# Simulation Parameter Analysis R Toolkit ApplicatioN: Spartan

## Documentation for package ‘spartan’ version 3.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

aa_getATestResults | Calculates the A-Test scores observed for all sets, for each sample size
aa_graphATestsForSampleSize | Produce a plot for each sample size, showing the A-Test scores for each set of that size
aa_graphSampleSizeSummary | Plots a comparison of the maximum A-Test score for each sample size
aa_sampleSizeSummary | Determines the median and maximum A-Test score observed for each sample size
aa_summariseReplicateRuns | Summarise results in set folder structure into one single CSV file
analysenetwork_structures | Analyse each network structure provided as a potential NN structure
atest | Calculates the A-test score for two distributions
calculate_fold_MSE | Calculate the mean squared error for this fold in k-fold cross validation
calculate_weights_for_ensemble_model | Internal function to calculate the weights for all emulators in the ensemble
createAndEvaluateFolds | Create and evaluate folds within k-fold cross validation
createtest_fold | Create test data fold for k-fold cross validation
createTrainingFold | Create training data fold for k-fold cross validation
create_abc_settings_object | Creates ensemble-specific parameters for ABC analysis
create_ensemble | Internal function to create the ensemble
create_neural_network | Create neural network emulator, using neuralnet package
determine_optimal_neural_network_structure | Determine the optimal hidden layer structure from those provided
efast_generate_medians_for_all_parameter_subsets | Generates summary file for stochastic simulations stored in multiple files
efast_generate_sample | Generates parameter sets for variance-based eFAST Sensitivity Analysis
efast_generate_sample_netlogo | Prepares Netlogo experiment files for a variance-based sensitivity analysis, using eFAST
efast_get_overall_medians | Calculates the summary stats for each parameter set (median of any replicates)
efast_graph_Results | Plot the parition of variance in a simulation response for each measure
efast_netlogo_get_overall_medians | Deprecated: Use 'efast_netlogo_get_overall_medians'
efast_netlogo_run_Analysis | Deprecated: Use 'efast_run_Analysis'
efast_process_netlogo_result | Analyses Netlogo simulation data for parameter sets generated for eFAST
efast_run_Analysis | Runs the eFAST Analysis for the pre-generated summary file
emulated_lhc_values | Latin-hypercube value set use to demonstrate emulated sensitivity analysis
emulate_efast_sampled_parameters | Emulate simulations for a set of eFAST generated parameter values
emulate_lhc_sampled_parameters | Emulate simulations for a set of latin-hypercube generated parameter values
emulation_algorithm_settings | Initialise machine-learning algorithms settings for emulation creation
emulator_parameter_evolution | Evolve parameter sets that meet a desired ensemble outcome
emulator_predictions | Used to generate predictions from an emulator, normalising data if required
ensemble_abc_wrapper | Wrapper to allow EasyABC functions to run using Ensemble
generate_emulators_and_ensemble | Generate a set of emulators and combine into an ensemble
generate_ensemble_from_existing_emulations | Generate an ensemble from previously created spartan emulation objects
generate_ensemble_training_set | Internal function used to combine test set predictions from emulators to form the ensemble training set
generate_requested_emulations | Generate emulators for specified machine learning techniques with provided data
graph_Posteriors_All_Parameters | Graph posterior distributions generated for all parameters, to PDF file
kfoldCrossValidation | Perform k-fold cross validation for assessing neural network structure performance
lhc_calculatePRCCForMultipleTimepoints | Calculates the PRCC for each parameter at each timepoint, storeing PRCC and P-Value in two different files to make the plot function easier
lhc_countSignificantParametersOverTime | Count number of significant (p<0.01) parameters over a timecourse
lhc_generateLHCSummary | Summarises simulation behaviour for each parameter set, by median of distribution of replicate runs
lhc_generatePRCoEffs | Generate Partial Rank Correlation Coefficients for parameter/response pairs
