generate_emulator_model

Machine learning technique being employed

technique

Parameters on which the model will take as input

parameters

Simulation responses the model should predict

measures

The training dataset used to create the model

dataset

Algorithm settings object, of parameters used by
the emulation creation algorithm

algorithm_settings

Simulation timepoint for which an emulator is being created

timepoint

Whether the data being input as training data has been
normalised. Affects whether predictions generated by the emulator are
rescaled or not

normalised

File formats in which result graphs should be produced

output_formats

Internal function used to generate the requested emulator, and graph
training performance

internal

Computer simulations are becoming a popular technique to use in attempts to further our understanding of complex systems. 'spartan', first described in our 2013 publication in PLoS Computational Biology, provided code for four techniques described in available literature which aid the analysis of simulation results, at both single and multiple timepoints in the simulation run. The first technique addresses aleatory uncertainty in the system caused through inherent stochasticity, and determines the number of replicate runs necessary to generate a representative result. The second examines how robust a simulation is to parameter perturbation, through the use of a one-at-a-time parameter analysis technique. Thirdly, a latin hypercube based sensitivity analysis technique is included which can elucidate non-linear effects between parameters and indicate implications of epistemic uncertainty with reference to the system being modelled. Finally, a further sensitivity analysis technique, the extended Fourier Amplitude Sampling Test (eFAST) has been included to partition the variance in simulation results between input parameters, to determine the parameters which have a significant effect on simulation behaviour. Version 1.3 added support for Netlogo simulations, aiding simulation developers who use Netlogo to build their simulations perform the same analyses. Version 2.0 added the ability to read all simulations in from a single CSV file in addition to the prescribed folder structure in previous versions. Version 3.0 offers significant additional functionality that permits the creation of emulations of simulation results, derived using the same sampling techniques in the global sensitivity analysis techniques, and the generation of combinations of these machine learning algorithms to one create one predictive tool, more commonly known as an ensemble model. Version 3.0 also improved the standard of the graphs produced in the original sensitivity analysis techniques, and introduced a polar plot to examine parameter sensitivity.

generate_emulator_model: Internal function used to generate the requested emulator, and graph training performance

Description

Usage

Arguments

Value