Introduction to gadget3: A single stock model

This vignette walks through a script that will generate a gadget3 model, explaining concepts along the way.

Creating a (single species) model

As opposed gadget2 and other modelling frameworks, there is no input data format. Instead, the model configuration is written as an R script. This document will walk through the parts of a model script for a single-species model, introducing concepts along the way.

The first step in any script is to load gadget3. We will also use dplyr when formatting input data:

library(gadget3)
library(dplyr)

Actions

A gadget3 model is defined as a list of actions. Actions are snippets of code that define processes in a model.

To start with, we will add the g3a_time() to our list of actions:

actions <- list()

# Create time definitions ####################

actions_time <- list(
  g3a_time(
    1979L, 2023L,
    step_lengths = c(3L, 3L, 3L, 3L)),
  NULL)

actions <- c(actions, actions_time)

This acts as timekeeping for our model, starting in year 1979 and progressing until 2023. Each year will have 4 time steps in, of equal length.

As a convention, we build up an actions array of everything required, allowing sections to be added/removed as necessary.

Ultimately, this list will be converted to either R or TMB code with with g3_to_r() or g3_to_tmb() respectively. We can try this already with our time action, and generate a function that will count years & steps:

g3_to_r(actions_time)

function (param) 
{
    stopifnot("retro_years" %in% names(param))
    assert_msg <- function(expr, message) {
        if (isFALSE(expr)) {
            warning(message)
            return(TRUE)
        }
        return(FALSE)
    }
    cur_time <- -1L
    stopifnot("project_years" %in% names(param))
    project_years <- param[["project_years"]]
    end_year <- 2023L
    retro_years <- param[["retro_years"]]
    start_year <- 1979L
    total_steps <- length(step_lengths) * (end_year - retro_years - 
        start_year + project_years) + length(step_lengths) - 
        1L
    nll <- 0
    cur_year <- 0L
    step_count <- length(step_lengths)
    cur_year_projection <- FALSE
    cur_step <- 0L
    cur_step_final <- FALSE
    while (TRUE) {
        cur_time <- cur_time + 1L
        {
            comment("g3a_time: Start of time period")
            if (cur_time == 0 && assert_msg(param[["retro_years"]] >= 
                0, "retro_years must be >= 0")) 
                return(NaN)
            if (cur_time == 0 && assert_msg(project_years >= 
                0, "project_years must be >= 0")) 
                return(NaN)
            if (cur_time > total_steps) 
                return(nll)
            cur_year <- start_year + (cur_time%/%step_count)
            cur_year_projection <- cur_year > end_year - param[["retro_years"]]
            cur_step <- (cur_time%%step_count) + 1L
            cur_step_final <- cur_step == step_count
        }
    }
}
<bytecode: 0x55c15d658c70>
<environment: 0x55c15c291080>

g3_to_tmb(actions_time)

#include <TMB.hpp>



template<class Type>
Type objective_function<Type>::operator() () {
    DATA_SCALAR(reporting_enabled); DATA_UPDATE(reporting_enabled);
    PARAMETER(retro_years);
    auto assert_msg = [](bool expr, std::string message) -> bool {
    if (!expr) { Rf_warning(message.c_str()); return TRUE; }
    return FALSE;
};
    int cur_time = -1;
    PARAMETER(project_years);
    vector<int> step_lengths(4); step_lengths.setConstant(3);
    int end_year = 2023;
    int start_year = 1979;
    auto total_steps = (step_lengths).size()*(end_year - retro_years - start_year + project_years) + (step_lengths).size() - 1;
    Type nll = (double)(0);
    int cur_year = 0;
    auto step_count = (step_lengths).size();
    int cur_year_projection = false;
    int cur_step = 0;
    int cur_step_final = false;

    while (true) {
        cur_time += 1;
        {
            // g3a_time: Start of time period;
            if ( cur_time == 0 && assert_msg(retro_years >= (double)(0), "retro_years must be >= 0") ) {
                return NAN;
            }
            if ( cur_time == 0 && assert_msg(project_years >= (double)(0), "project_years must be >= 0") ) {
                return NAN;
            }
            if ( cur_time > total_steps ) {
                return nll;
            }
            cur_year = start_year + (((int) cur_time) / ((int) step_count));
            cur_year_projection = cur_year > end_year - retro_years;
            cur_step = (cur_time % step_count) + 1;
            cur_step_final = cur_step == step_count;
        }
    }
}

Stocks

After actions, the other key concept in a gadget3 model is a stock. These are the means to describe populations within your model. For simpler scenarios such as here, stocks map directly to a species. However, more complicated models may have one stock per-maturation-stage, sex or both.