lhc_generateTimepointFiles | Generates spartan-compatible timepoint files if simulation results over time are in one file
lhc_generate_lhc_sample | Generates sets of simulation parameters using latin-hypercube sampling
lhc_generate_lhc_sample_netlogo | Prepares Netlogo experiment files for a sampling-based sensitivity analysis, using latin-hypercube sampling
lhc_generate_netlogo_PRCoEffs | Deprecated. Use 'lhc_generatePRCoEffs' instead
lhc_graphMeasuresForParameterChange | Generates parameter/measure plot for each pairing in the analysis
lhc_graphPRCCForMultipleTimepoints | Produce a plot of PRCC values obtained at multiple timepoints
lhc_netlogo_graphMeasuresForParameterChange | Deprecated. Use 'lhc_graphMeasuresForParameterChange' instead
lhc_plotCoEfficients | Plots the PRCC coefficients against each other for ease of comparison
lhc_polarplot | Creates a polar plot for each response, showing PRCC for each parameter
lhc_process_netlogo_result | Analyses Netlogo simulations generated for a latin-hypercube based sensitivity analysis
lhc_process_sample_run_subsets | Summarises results of runs for parameter sets generated by a latin-hypercube
normaliseATest | Normalises the A-Test such that it is above 0.5
normalise_dataset | Normalise a dataset such that all values are between 0 and 1
nsga2_set_user_params | Initialise analysis specific parameters for NSGA-2
num.decimals | Diagnostic function used to determine number of decimal places
oat_countResponsesOfDesiredValue | Counts the number of simulation responses where a output response equals a desired result, for a specified parameter.
oat_csv_result_file_analysis | Performs a robustness analysis for supplied simulation data, comparing simulation behaviour at different parameter values
oat_generate_netlogo_behaviour_space_XML | Creates a Netlogo compatible behaviour space experiment for robustness analysis
oat_graphATestsForSampleSize | Takes each parameter in turn and creates a plot showing A-Test score against parameter value.
oat_parameter_sampling | Create parameter samples for robustness (local) analysis
oat_plotResultDistribution | For stochastic simulations plots the distribution of results for each parameter value
oat_processParamSubsets | Summarises stochastic, repeated, simulations for all robustness parameter sets into a single file.
oat_process_netlogo_result | Takes a Netlogo behaviour space file and performs a robustness analysis from that simulation data
partition_dataset | Partition latin-hypercube summary file to training, testing, and validation
perform_aTest_for_all_sim_measures | Performs A-Test to compare all simulation measures
plotATestsFromTimepointFiles | Plots the A-Tests for all timepoints being examined
ploteFASTSiFromTimepointFiles | Plot the Si value for all parameters for multiple simulation timepoints
plotPRCCSFromTimepointFiles | Plots Graphs for Partial Rank Correlation Coefficients Over Time
plot_compare_sim_observed_to_model_prediction | Internal function used to create accuracy plots of the emulation against observed data
produce_accuracy_plots_all_measures | Internal function used to create accuracy plots of the emulation against observed data, for all measures
produce_accuracy_plots_single_measure | Internal function used to create accuracy plots of the emulation against observed data
screen_nsga2_parameters | Screens NSGA-2 related parameters, guiding which to select for evolving parameter sets
selectSuitableStructure | Selects the most suitable neural network structure from the potentials made
set.nsga_sensitivity_params | Set parameters for NSGA-2 sensitivity analysis
sim_data_for_emulation | Set of parameter and response pairs for training an emulator of a simulation
tutorial_consistency_set | Example dataset showing the structure for consistency analysis data
updateErrorForStructure | Add the MSE for a newly examined structure to the list of those already seen
use_ensemble_to_generate_predictions | Predict simulation responses for a parameter set using an ensemble
visualise_data_distribution | Used to diagnose skew in a training dataset before use in emulation
weight_emulator_predictions_by_ensemble | Internal function to weight emulator predictions by that calculated for the ensemble