Simulation Parameter Analysis R Toolkit ApplicatioN: Spartan

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Documentation for package ‘spartan’ version 3.0.0

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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