NTuple{M,NTuple{2,Int}};
    alg=get_optimiser(sol),
    conf_level::F=0.95,
    confidence_region_method=Val(:contour),
    threshold=get_chisq_threshold(conf_level, 2),
    resolution=200,
    grids=construct_profile_grids(n, sol, get_lower_bounds(prob), get_upper_bounds(prob), resolution),
    min_layers=10,
    outer_layers=0,
    normalise=Val(true),
    parallel=Val(false),
    next_initial_estimate_method=Val(:nearest),
    kwargs...) where {M,F}

Computes bivariates profile likelihoods for the parameters from a likelihood problem prob with MLEs sol. You can also call this function using Symbols, e.g. if get_syms(prob) = [:λ, :K, :u₀], then calling bivariate_profile(prob, sol, ((:λ, :K), (:K, u₀))) is the same as calling bivariate_profile(prob, sol, ((1, 2), (2, 3))) (the integer coordinate representation is still used in the solution, though).

For plotting, see the plot_profiles function (requires that you have loaded a backend of Makie.jl).

Arguments

• prob::LikelihoodProblem: The LikelihoodProblem.
• sol::LikelihoodSolution: The LikelihoodSolution. See also mle.
• n::NTuple{M,NTuple{2,Int}}: The parameter indices to compute the profile likelihoods for. These should be tuples of indices, e.g. n = ((1, 2),) will compute the bivariate profile between the parameters 1 and 2.

Keyword Arguments

• alg=get_optimiser(sol): The optimiser to use for solving each optimisation problem.
• conf_level::F=0.95: The level to use for the ConfidenceRegions.
• confidence_region_method=:contour: The method to use for computing the confidence regions. See also get_confidence_regions!. The default, :contour, using Contour.jl to compute the boundary of the confidence region. An alternative option is :delaunay, which uses DelaunayTriangulation.jl and triangulation contouring to find the boundary. This latter option is only available if you have already done using DelaunayTriangulation.
• threshold=get_chisq_threshold(conf_level, 2): The threshold to use for defining the confidence regions.
• resolution=200: The number of points to use for defining grids below, giving the number of points to the left and right of each interest parameter. This can also be a vector, e.g. resolution = [20, 50, 60] will use 20 points for the first parameter, 50 for the second, and 60 for the third. When defining the grid between pairs of values, the maximum of the two resolutions is used (thus defining a square grid).
• grids=construct_profile_grids(n, sol, get_lower_bounds(prob), get_upper_bounds(prob), resolution): The grids to use for each parameter pair.
• min_layers=10: The minimum number of layers to allow for the profile away from the MLE. If fewer than this number of layers are used before reaching the threshold, then the algorithm restarts and computes the profile likelihood a number min_steps of points in that direction.
• outer_layers=0: The number of layers to go out away from the bounding box of the confidence region.
• normalise=true: Whether to optimise the normalised profile log-likelihood or not.
• parallel=false: Whether to use multithreading. If true, will use multithreading so that multiple parameters are profiled at once, and the work done evaluating the solution at each node in a layer is distributed across each thread.
• next_initial_estimate_method = :nearest: Method for selecting the next initial estimate when stepping onto the next layer when profiling. :nearest simply uses the solution at the nearest node from the previous layer, but you can also use :mle to reuse the MLE or :interp to use linear interpolation. See also set_next_initial_estimate!.
• kwargs...: Extra keyword arguments to pass into solve for solving the OptimizationProblem. See also the docs from Optimization.jl.

Output

Returns a BivariateProfileLikelihoodSolution.

set_next_initial_estimate!(sub_cache, other_mles, I::CartesianIndex, fixed_vals, grid, layer, prob, next_initial_estimate_method::Val{M}) where {M}

Method for selecting the next initial estimate for the optimisers.

