lhc_graphMeasuresForParameterChange_from_db

Results for a specified experiment mined from the database

db_results

Correlation coefficients calculated for those results,
held in the databae

corcoeffs

Parameters included in this analysis

parameters

measures

Scale in which each of the output responses is
measured. Used to label plots

MEASURE_SCALE

Folder where the graphs should be stored

output_directory

Type of graph to plot. Can be PDF, PNG, TIFF, BMP, etc,
all formats supported by ggplot2

OUTPUT_TYPE

Produces a graph for each parameter, and each output measure, showing
the simulation output achieved when that parameter was assigned that value.
Eases identification of any non-linear effects. This method uses simulation
results stored in a database by spartanDB

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.

lhc_graphMeasuresForParameterChange_from_db: Generates parameter/measure plot for each pairing in the analysis, from results stored in a database

Description

Usage

Arguments