We define a stock with the g3_stock() and associated g3s_* functions, for example:

area_names <- g3_areas(c('IXa', 'IXb'))

# Create stock definition for fish ####################
fish <- g3_stock("fish", seq(10, 100, 10)) |>
  g3s_livesonareas(area_names["IXa"]) |>
  g3s_age(1L, 5L)

Here we define a stock called “fish” with length bins 10..100, then add an area to live in & 5 age bins.

Ultimately, the stock functions define the structure of the arrays that will hold the state of that stock within gadget3. We can use g3_stock_instance() to see an example of the array used:

# aperm() re-orders dimensions for more compact printing
aperm(g3_stock_instance(fish, 0), c(1,3,2))

## , , area = IXa
## 
##          age
## length    age1 age2 age3 age4 age5
##   10:20      0    0    0    0    0
##   20:30      0    0    0    0    0
##   30:40      0    0    0    0    0
##   40:50      0    0    0    0    0
##   50:60      0    0    0    0    0
##   60:70      0    0    0    0    0
##   70:80      0    0    0    0    0
##   80:90      0    0    0    0    0
##   90:100     0    0    0    0    0
##   100:Inf    0    0    0    0    0

For example, the abundance and mean weight of the stock will be stored in one of these arrays within the model.

Stock actions

Now we have a stock, we can add apply population dynamics actions, and save them in the actions array from earlier:

actions_fish <- list(
  g3a_growmature(fish, g3a_grow_impl_bbinom(
    maxlengthgroupgrowth = 4L)),
  g3a_naturalmortality(fish),
  g3a_initialconditions_normalcv(fish),
  g3a_renewal_normalparam(fish,
    run_step = 2),
  g3a_age(fish),
  NULL)

actions_likelihood_fish <- list(
  g3l_understocking(list(fish), nll_breakdown = TRUE),
  NULL)

actions <- c(actions, actions_fish, actions_likelihood_fish)

Each of these g3a_* actions will have a 1:1 parallel with gadget2 stockfile components, so if you are familiar with these config files will do what you expect.

For each action you can click through to the reference to get more information on what it does, but in summary we have defined:

g3a_growmature(): The growth model
g3a_naturalmortality(): Natural mortality of our stock
g3a_initialconditions_normalcv(): Initial recruitment, defining numbers & mean weights for the start of the model
g3a_renewal_normalparam(): Recruitment occuring every spring (run_step = 2), independent of stock status
g3a_age(): Move fish through age groups at the end of a year
g3l_understocking(): A penalty applied to the likelihood used to prevent more fish being eaten/fished than is available.

There are more actions available besides these, for instance g3a_spawn() can be used for recruitment dependent on stock size instead of g3a_renewal_normalparam(). For a full list, see ??gadget3::"G3 action" or the package reference index.

Likelihood actions are actions that will sum their output into the model’s overall likelihood score, analogous to gadget2’s likelihood components.

The order of these actions as we have defined them is not preserved. When a model runs, the steps will not happen in the above order, they will be re-ordered to match the standard action order, see ?g3_action_order.

Model parameters

The definition above should look quite barren, bar maxlengthgroupgrowth we have not provided any figures for the stock dynamics.

The defaults for all actions define model parameters that can be set as fixed values or optimised later, rather than baking them into the model.

For instance, we can see that g3a_naturalmortality() creates a parameter for M by default:

head(g3a_naturalmortality)

##                                                                            
## 1 function (stock, mortality_f = g3a_naturalmortality_exp(), run_f = TRUE, 
## 2     run_at = g3_action_order$naturalmortality)                           
## 3 {                                                                        
## 4     out <- new.env(parent = emptyenv())                                  
## 5     action_name <- unique_action_name()                                  
## 6     stock__num <- g3_stock_instance(stock, 0)

head(g3a_naturalmortality_exp)

##                                                                                   
## 1 function (param_f = g3_parameterized("M", by_stock = by_stock,                  
## 2     by_age = TRUE), by_stock = TRUE, action_step_size_f = ~cur_step_size)       
## 3 {                                                                               
## 4     f_substitute(~exp(-(param_f) * action_step_size_f), list(param_f = param_f, 
## 5         action_step_size_f = action_step_size_f))                               
## 6 }

Without any arguments, we use g3a_naturalmortality_exp(), which sets M to be g3_parameterized("M", by_stock = TRUE, by_age = TRUE). This tells gadget3 that a parameter M should be expected by the model, that will both be broken down by stock (i.e. will include the name of our stock), and each age within that stock.