Arguments

• sub_cache: Cache for the next initial estimate.
• other_mles: Solutions to the optimisation problems found so far.
• I::CartesianIndex: The coordinate of the node currently being considered.
• fixed_vals: The current values for the parameters of interest.
• grid: The grid for the parameters of interest.
• layer: The current layer.
• prob: The restricted optimisation problem.
• next_initial_estimate_method::Val{M}: The method to use.

The methods currently available for next_initial_estimate_method are:

• next_initial_estimate_method = Val(:mle): Simply sets sub_cache to be the MLE.
• next_initial_estimate_method = Val(:nearest): Sets sub_cache to be other_mles[J], where J is the nearest node to I in the previous layer.
• next_initial_estimate_method = Val(:interp): Uses linear interpolation from all the previous layers to extrapolate and compute a new sub_cache.

Outputs

There are no outputs.

get_prediction_intervals(q, prof::(Bivariate)ProfileLikelihoodSolution, data;
    q_prototype=isinplace(q, 3) ? nothing : build_q_prototype(q, prof, data), resolution=250)

Obtain prediction intervals for the output of the prediction function q, assuming q returns (or operates in-place on) a vector.

Arguments

• q: The prediction function, taking either the form (θ, data) or (cache, θ, data). The former version is an out-of-place version, returning the full vector, while the latter version is an in-place version, with the output being placed into cache. The argument θ is the same as the parameters used in the likelihood problem (from prof), and the data argument is the same data as in this function.
• prof::(Bivariate)ProfileLikelihoodSolution: The profile likelihood results.
• data: The argument data in q.

Keyword Arguments

• q_prototype=isinplace(q, 3) ? nothing : build_q_prototype(q, prof, data): A prototype for the result of q. If you are using the q(θ, data) version of q, this can be inferred from build_q_prototype, but if you are using the in-place version then a build_q_prototype is needed. For example, if q returns a vector with eltype Float64 and has length 27, q_prototype could be zeros(27).
• resolution::Integer=250: The amount of curves to evaluate for each parameter. This will be the same for each parameter. If prof isa BivariateProfileLikelihoodSolution, then resolution^2 points are defined inside a bounding box for the confidence region, and then we throw away all points outside of the actual confidence region.
• parallel=false: Whether to use multithreading. Multithreading is used when building q_vals below.

Outputs

Four values are returned. In order:

• individual_intervals: Prediction intervals for the output of q, relative to each parameter.
• union_intervals: The union of the individual prediction intervals from individual_intervals.
• q_vals: Values of q at each parameter considered. The output is a Dict, where the parameter index is mapped to a matrix where each column is an output from q, with the jth column corresponding to the parameter value at param_ranges[j].
• param_ranges: Parameter values used for each prediction interval.

plot_profiles(prof::ProfileLikelihoodSolution, vars = profiled_parameters(prof); 
    ncol=nothing, 
    nrow=nothing,
    true_vals=Dict(vars .=> nothing), 
    spline=true, 
    show_mles=true, 
    shade_ci=true, 
    fig_kwargs=nothing, 
    axis_kwargs=nothing,
    show_points=false,
    markersize=9,
    latex_names = Dict(vars .=> [L"	heta_{i}" for i in SciMLBase.sym_to_index.(vars, Ref(prof))]))

Plot results from a profile likelihood solution prof. To use this function you you need to have done using CairoMakie (or any other Makie backend).

Arguments

• prof::ProfileLikelihoodSolution: The profile likelihood solution from profile.
• vars = profiled_parameters(prof): The parameters to plot.

