emulate_lhc_sampled_parameters

Path to where the analysis output should be stored

filepath

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

surrogateModel

parameters

measures

Scale of each of the simulation responses

measure_scale

Name of the CSV file generated by spartan (or of)
parameter values, separated in columns - if reading these in from a file

param_file

Name of the R dataset in the environment that contains the
parameter sets (the tutorial one is emulated_lhc_values)

dataset

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 file 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

Scale of the timepoints, if being used

timepointscale

Whether output should be written to CSV file. Used with spartanDB

write_csv_files

Whether plots should be produced for this analysis

graph_results

This method runs an ensemble for all parameter value sets specified in a CSV
file generated from spartan latin-hypercube sampling techniques. The output
is a CSV file that can then be analysed using the spartan analysis methods
detailed in Technique 3.

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_lhc_sampled_parameters: Emulate simulations for a set of latin-hypercube generated parameter values

Description

Usage

Arguments