Skip to contents

The nlrx package provides tools to setup NetLogo simulations in R. It uses a similar structure as NetLogos Behavior Space but offers more flexibility and additional tools for running sensitivity analyses.


Get started:

General information that is needed to run NetLogo simulations remotely, such as path to the NetLogo installation folder is stored within a nl class object. Nested within this nl class are the classes experiment and simdesign. The experiment class stores all experiment specifications. After attaching a valid experiment, a simdesign class object can be attached to the nl class object, by using one of the simdesign helper functions. These helper functions create different parameter input matrices from the experiment variable definitions that can then be executed by the run_nl_one() and run_nl_all() functions. The nested design allows to store everything related to the experiment within one R object. Additionally, different simdesign helper functions can be applied to the same nl object in order to repeat the same experiment with different parameter exploration methods (simdesigns).

Step by step application example

The "Wolf Sheep Predation" model from the NetLogo models library is used to present a basic example on how to setup and run NetLogo model simulations from R.

Step 1: Create a nl object:

The nl object holds all information on the NetLogo version, a path to the NetLogo directory with the defined version, a path to the model file, and the desired memory for the java virtual machine. Depending on the operation system, paths to NetLogo and the model need to be adjusted.

# Windows default NetLogo installation path (adjust to your needs!):
netlogopath <- file.path("C:/Program Files/NetLogo 6.0.3")
modelpath <- file.path(netlogopath, "app/models/Sample Models/Biology/Wolf Sheep Predation.nlogo")
outpath <- file.path("C:/out")
# Unix default NetLogo installation path (adjust to your needs!):
netlogopath <- file.path("/home/NetLogo 6.0.3")
modelpath <- file.path(netlogopath, "app/models/Sample Models/Biology/Wolf Sheep Predation.nlogo")
outpath <- file.path("/home/out")
nl <- nl(nlversion = "6.0.3",
         nlpath = netlogopath,
         modelpath = modelpath,
         jvmmem = 1024)

Step 2: Attach an experiment

The experiment object is organized in a similar fashion as NetLogo Behavior Space experiments. It contains all information that is needed to generate a simulation parameter matrix and to execute the NetLogo simulations. Details on the specific slots of the experiment class can be found in the package documentation (?experiment) and the "Advanced configuration" vignette.

nl@experiment <- experiment(expname="wolf-sheep",
                             metrics=c("count sheep", "count wolves", "count patches with [pcolor = green]"),
                             variables = list('initial-number-sheep' = list(min=50, max=150, qfun="qunif"),
                                              'initial-number-wolves' = list(min=50, max=150, qfun="qunif")),
                             constants = list("model-version" = "\"sheep-wolves-grass\"",
                                              "grass-regrowth-time" = 30,
                                              "sheep-gain-from-food" = 4,
                                              "wolf-gain-from-food" = 20,
                                              "sheep-reproduce" = 4,
                                              "wolf-reproduce" = 5,
                                              "show-energy?" = "false"))

Step 3: Attach a simulation design

While the experiment defines the variables and specifications of the model, the simulation design creates a parameter input table based on these model specifications and the chosen simulation design method. nlrx provides a bunch of different simulation designs, such as full-factorial, latin-hypercube, sobol, morris and eFast (see "Simdesign Examples" vignette for more information on simdesigns). All simdesign helper functions need a properly defined nl object with a valid experiment design. Each simdesign helper also allows to define a number of random seeds that are randomly generated and can be used to execute repeated simulations of the same parameter matrix with different random-seeds (see "Advanced configuration" vignette for more information on random-seed and repetition management). A simulation design is attached to a nl object by using one of the simdesign helper functions:

nl@simdesign <- simdesign_lhs(nl=nl,

Step 4: Run simulations

All information that is needed to run the simulations is now stored within the nl object. The run_nl_one() function allows to run one specific simulation from the siminput parameter table. The run_nl_all() function runs a loop over all simseeds and rows of the parameter input table siminput. The loops are constructed in a way that allows easy parallelisation, either locally or on remote HPC machines (see "Advanced configuration" vignette for more information on parallelisation).

results <- run_nl_all(nl = nl)

Step 5: Attach results to nl and run analysis

nlrx provides method specific analysis functions for each simulation design. Depending on the chosen design, the function reports a tibble with aggregated results or sensitivity indices. In order to run the analyze_nl() function, the simulation output has to be attached to the nl object first. After attaching the simulation results, these can also be written to the defined outpath of the experiment object.

# Attach results to nl object:
setsim(nl, "simoutput") <- results
# Write output to outpath of experiment within nl
# Do further analysis:


Jan Salecker