Keyword Arguments

• ncol=nothing: The number of columns to use. If nothing, chosen automatically via choose_grid_layout.
• nrow=nothing: The number of rows to use. If nothing, chosen automatically via choose_grid_layout
• true_vals=Dict(vars .=> nothing): A dictionary mapping parameter indices to their true values, if they exist. If nothing, nothing is plotted, otherwise a black line is plotted at the true value for the profile.
• spline=true: Whether the curve plotted should come from a spline through the results, or if the data itself should be plotted.
• show_mles=true: Whether to put a red line at the MLEs.
• shade_ci=true: Whether to shade the area under the profile between the confidence interval.
• fig_kwargs=nothing: Extra keyword arguments for Figure (see the Makie docs).
• axis_kwargs=nothing: Extra keyword arguments for Axis (see the Makie docs).
• show_points=false: Whether to show the profile data.
• markersize=9: The marker size used for show_points.
• latex_names = Dict(vars .=> [L" heta_{i}" for i in SciMLBase.sym_to_index.(vars, Ref(prof))])): LaTeX names to use for the parameters. Defaults to θᵢ, where i is the index of the parameter.

Output

The Figure() is returned.

plot_profiles(prof::BivariateProfileLikelihoodSolution, vars = profiled_parameters(prof); 
    ncol=nothing,
    nrow=nothing,
    true_vals=Dict(1:number_of_parameters(get_likelihood_problem(prof)) .=> nothing),
    show_mles=true,
    fig_kwargs=nothing,
    axis_kwargs=nothing,
    interpolation=false,
    smooth_confidence_boundary=false,
    close_contour=true,
    latex_names=Dict(1:number_of_parameters(get_likelihood_problem(prof)) .=> get_syms(prof)))

Plot results from a bivariate profile likelihood solution prof. To use this function you you need to have done using CairoMakie (or any other Makie backend).

Arguments

• prof::ProfileLikelihoodSolution: The profile likelihood solution from profile.
• vars = profiled_parameters(prof): The parameters to plot.

Keyword Arguments

• ncol=nothing: The number of columns to use. If nothing, chosen automatically via choose_grid_layout.
• nrow=nothing: The number of rows to use. If nothing, chosen automatically via choose_grid_layout
• true_vals=Dict(1:number_of_parameters(get_likelihood_problem(prof)) .=> nothing): A dictionary mapping parameter indices to their true values, if they exist. If nothing, nothing is plotted, otherwise a black dot is plotted at the true value on the bivariate profile's plot.
• show_mles=true: Whether to put a red dot at the MLEs.
• fig_kwargs=nothing: Extra keyword arguments for Figure (see the Makie docs).
• axis_kwargs=nothing: Extra keyword arguments for Axis (see the Makie docs).
• interpolation=false: Whether to plot the profile using the interpolant (true), or to use the data from prof directly (false).
• smooth_confidence_boundary=false: Whether to smooth the confidence region boundary when plotting (true) or not (false). The smoothing is done with a spline.
• close_contour=true: Whether to connect the last part of the confidence region boundary to the beginning (true) or not (false).
• latex_names=Dict(1:number_of_parameters(get_likelihood_problem(prof)) .=> get_syms(prof))): LaTeX names to use for the parameters. Defaults to the syms names.
• xlim_tuples=nothing: xlims to use for each plot. nothing if the xlims should be set automatically.
• ylim_tuples=nothing: ylims to use for each plot. nothing if the ylims should be set automatically.

Output

The Figure() is returned.

abstract type AbstractGrid{N,B,T}

Type representing a grid, where N is the number of parameters, B is the type for the bounds, and T is the number type.

struct RegularGrid{N,B,R,S,T} <: AbstractGrid{N,B,T}

Struct for a grid in which each parameter is regularly spaced.

Fields

• lower_bounds::B: Lower bounds for each parameter.
• upper_bounds::B: Upper bounds for each parameter.
• resolution::R: Number of grid points for each parameter. If R <: Number, then the same number of grid points is used for each parameter.
• step_sizes::S: Grid spacing for each parameter.

Constructor

You can construct a RegularGrid using RegularGrid(lower_bounds, upper_bounds, resolution).

struct FusedRegularGrid{N,B,R,S,T,C,OR} <: AbstractGrid{N,B,T}

Struct representing the fusing of two grids.

Fields

• positive_grid::RegularGrid{N,B,R,S,T}

This is the first part of the grid, indexed into by positive integers.