We can define a model with just g3a_time() and g3a_initialconditions_normalcv() to see the end result. To be able to run the function you need to provide a list of parameter values, the format of this list is defined by the attached parameter template:

simple_actions <- list(
    g3a_time(1990, 1991),
    g3a_initialconditions_normalcv(fish))
simple_fn <- g3_to_r(c(simple_actions, list(
    g3a_report_detail(simple_actions) )))

params <- attr(simple_fn, 'parameter_template')
unlist(params)

## fish.init.scalar      fish.init.1      fish.init.2      fish.init.3 
##              1.0              1.0              1.0              1.0 
##      fish.init.4      fish.init.5         fish.M.1         fish.M.2 
##              1.0              1.0              0.0              0.0 
##         fish.M.3         fish.M.4         fish.M.5           init.F 
##              0.0              0.0              0.0              0.0 
##           recage        fish.Linf           fish.K          fish.t0 
##              0.0              1.0              1.0              0.0 
##       fish.lencv      fish.walpha       fish.wbeta    report_detail 
##              0.1              0.0              0.0              1.0 
##      retro_years    project_years 
##              0.0              0.0

g3a_report_detail() adds standard reporting to our model, we will cover it’s use later.

We can fill in these values and run the model:

params$fish.init.scalar <- 10
params$fish.init.1 <- 10
params$fish.init.2 <- 10
params$fish.init.3 <- 10
params$fish.init.4 <- 10
params$fish.init.5 <- 10
params$fish.M.1 <- 0.15
params$fish.M.2 <- 0.15
params$fish.M.3 <- 0.15
params$fish.M.4 <- 0.15
params$fish.M.5 <- 0.15
params$init.F <- 0.5
params$recage <- 0
params$fish.Linf <- max(g3_stock_def(fish, "midlen"))
params$fish.K <- 0.3
params$fish.t0 <- g3_stock_def(fish, "minage") - 0.8
params$fish.lencv <- 0.1
params$report_detail <- 1

# Run model and pull out final abundance from the result
abund <- attr(simple_fn(params), "detail_fish__num")[,area = 'IXa', , time = '1990-01']

par(mfrow=c(3, 2), mar = c(2,2,1,0))
for (a in dimnames(abund)$age) barplot(abund[, age = a], main = a)

Altering K results in corresponding changes to the stock structure:

params$fish.K <- 0.9
abund <- attr(simple_fn(params), "detail_fish__num")[,area = 'IXa', , time = '1990-01']
par(mfrow=c(3, 2), mar = c(2,2,1,0))
for (a in dimnames(abund)$age) barplot(abund[, age = a], main = a)

Fleet actions

Fleets in gadget3 are modelled as stock objects, which predate on their target stocks.

To define a fleet, we need to introduce historical data into the model. In our case we will generate random data, but the aggretation steps would apply regardless.

Landings data

# Fleet data for f_surv #################################

# Landings data: For each year/step/area
expand.grid(year = 1990:1994, step = 2, area = 'IXa') |>
    # Generate a random total landings by weight
    mutate(weight = rnorm(n(), mean = 1000, sd = 100)) |>
    # Assign result to landings_f_surv
    identity() -> landings_f_surv

Here we use expand.grid() to generate a data.frame() with all possible year/step/area cominations. We then use dplyr::mutate() to add a weight column to this table, using rnorm() to generate random numbers distributed about a mean.

The identity() function is a do-nothing function that passes through the input. We use this to move the assignment onto it’s own line.

The end result is a data.frame() of total biomass figures:

landings_f_surv

##   year step area    weight
## 1 1990    2  IXa  943.9524
## 2 1991    2  IXa  976.9823
## 3 1992    2  IXa 1155.8708
## 4 1993    2  IXa 1007.0508
## 5 1994    2  IXa 1012.9288

plot(landings_f_surv[c('year', 'weight')], ylim = c(0, 2000), col = "red")

Note that we haven’t provided data for all years/steps, as we’ll assume this fleet only works in spring. For more information on how this works, see vignette("incorporating-observation-data").

