efast_run_Analysis_from_DB

Set of simulation results mined from the database,
put into the format required by spartan

efast_sim_results

Number of samples taken per parameter

number_samples

Array containing the names of the parameters of which
parameter samples have been generated

parameters

The number of 'resamples' to perform (see eFAST
documentation) - recommend using at least 3

number_curves

Array containing the names of the output measures which
are used to analyse the simulation

measures

Which measures in the range should be tested
to see if the result is statistically significant. To do all, and if
there were 3 measures, this would be set to 1:3

OUTPUTMEASURES_TO_TTEST

The level of significance to use for the T-Test
(e.g. 0.95)

TTEST_CONF_INT

Produces a file summarising the analysis; partitioning the variance between
parameters and providing relevant statistics. These include, for each
parameter of interest, first-order sensitivity index (Si), total-order
sensitivity index (STi), complementary parameters sensitivity index (SCi),
and relevant p-values and error bar data calculated using a two-sample
t-test and standard error respectively. For a more detailed examination of
 this analysis, see the references in the R Journal paper. For ease of
 representation, the method also produces a graph showing this data for
 each simulation output measure. In this case, this function works with
 spartanDB to analyse data stored in a database

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.

efast_run_Analysis_from_DB: Runs the eFAST Analysis for a set of results stored in a database

Description

Usage

Arguments