library(gadget3) library(magrittr)
There are also special g3 functions that can be used in formula that affect the resulting code, rather than just being called when run.
See ?g3_param for more information.
All G3 models have the G3 global environment included, which contains the
definition of generally useful functions. For example avoid_zero(), used to
avoid div/0 conditions by adding a small amount of error close to zero.
See R/aab_env.R for more information, and other existing helpers.
Some things aren't easy to do with code translation. g3_native allows you to
define a function with separate R and C++ definitions, for example logspace_add_vec,
used in many actions to avoid div/0, has the following definition:
# vector<Type> form of logspace_add g3_env$logspace_add_vec <- g3_native(r = function(a,b) { # https://github.com/kaskr/adcomp/issues/7#issuecomment-642559660 pmax(a, b) + log1p(exp(pmin(a,b) - pmax(a, b))) }, cpp = '[](vector<Type> a, Type b) -> vector<Type> { vector<Type> res(a.size()); for(int i = 0; i < a.size(); i++) { res[i] = logspace_add(a[i], b); } return res; }')
Most actions interact with stocks, and fill out abstract formulae with the
stocks provided to the function. To do this you need to do a series of
substitutions, which are handled by g3_step(). This takes a formula,
looks for stock_* named functions and mangles the formula as appropriate.
For example, a snippet from action_mature.R.
out <- new.env(parent = emptyenv()) out[[step_id(run_at, 1, stock)]] <- g3_step(f_substitute(~{ debug_label("g3a_mature for ", stock) # Matured stock will weigh the same stock_with(stock, stock_with(matured, matured__wgt <- stock__wgt)) stock_iterate(stock, stock_intersect(matured, if (run_f) { debug_label("Move matured ", stock, " into temporary storage") stock_ss(matured__num) <- stock_ss(stock__num) * maturity_f stock_ss(stock__num) <- stock_ss(stock__num) - stock_ss(matured__num) })) }, list(run_f = run_f, maturity_f = maturity_f)))
Assume that stock has name "ling_imm" and matured has name "ling_imm_maturing".
The first line uses debug_label() to produce a debug_label() function
call, debug_label("g3a_mature for ling_imm"). This will be used as a code
comment and a label for this block if producing diagrams.
Next stock_with() is used to to transform matured__wgt <- stock__wgt
to use the proper stock names. We don't care about dimensions since we're
copying over all the data.
Finally, we use a combination of stock_iterate() and stock_intersect().
stock_iterate() will create a loop that loops over all of the stock's
dimensions, and stock_ss() will subset stock__num, prividing
1-dimension lengthgroup vector. stock_intersect().
These iterators will then be available to the maturity_f that the users
provide, as demonstrated in the [Stocks] section.
For more information on the stock_* functions, see ?stock_ss.
Obviously R and C++'s type systems are quite different, and gadget3 attempts to bridge the gap.
In R, there is no difference between a scalar and a 1-element vector. If you
expect to treat the variable as a vector or array, then state this explicitly
with array, even if the result may be a 1-element vector. This means that
methods that work on TMB array or vector classes will be available.
One needs to be more careful with the type of constants in C++ than R. In
particular, x / 2 means integer division. As a result, G3 will cast any
numeric constant as a double outside of certain situations, e.g. array indices,
which will be integer values. However, if you do want an integer it's best to
express this explictly, i.e. 3L. Note that there is no difference in R code
between 3 and 3.0.
R forumlas all you to include extra definitions when defining a formula. This allows you to break up a definition into more readable chunks. For example:
ling_imm <- g3_stock('ling_imm', seq(0, 50, 10)) %>% g3s_age(3, 10) nmort <- function() { E <- ~stock__minlen * age F <- ~stock__minage # TODO: Does this work now? ~E * F * 4 } g3_to_r(list(g3a_naturalmortality(ling_imm, nmort())))
Note that:
E refers to age, gadget3 has automatically inserted it into the loop.F however can be defined outside the loop, so is.If you need to have something available to ther steps, it can be defined using
g3_global_formula and providing an init_val:
ling_imm <- g3_stock('ling_imm', seq(0, 50, 10)) %>% g3s_age(3, 10) nmort <- function() { # Define a counter E <- g3_global_formula( ~E + 1, init_val = 0L) # We can just give init_val, to define something global to the model F <- g3_global_formula( init_val = 99L) ~E * F * 4 } g3_to_r(list(g3a_naturalmortality(ling_imm, nmort())))
As well as making values available to other steps, g3_global_formula()
can also be used to ensure that the value ends up in the model report, which will
happen automatically for any non-constant global in the model.
There are limits to what the Gadget3 transpilation can manage, particularly
once you need to operate on matrices. To get around this, you can define
functions using g3_native, which annotates functions with a C++ definition.
For example, it is used to define logspace_add_vec:
# vector<Type> form of logspace_add g3_env$logspace_add_vec <- g3_native(r = function(a,b) { pmax(a, b) + log1p(exp(pmin(a,b) - pmax(a, b))) }, cpp = '[](vector<Type> a, Type b) -> vector<Type> { vector<Type> res(a.size()); for(int i = 0; i < a.size(); i++) { res[i] = logspace_add(a[i], b); } // NB: This is a string within R code, so we have to escape slashes. // For example, "\\n" return res; }')
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