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Analyze NetLogo simulation output
analyze_nl(nl, metrics = getexp(nl, "metrics"), funs = list(mean = mean))analysis summary tibble
nl object
vector of strings defining metric columns for evaluation. Defaults to metrics of the experiment within the nl object
list with the summary metrics for the sensitivity results
The analyze_nl function runs basic analyses on NetLogo simulation output.
In order to execute this function, simulation output needs to be attached to the simdesign first with setsim(nl, "output") <- results.
analyze_nl calls different post-processing analysis functions, depending on the specified method in the simdesign object of the nl object.
The following simdesign are currently supported:
simdesign_ff
Calls analyze_ff. The function calculates aggregated output metrics by dropping random seeds and aggregating values with the provided functions.
simdesign_lhs
Calls analyze_lhs. The function calculates aggregated output metrics by dropping random seeds and aggregating values with the provided functions.
simdesign_sobol
Calls analyze_sobol. The function calculates sobol sensitivity indices from the output results using the sensitivity package.
simdesign_sobol2007
Calls analyze_sobol2007. The function calculates sobol sensitivity indices from the output results using the sensitivity package.
simdesign_soboljansen
Calls analyze_soboljansen. The function calculates sobol sensitivity indices from the output results using the sensitivity package.
simdesign_morris
Calls analyze_morris. The function calculates morris sensitivity indices from the output results using the sensitivity package.
simdesign_eFast
Calls analyze_eFast. The function calculates eFast sensitivity indices from the output results using the sensitivity package.
For the following simdesign no postprocessing analysis function has been implemented yet:
simdesign_simple, simdesign_distinct, simdesign_GenSA, simdesign_GenAlg
# Load nl object including output data from testdata
nl <- nl_sobol
# Define aggregation measurements:
myfuns <- list(mean=mean, sd=sd, min=min, max=max)
# Calculate sensitivity indices:
analyze_nl(nl, funs = myfuns)
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