Length distribution data

Next we generate some length-distribution data:

# Length distribution data: Randomly generate 100 samples in each year/step/area
expand.grid(year = 1990:1994, step = 2, area = 'IXa', length = rep(NA, 100)) |>
  # Generate random lengths for these samples
  mutate(length = rnorm(n(), mean = 50, sd = 20)) |>
  # Save unagggregated data into ldist_f_surv.raw
  identity() -> ldist_f_surv.raw

# Aggregate .raw data
ldist_f_surv.raw |>
  # Group into length bins
  group_by(
      year = year,
      step = step,
      length = cut(length, breaks = c(seq(0, 80, 20), Inf), right = FALSE) ) |>
  # Report count in each length bin
  summarise(number = n(), .groups = 'keep') |>
  # Save into ldist_f_surv
  identity() -> ldist_f_surv

As before, expand.grid() and mutate() generate a table with random lengths distributed about the mean. This is our unaggregated data, which we save using assign() so we can see the end result:

head(ldist_f_surv.raw)

##   year step area   length
## 1 1990    2  IXa 84.30130
## 2 1991    2  IXa 59.21832
## 3 1992    2  IXa 24.69878
## 4 1993    2  IXa 36.26294
## 5 1994    2  IXa 41.08676
## 6 1990    2  IXa 74.48164

We next use group_by() and cut() to aggregate by year, step & length bins. cut() is responsible for binning continuous data. We can see what it does by running for single values:

cut(c(50), breaks = c(seq(0, 80, 20), Inf), right = FALSE)

## [1] [40,60)
## Levels: [0,20) [20,40) [40,60) [60,80) [80,Inf)

Note that we add Inf to the end of the list of breaks, to create a plus-group. We also specify right = FALSE so that the groups are closed on the left.

Also note that our breaks aren’t the same as our stock definition, this is allowed and gadget3 will re-aggregate the model data to match. For more information on how this works, see vignette("incorporating-observation-data").

Finally, summarise counts the number in each group and puts the result into a number column. The end result looks like:

summary(ldist_f_surv)

##       year           step        length      number     
##  Min.   :1990   Min.   :2   [0,20)  :5   Min.   : 2.00  
##  1st Qu.:1991   1st Qu.:2   [20,40) :5   1st Qu.: 6.00  
##  Median :1992   Median :2   [40,60) :5   Median :19.50  
##  Mean   :1992   Mean   :2   [60,80) :5   Mean   :19.23  
##  3rd Qu.:1993   3rd Qu.:2   [80,Inf):5   3rd Qu.:26.75  
##  Max.   :1994   Max.   :2   NA's    :1   Max.   :46.00

years <- unique(ldist_f_surv$year)
par(mfrow=c(2, ceiling(length(years) / 2)))
for (y in years) plot(as.data.frame(ldist_f_surv) |>
    filter(year == y & step == 2) |>
    select(length, number), main = y, ylim = c(0, 60))

Age-length distribution data

Finally, we can apply the same techniques to generate and aggregate age-length data:

# Assume 5 * 5 samples in each year/step/area
expand.grid(year = 1990:1994, step = 2, area = 'IXa', age = rep(NA, 5), length = rep(NA, 5)) |>
  # Generate random lengths/ages for these samples
  mutate(length = rnorm(n(), mean = 50, sd = 20)) |>
  # Generate random whole numbers for age
  mutate(age = floor(runif(n(), min = 1, max = 5))) |>
  # Group into length/age bins
  group_by(
      year = year,
      step = step,
      age = age,
      length = cut(length, breaks = c(seq(0, 80, 20), Inf), right = FALSE) ) |>
  # Report count in each length bin
  summarise(number = n(), .groups = 'keep') ->
  aldist_f_surv

The end result is a data.frame() with year/step/age/length/number:

summary(aldist_f_surv)

##       year           step        age             length       number     
##  Min.   :1990   Min.   :2   Min.   :1.000   [0,20)  : 3   Min.   :1.000  
##  1st Qu.:1991   1st Qu.:2   1st Qu.:1.250   [20,40) :14   1st Qu.:1.000  
##  Median :1992   Median :2   Median :3.000   [40,60) :19   Median :2.000  
##  Mean   :1992   Mean   :2   Mean   :2.552   [60,80) :15   Mean   :2.155  
##  3rd Qu.:1993   3rd Qu.:2   3rd Qu.:3.750   [80,Inf): 5   3rd Qu.:3.000  
##  Max.   :1994   Max.   :2   Max.   :4.000   NA's    : 2   Max.   :6.000

years <- unique(aldist_f_surv$year)
ages <- unique(aldist_f_surv$age)

par(mfrow=c(length(years), length(ages)), mar = c(2,2,1,0))
for (y in years) for (a in ages) plot(as.data.frame(ldist_f_surv) |>
    filter(year == y & step == 2) |>
    select(length, number), main = sprintf("year = %d, age = %s", y, a), ylim = c(0, 60))

