prepare_parameter_value_list

Vector of the minimum values for each parameter

PMIN

Vector of the maximum values for each parameter

PMAX

Vector of the sample increment value for each parameter

PINC

Vector of the sampling values specified for each parameter,
rather than an incremental sequence

PARAMVALS

The current parameter being analysed

PARAM_OF_INT

When iterating through parameter values, we need to do a bit of prep, as
there are two ways these values can be specified (increment and specified
in a list). This returns a list of the values for both specifications, so
these can be iterated through without issue

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.

prepare_parameter_value_list: Prepares the parameter value list, as either an interval range or specific values can be supplied

Description

Usage

Arguments