analyze_nl {nlrx} | R Documentation |
Analyze NetLogo simulation output
Description
Analyze NetLogo simulation output
Usage
analyze_nl(nl, metrics = getexp(nl, "metrics"), funs = list(mean = mean))
Arguments
nl |
nl object |
metrics |
vector of strings defining metric columns for evaluation. Defaults to metrics of the experiment within the nl object |
funs |
list with the summary metrics for the sensitivity results |
Details
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:
Calls analyze_ff. The function calculates aggregated output metrics by dropping random seeds and aggregating values with the provided functions.
Calls analyze_lhs. The function calculates aggregated output metrics by dropping random seeds and aggregating values with the provided functions.
Calls analyze_sobol. The function calculates sobol sensitivity indices from the output results using the sensitivity package.
Calls analyze_sobol2007. The function calculates sobol sensitivity indices from the output results using the sensitivity package.
Calls analyze_soboljansen. The function calculates sobol sensitivity indices from the output results using the sensitivity package.
Calls analyze_morris. The function calculates morris sensitivity indices from the output results using the sensitivity package.
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
Value
analysis summary tibble
Examples
# 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)