Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval=FALSE---------------------------------------------------------------
# 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)
## ----eval=FALSE---------------------------------------------------------------
# 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"))
## ----eval=FALSE---------------------------------------------------------------
# nl@simdesign <- simdesign_lhs(nl=nl,
# samples=100,
# nseeds=3,
# precision=3)
## ----eval=FALSE---------------------------------------------------------------
# # Evaluate nl object:
# eval_variables_constants(nl)
# print(nl)
#
# # Run all simulations (loop over all siminputrows and simseeds)
# results <- run_nl_all(nl)
## ----eval=FALSE---------------------------------------------------------------
# # 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)
## ----eval=FALSE---------------------------------------------------------------
# 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")
#
# # Setup nl object
# nl <- nl(nlversion = "6.0.3",
# nlpath = netlogopath,
# modelpath = modelpath,
# jvmmem = 1024)
#
# # Attach experiment
# 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"))
#
# # Attach simdesign
# nl@simdesign <- simdesign_lhs(nl=nl,
# samples=100,
# nseeds=3,
# precision=3)
#
# # Evaluate nl object:
# eval_variables_constants(nl)
# print(nl)
#
# # Run all simulations (loop over all siminputrows and simseeds)
# results <- run_nl_all(nl)
#
# # 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)
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