plotATestsFromTimepointFiles

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

FILEPATH

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

PARAMETERS

Name of the CSV file containing the A-Test results to be plotted

ATESTRESULTFILENAME

Value to plot for a large difference between distributions on this plot

ATESTSIGLEVEL

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

MEASURES

Array containing the minimum value that should be used for each parameter. Sets a lower bound on sampling space

PMIN

Array containing the maximum value that should be used for each parameter. Sets an upper bound on sampling space

PMAX

Array containing the increment value that should be applied for each parameter. For example, a parameter could have a minimum value of 10, and maximum value of 100, and be incremented by 10

PINC

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

When plotting a time-course analysis, it may be useful to compare results
gained at multiple timepoints, and determine the differences in
performance over time. This function provides a means of plotting those
results

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.

plotATestsFromTimepointFiles: Plots the A-Tests for all timepoints being examined

Description

Usage

Arguments