## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
library(apsimx)
library(ggplot2)
apsimx_options(warn.versions = FALSE)
tmp.dir <- tempdir()

## ----sens0, eval = FALSE------------------------------------------------------
#  ## Create a temporary directory and copy the Wheat.apsimx file
#  tmp.dir <- tempdir()
#  extd.dir <- system.file("extdata", package = "apsimx")
#  file.copy(file.path(extd.dir, "Wheat.apsimx"), tmp.dir)
#  ## Identify a parameter of interest
#  ## In this case we want to know the impact of varying the fertilizer amount
#  pp <- inspect_apsimx("Wheat.apsimx", src.dir = tmp.dir,
#                        node = "Manager", parm = list("SowingFertiliser", 1))
#  ## For simplicity, we create a vector of fertilizer amounts (instead of sampling)
#  ferts <- seq(5, 200, length.out = 7)
#  col.res <- NULL
#  for(i in seq_along(ferts)){
#  
#    edit_apsimx("Wheat.apsimx", src.dir = tmp.dir,
#                node = "Other",
#                parm.path = pp, parm = "Amount",
#                value = ferts[i])
#  
#    sim <- apsimx("Wheat.apsimx", src.dir = tmp.dir)
#    col.res <- rbind(col.res, data.frame(fertilizer.amount = ferts[i],
#                                         wheat.aboveground.wt = mean(sim$Wheat.AboveGround.Wt, na.rm = TRUE)))
#  }

## ----sens_apsimx, eval = FALSE------------------------------------------------
#  tmp.dir <- tempdir()
#  extd.dir <- system.file("extdata", package = "apsimx")
#  file.copy(file.path(extd.dir, "Wheat.apsimx"), tmp.dir)
#  ## Identify a parameter of interest
#  ## In this case we want to know the impact of varying the fertilizer amount
#  ## and the plant population
#  pp1 <- inspect_apsimx("Wheat.apsimx", src.dir = tmp.dir,
#                        node = "Manager", parm = list("SowingFertiliser", 1))
#  pp1 <- paste0(pp1, ".Amount")
#  
#  pp2 <- inspect_apsimx("Wheat.apsimx", src.dir = tmp.dir,
#                        node = "Manager", parm = list("SowingRule1", 9))
#  pp2 <- paste0(pp2, ".Population")
#  
#  ## The names in the grid should (partially) match the parameter path names
#  grd <- expand.grid(Fertiliser = c(50, 100, 150), Population = c(100, 200, 300))
#  
#  ## This takes 2-3 minutes
#  sns <- sens_apsimx("Wheat.apsimx", src.dir = tmp.dir,
#                      parm.paths = c(pp1, pp2),
#                      grid = grd)

## ----sensitivity-parameterSets------------------------------------------------
library(sensitivity)
## Simple example: see documentation for other options
X.grid <- parameterSets(par.ranges = list(Fertiliser = c(1, 300), 
                                          Population = c(1, 300)),
                        samples = c(3,3), method = "grid")
X.grid

## ----sensitivity-morris-------------------------------------------------------
X.mrrs <- morris(factors = c("Fertiliser", "Population"),
                   r = 3, design = list(type = "oat", levels = 3, grid.jump = 1),
                   binf = c(0, 5), bsup = c(200, 300))
X.mrrs$X

## ----sensitivity-decoupling, eval = FALSE-------------------------------------
#  ## This takes 2-3 minutes
#  sns2 <- sens_apsimx("Wheat.apsimx", src.dir = tmp.dir,
#                       parm.paths = c(pp1, pp2),
#                       grid = X.mrrs$X)
#  ## These are the sensitivity results for AboveGround.Wt only
#  sns.res.ag <- tell(X.mrrs, sns2$grid.sims$Wheat.AboveGround.Wt)
#  sns.res.ag
#  ## Call: morris(factors = c("Fertiliser", "Population"),
#  ## r = 3, design = list(type = "oat", levels = 3, grid.jump = 1),
#  ## binf = c(0, 5), bsup = c(200, 300))
#  ##
#  ## Model runs: 9
#  ##                 mu  mu.star    sigma
#  ## Fertiliser 916.6674 916.6674 662.8798
#  ## Population 448.4530 448.4530 640.6807
#  plot(sns.res.ag)