aa_getATestResults_overTime

Directory where the results of the simulation runs, in folders or in single CSV file format, can be found

FILEPATH

The sample sizes chosen (i.e. in our case, this would be an array containing 1,5,50,100,300,800

SAMPLESIZES

The number of subsets for each sample size (i.e in the tutorial case, 20)

NUMSUBSETSPERSAMPLESIZE

An array containing the names of the simulation output measures to be analysed.

MEASURES

Name of the file that will contain the A-Test scores for each sample size

ATESTRESULTSFILENAME

The A-Test determines there is a large difference between two sets if the result is greater than 0.2 either side of the 0.5 line. Should this not be suitable, this can be changed here

LARGEDIFFINDICATOR

The name of the CSV file containing the simulation responses, if reading from a CSV file

AA_SIM_RESULTS_FILE

The name of the R object containing the simulation responses, if not reading from a CSV file

AA_SIM_RESULTS_OBJECT

Implemented so this method can be used when analysing multiple simulation timepoints. If only analysing one timepoint, this should be set to NULL. If not, this should be an array of timepoints, e.g. c(12,36,48,60)

TIMEPOINTS

Implemented so this method can be used when analysing multiple simulation timepoints. Sets the scale of the timepoints being analysed, e.g. "Hours"

TIMEPOINTSCALE

Used internally by the getATestResults method when producing graphs for multiple timepoints. Should not be set in function call.

GRAPHNAME

Get A-Test results for multiple simulation timepoints

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.

aa_getATestResults_overTime: Get A-Test results for multiple simulation timepoints

Description

Usage

Arguments