CalibrationResult

This structure contains the results obtained from calling calibrate. The struct behaves like an AbstractVector, but it also provides a Tables.jl interface. The result is considered a table with a single row, the column names being the names in the search space. For example if the search_space contains something like [a => (0, 1.), (b => (0, 2.))], you can get the tuned value for a with result[:a], where result is the corresponding CalibrationResult.

One consequence for the Tables.jl interface is that one can easily create a DataFrame out of the result with just DataFrame(results), or can write it to as .csv file with CSV.write(io, result). To create a vector of pairs, Tables.columnnames(results) .=> results can be used.

A convergence plot showing how the value of the loss function evolved during calibration can be obtained using convergenceplot(result).

Note that the order of the columns in the result is not necessarily the order that was provided in the search_space argument of the inverse problem depending on internal optimizations. The recommended way of obtaining the order of the columns is via the Tables.jl interface, Tables.columnnames(results).

ParameterEnsemble

The structure contains the result of a parameter ensemble when a StochGlobalOpt method was used to generate the population. To export results to a DataFrame use DataFrame(ps) and to plot them use plot(ps, experiment), where ps is the ParameterEnsemble and experiment is the AbstractExperiment, whose default parameter (or initial condition) values will be used.

MCMCResult

The structure contains the result of a parameter ensemble when an MCMCOpt method was used to generate the population. To export results to a DataFrame use DataFrame(ps) and to plot them use plot(ps, experiment), where ps is the VpopResult and experiment is the AbstractExperiment, whose default parameter (or initial condition) values will be used.

The original Markov chain object that the MCMC method generated can be accessed using ps.original. This object contains summary statistics for each parameter and diagnostics of the MCMC method (e.g. Rhat and Effective Sample Size). See the MCMCChains.jl documentation for more information about diagnostic checks that can be run on ps.original.

import_res(res, prob)

Import a CalibrationResult object (CalibrationResult) from tabular data or a vector. If a table (in the sense of Tables.jl) in provided, it is assumed to have one row. Note that since a Vector is one column and calibration results are one row, the input is internally permuted.

Arguments

• res: tabular data that contains the results of a calibration. This could be e.g a DataFrame, a CSV.File or a Vector.
• prob: an InverseProblem object.

Example

julia> import_res([1,2], prob)
Calibration result imported from Array.

┌────┬────┐
│ k1 │ c1 │
├────┼────┤
│  1 │  2 │
└────┴────┘

import_ps(ps, prob)

Import a ParameterEnsemble object (ParameterEnsemble) from tabular data.

Arguments

• ps: tabular data that contains the parameters of the population. This could be e.g a DataFrame or a CSV.File.
• prob: an InverseProblem object.