Sensitivity analysis is conducted to check output variations when a parameter is changed. This post is to show how to conduct sensitivity analysis using rapsimng package using factorial simulations.
We use the base phyllochron as an example to demonstrate how to generate a new apsimx file from a template.
The base phyllochron is a key parameter for wheat phelonogy and leaf appearance rate. The range of base phyllochron is from 60 to 130 degree days.
The data.frame requires three columns (i.e. parameter, value, name) and multiple parameters can be specified here.
phyllochron_para <- tibble(parameter = "[Phenology].Phyllochron.BasePhyllochron.FixedValue",
value = seq(60, 130, by = 1)) %>%
mutate(name = paste0("Cultivar", seq_len(n())))
head(phyllochron_para)
#> # A tibble: 6 × 3
#> parameter value name
#> <chr> <dbl> <chr>
#> 1 [Phenology].Phyllochron.BasePhyllochron.FixedValue 60 Cultivar1
#> 2 [Phenology].Phyllochron.BasePhyllochron.FixedValue 61 Cultivar2
#> 3 [Phenology].Phyllochron.BasePhyllochron.FixedValue 62 Cultivar3
#> 4 [Phenology].Phyllochron.BasePhyllochron.FixedValue 63 Cultivar4
#> 5 [Phenology].Phyllochron.BasePhyllochron.FixedValue 64 Cultivar5
#> 6 [Phenology].Phyllochron.BasePhyllochron.FixedValue 65 Cultivar6
The template is an apsimx file setup for the actual experiment or the
specified environments (i.e. locations, sowing date or years). I assume
there is a factor Cv
for culivar in the
Permutation
model which specified the cultivar by
[Sowing].Script.CultivarName
.
template <- read_apsimx(system.file("extdata/wheat.apsimx", package = "rapsimng"))
update_cultivar
can be used to add the list of cultivars
in the Replacements
. The Specification
in the
Permutation.Cv
can be replace with new values.
template <- update_cultivar(template, phyllochron_para)
node <- search_path(template, "[Permutation].Cv")
if (length(node) == 0) {
stop("[Permutation].Cv is not found")
}
new_value <- paste0("\\1", paste(phyllochron_para$name, collapse = ","))
node$node$Specification <- gsub("(.+ *= *)(.+)$", new_value, node$node$Specification)
node$node$Specification
#> [1] "[Sowing].Script.CultivarName=Cultivar1,Cultivar2,Cultivar3,Cultivar4,Cultivar5,Cultivar6,Cultivar7,Cultivar8,Cultivar9,Cultivar10,Cultivar11,Cultivar12,Cultivar13,Cultivar14,Cultivar15,Cultivar16,Cultivar17,Cultivar18,Cultivar19,Cultivar20,Cultivar21,Cultivar22,Cultivar23,Cultivar24,Cultivar25,Cultivar26,Cultivar27,Cultivar28,Cultivar29,Cultivar30,Cultivar31,Cultivar32,Cultivar33,Cultivar34,Cultivar35,Cultivar36,Cultivar37,Cultivar38,Cultivar39,Cultivar40,Cultivar41,Cultivar42,Cultivar43,Cultivar44,Cultivar45,Cultivar46,Cultivar47,Cultivar48,Cultivar49,Cultivar50,Cultivar51,Cultivar52,Cultivar53,Cultivar54,Cultivar55,Cultivar56,Cultivar57,Cultivar58,Cultivar59,Cultivar60,Cultivar61,Cultivar62,Cultivar63,Cultivar64,Cultivar65,Cultivar66,Cultivar67,Cultivar68,Cultivar69,Cultivar70,Cultivar71"
template <- replace_model(template, node$path, node$node)
Finally the new model can be write into file system and run with
APSIM NG. Uncomment the sections below, update the path to
Models.exe
.
# write_apsimx(template, "new-path.apsimx")
# models_path <- "path-to-Models.exe"
# run_models(models_path, sim_name, csv = TRUE, parallel = FALSE)
The example in this post can be modified into parallel codes for sensivity analysis in the large scales.