inst/doc/optimization.R

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

## ----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-GenSA1",
#                             outpath=outpath,
#                             repetition=1,
#                             tickmetrics="false",
#                             idsetup="setup",
#                             idgo="go",
#                             runtime=50,
#                             metrics=c("(1 / count wolves)"),
#                             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_GenSA(nl,
#                                 evalcrit = 1,
#                                 nseeds = 1,
#                                 control=list(maxit = 20))

## ----eval=FALSE---------------------------------------------------------------
# results <- run_nl_dyn(nl, seed = nl@simdesign@simseeds[1])

## ----eval=FALSE---------------------------------------------------------------
# results

## ----eval=FALSE---------------------------------------------------------------
# setsim(nl, "simoutput") <- tibble::enframe(results)
# saveRDS(nl, file.path(nl@experiment@outpath, "genSA_1.rds"))
# 

## ----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-GenSA2",
#                             outpath=outpath,
#                             repetition=1,
#                             tickmetrics="false",
#                             idsetup="setup",
#                             idgo="go",
#                             runtime=50,
#                             metrics.patches = c("pxcor", "pycor", "pcolor"),
#                             variables = list('initial-number-sheep' = list(min=50, max=150),
#                                              'initial-number-wolves' = list(min=50, max=150)),
#                             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---------------------------------------------------------------
# critfun <- function(nl) {
#   library(landscapemetrics)
#   res_spat <- nl_to_raster(nl)
#   res_spat_raster <- res_spat$spatial.raster[[1]]
#   lsm <- lsm_l_ed(res_spat_raster)
#   crit <- lsm$value
#   return(crit)
# }

## ----eval=FALSE---------------------------------------------------------------
# nl@simdesign <- simdesign_GenSA(nl,
#                                 evalcrit = critfun,
#                                 nseeds = 1,
#                                 control=list(maxit = 20))

## ----eval=FALSE---------------------------------------------------------------
# results <- run_nl_dyn(nl, seed = nl@simdesign@simseeds[1])

## ----eval=FALSE---------------------------------------------------------------
# results

## ----eval=FALSE---------------------------------------------------------------
# setsim(nl, "simoutput") <- tibble::enframe(results)
# saveRDS(nl, file.path(nl@experiment@outpath, "genSA_2.rds"))
#