emulate_efast_sampled_parameters

Path to the folder containing the set of CSV files for eFAST
analysis

filepath

Ensemble or emulator to use to emulate the simulation
responses for those sets

surrogateModel

parameters

measures

Number of resample curves used in eFAST sampling

num_curves

Boolean stating whether this analysis is being run using
an ensemble of machine learning methods (TRUE), or a single emulator (FALSE)

ensemble_set

Whether the parameters in the sampling CSV files need to be
normalised before input to the emulator (which must take values between 0
and 1)

normalise

Simulation timepoint being analysed, if more than one. See
the vignette for analysing more than one timepoint

timepoint

Whether parameters to emulate are being supplied in a CSV
file. Default is TRUE, with spartanDB this is false

csv_file_input

Where spartanDB is used, or another application that
supplied parameter values as an R object created by spartan's sampling method,
the name of that R object

spartan_sample_obj

Whether results for each curve should be output to a
CSV file. SpartanDB instead requires an R object containing this data, which is
returned

write_csv_file_out

Whether the resultant predictions should be
normalised

normalise_result

This method runs an ensemble for all parameter value sets specified in a CSV
file generated from spartan eFAST sampling techniques. The output is a set
of CSV files that can then be analysed using the spartan analysis methods
detailed in Technique 4.

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.

emulate_efast_sampled_parameters: Emulate simulations for a set of eFAST generated parameter values

Description

Usage

Arguments