Gadget3 actions into R code — run

Convert g3 actions into an R function that can then be executed

g3_to_r(
        actions,
        work_dir = getOption('gadget3.r.work_dir', default = tempdir()),
        trace = FALSE,
        strict = FALSE,
        cmp_options = list(optimize = 3) )

# S3 method for class 'g3_r'
print(x, ..., with_environment = FALSE, with_template = FALSE)

Arguments

actions: A list of actions (i.e. list of formula objects), as produced by g3a_* functions.
work_dir: Where to write the temporary R script containing your function
cmp_options: options to pass through to compiler::cmpfun(). If NULL, then don't run the model through the byte-code compiler
trace: If TRUE, turn all comments into print statements.
strict: If TRUE, enable extra sanity checking in actions. Any invalid conditions (e.g. more/less fish after growth) will result in a warning.
x: The g3_to_r-generated function to print
with_environment: If TRUE, list data stored in function environment when printing
with_template: If TRUE, show parameter template when printing
...: Other arguments

Value

A function that takes a params variable, that defines all g3_params required by the model. The following attributes will be set:

actions: The original actions list given to the function
parameter_template: A list of all parameters expected by the model, to fill in

Use e.g. attr(fn, 'parameter_template') to retrieve them.

Invariant model data will be stored as a closure, i.e. in environment(fn). This can be fetched with environment(fn)$cdist_sumofsquares_ldist_gil_obs__num.

The function will return nll produced by the model. You can also use attributes(nll) to get any report variables from the model.

Examples

ling_imm <- g3_stock(c(species = 'ling', 'imm'), seq(20, 156, 4)) %>% g3s_age(3, 10)

initialconditions_action <- g3a_initialconditions_normalparam(
    ling_imm,
    factor_f = g3a_renewal_initabund(by_stock_f = 'species'),
    by_stock = 'species',
    by_age = TRUE)

# Timekeeping action
time_action <- g3a_time(
    start_year = 2000,
    end_year = 2004,
    c(3, 3, 3, 3))

# Generate a model from the above 2 actions
# NB: Obviously in reality we'd need more actions
fn <- g3_to_r(list(initialconditions_action, time_action))

if (interactive()) {
  # Edit the resulting function
  fn <- edit(fn)
}

param <- attr(fn, 'parameter_template')
param$project_years <- 0
param$ling.init.F <- 0.4
param$ling.Linf <- 160
param$ling.K <- 90
param$ling.recl <- 12
param$recage <- g3_stock_def(ling_imm, 'minage')
param[grepl('^ling.init.sd.', names(param))] <- 50.527220
param[grepl('^ling_imm.init.\\d+', names(param))] <- 1
param$ling_imm.init.scalar <- 200
param$ling_imm.walpha <- 2.27567436711055e-06
param$ling_imm.wbeta <- 3.20200445996187
param$ling_imm.M <- 0.15

# Run the model with the provided parameters
nll <- fn(param)

# Get the report from the last model run
report <- attributes(nll)

# Fetch a value from the model data
environment(fn)$ling_imm__midlen
#>   20:24   24:28   28:32   32:36   36:40   40:44   44:48   48:52   52:56   56:60 
#>      22      26      30      34      38      42      46      50      54      58 
#>   60:64   64:68   68:72   72:76   76:80   80:84   84:88   88:92   92:96  96:100 
#>      62      66      70      74      78      82      86      90      94      98 
#> 100:104 104:108 108:112 112:116 116:120 120:124 124:128 128:132 132:136 136:140 
#>     102     106     110     114     118     122     126     130     134     138 
#> 140:144 144:148 148:152 152:156 156:Inf 
#>     142     146     150     154     158