R/nlrx-package.R

In nldoc/nlrx: Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'

#' nlrx: A package for running NetLogo simulations from R.
#'
#' 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.
#'
#'
#' @details
#'
#' 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.
#'
#' ```
#' library(nlrx)
#' # 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",
#'                              outpath=outpath,
#'                              repetition=1,
#'                              tickmetrics="true",
#'                              idsetup="setup",
#'                              idgo="go",
#'                              runtime=50,
#'                              evalticks=seq(40,50),
#'                              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,
#'                                samples=100,
#'                                nseeds=3,
#'                                precision=3)
#' ```
#'
#' 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 \code{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
#' write_simoutput(nl)
#' # Do further analysis:
#' analyze_nl(nl)
#' ```
#'
#'
#' @docType package
#' @name nlrx-package
#' @author Jan Salecker \email{jan.salecker@posteo.de}
#' @keywords package
# nocov start
"_PACKAGE"

globalVariables(c(
  ".",
  "[run number]",
  "[step]",
  "funs",
  "group_by",
  "metrics",
  "random-seed",
  "vars",
  "siminputrow",
  "seed",
  "maps",
  "x",
  "y",
  "X",
  "Y",
  "group",
  "var",
  "dist.missing",
  "values.missing",
  "x_coord_ind",
  "y_coord_ind",
  "patches_own",
  "patches_own_names",
  "turtle_coords",
  "value",
  "metrics.patches",
  "metrics.turtles",
  "pxcor",
  "pycor",
  "end1",
  "end2",
  "metrics.links",
  "agent",
  "today",
  "browseURL",
  "start"
))
# nocov end