• negative_grid::RegularGrid{N,B,R,S,T}

This is the second part of the grid, indexed into by negative integers.

• centre::C

The two grids meet at a common centre, and this is that centre.

• resolutions::R

This is the vector of resolutions provided (e.g. if store_original_resolutions=true in the constructor below), or the transformed version from get_resolution_tuples.

Constructor

You can construct a FusedRegularGrid using the method

FusedRegularGrid(lower_bounds::B, upper_bounds::B, centre::C, resolutions::R; store_original_resolutions=false) where {B,R,C}

For example, the following code creates fused as the fusion of grid_1 and grid_2:

lb = [2.0, 3.0, 1.0, 5.0]
ub = [15.0, 13.0, 27.0, 10.0]
centre = [7.3, 8.3, 2.3, 7.5]
grid_1 = RegularGrid(centre .+ (ub .- centre) / 173, ub, 173)
grid_2 = RegularGrid(centre .- (centre .- lb) / 173, lb, 173)
fused = ProfileLikelihood.FusedRegularGrid(lb, ub, centre, 173)

There are 173 points to the left and right of centre in this case. To use a varying number of points, use e.g.

lb = [2.0, 3.0, 1.0, 5.0, 4.0]
ub = [15.0, 13.0, 27.0, 10.0, 13.0]
centre = [7.3, 8.3, 2.3, 7.5, 10.0]
res = [(2, 11), (23, 25), (19, 21), (50, 51), (17, 99)]
grid_1 = RegularGrid(centre .+ (ub .- centre) ./ [2, 23, 19, 50, 17], ub, [2, 23, 19, 50, 17])
grid_2 = RegularGrid(centre .- (centre .- lb) ./ [11, 25, 21, 51, 99], lb, [11, 25, 21, 51, 99])
fused = ProfileLikelihood.FusedRegularGrid(lb, ub, centre, res) # fused grid_1 and grid_2

struct IrregularGrid{N,B,R,S,T} <: AbstractGrid{N,B,T}

Struct for an irregular grid of parameters.

Fields

• lower_bounds::B: Lower bounds for each parameter.
• upper_bounds::B: Upper bounds for each parameter.
• grid::G: The set of parameter values, e.g. a matrix where each column is the parameter vector.

Constructor

You can construct a IrregularGrid using IrregularGrid(lower_bounds, upper_bounds, grid).

struct GridSearch{F,G}

Struct for a GridSearch.

Fields

• f::F: The function to optimise, of the form f(x, p).
• p::P: The arguments p in the function f.
• grid::G: The grid, where G<:AbstractGrid. See also grid_search.

grid_search(prob; save_vals=Val(false), minimise:=Val(false), parallel=Val(false))

Performs a grid search for the given grid search problem prob.

Arguments

• prob::GridSearch{F, G}: The grid search problem.

Keyword Arguments

• save_vals:=Val(false): Whether to return a array with the function values.
• minimise:=Val(false): Whether to minimise or to maximise the function.
• parallel:=Val(false): Whether to run the grid search with multithreading.

Outputs

• f_opt: The optimal objective value.
• x_argopt: The parameter that gave f_opt.
• f_res: If save_vals==Val(true), then this is the array of function values.

grid_search(f, grid::AbstractGrid; save_vals=Val(false), minimise=Val(false), parallel=Val(false))

For a given grid and function f, performs a grid search.

Arguments

• f: The function to optimise.
• grid::AbstractGrid: The grid to use for optimising.

Keyword Arguments

• save_vals=Val(false): Whether to return a array with the function values.
• minimise=Val(false): Whether to minimise or to maximise the function.
• parallel=Val(false): Whether to run the grid search with multithreading.

grid_search(prob::LikelihoodProblem, grid::AbstractGrid, parallel=Val(false); save_vals=Val(false))

Given a grid and a likelihood problem prob, maximises it over the grid using a grid search. If save_vals==Val(true), then the likelihood function values at each gridpoint are returned. Set parallel=Val(true) if you want multithreading.