lhc_generate_lhc_sample_netlogo

Directory where the parameter samples are to be stored

FILEPATH

Array containing the names of the parameters of which parameter samples will be generated

PARAMETERS

Array containing either the parameter value (if not of
interest in the analysis), or range under which this is being explored
(stated as as string e.g. "[5,50]" for a range of 5-50.

PARAMVALS

The number of parameter subsets to be generated in the LHC design

NUMSAMPLES

Choice of algorithm to use to generate the hypercube. Can be set to either 'normal' or 'optimum'. Beware optimum can take a long time to generate an optimised parameter set (more than 24 hours in some circumstances).

ALGORITHM

The number of times Netlogo should repeat the experiment for each set of parameter values.

EXPERIMENT_REPETITIONS

Boolean stating whether Netlogo should produce output for each timestep.

RUN_METRICS_EVERYSTEP

The name of the function in Netlogo that sets up the simulation. Commonly is named setup.

NETLOGO_SETUP_FUNCTION

The name of the function in Netlogo that starts the simulation. Commonly named go.

NETLOGO_RUN_FUNCTION

Array containing the names of the Netlogo output measures which are used to analyse the simulation.

MEASURES

This generates a specified number of simulation parameters sets using
latin-hypercube sampling. These are then processed into Netlogo XML
experiment files, one for each set of parameters.

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.

lhc_generate_lhc_sample_netlogo: Prepares Netlogo experiment files for a sampling-based sensitivity analysis, using latin-hypercube sampling

Description

Usage

Arguments