Fleet definition

A fleet, f_surv, is defined in much the same way as our stock above, however with a different set of actions:

# Create fleet definition for f_surv ####################
f_surv <- g3_fleet("f_surv") |> g3s_livesonareas(area_names["IXa"])

We define the stock with g3_fleet() instead of g3_stock(), as a fleet isn’t divided into length or age bins. Simiarly, g3s_age() to divide into age bins isn’t relevant.

actions_f_surv <- list(
  g3a_predate_fleet(
    f_surv,
    list(fish),
    suitabilities = g3_suitability_exponentiall50(),
    catchability_f = g3a_predate_catchability_totalfleet(
      g3_timeareadata("landings_f_surv", landings_f_surv, "weight", areas = area_names))),
  NULL)
actions <- c(actions, actions_f_surv)

The only action of f_surv is to predate fish. We define this with g3a_predate_fleet(), setting:

suitabilities: This defines a predator’s preference for stocks. In this case we use g3_suitability_exponentiall50() a logarithmic dependence on the difference between length of individuals to \(l_{50}\), the length of prey with a 50% probability of predation
catchability_f: This controls a predator’s catch/effort. In this case we use g3a_predate_catchability_totalfleet() to define effort based on total biomass caught, and g3_timeareadata() to provide a timeseries table of landings data generated above

For other possible settings, follow the links to the function definitions.

Finally we define likelihood actions using g3l_catchdistribution(), to compare modelled catch against our length & age-length distribution data generated above.

actions_likelihood_f_surv <- list(
  g3l_catchdistribution(
    "ldist_f_surv",
    obs_data = ldist_f_surv,
    fleets = list(f_surv),
    stocks = list(fish),
    function_f = g3l_distribution_sumofsquares(),
    area_group = area_names,
    report = TRUE,
    nll_breakdown = TRUE),
  g3l_catchdistribution(
    "aldist_f_surv",
    obs_data = aldist_f_surv,
    fleets = list(f_surv),
    stocks = list(fish),
    function_f = g3l_distribution_sumofsquares(),
    area_group = area_names,
    report = TRUE,
    nll_breakdown = TRUE),
  NULL)
actions <- c(actions, actions_likelihood_f_surv)

Note that the only difference between the 2 likelihood actions is the structure of the inputted data. aldist_f_surv unlike ldist_f_surv has an age column, gadget3 will detect this and group the modelled catch accordingly. Similarly neither data.frame has the full range of years, so comparisons will be made outside those ranges. For more detail on what can be done here, see vignette("incorporating-observation-data").

function_f defines the method of comparison between modelled catch & observation data, once aggregation has been done. g3l_distribution_sumofsquares() in this case compares the sum of squared difference. For more options on what to use here, follow the links to the reference.

To add futher fleets to your model, just repeat the same code with a different fleet name.

Survey indices

Measures of abudnance, such as commercial CPUE data, can be added as observation data by adding g3l_abundancedistribution() likelihood actions:

# Create abundance index for si_cpue ########################

# Generate random data
expand.grid(year = 1990:1994, step = 3, area = 'IXa') |>
    # Fill in a weight column with total biomass for the year/step/area combination
    mutate(weight = runif(n(), min = 10000, max = 100000)) ->
    dist_si_cpue

actions_likelihood_si_cpue <- list(

  g3l_abundancedistribution(
    "dist_si_cpue",
    dist_si_cpue,
    
    stocks = list(fish),
    function_f = g3l_distribution_surveyindices_log(alpha = NULL, beta = 1),
    area_group = area_names,
    report = TRUE,
    nll_breakdown = TRUE),
  NULL)

actions <- c(actions, actions_likelihood_si_cpue)

We create a data.frame() with year/step/area/weight columns, and input this into a likelihood action as before with our fleet.

The key differences between the catch distribution above are:

We are using g3l_abundancedistribution() instead of g3l_catchdistribution(), which compares model abundance instead of catch from a fleet.
Our observation data has a weight column instead of number. This results in us comparing total biomass, instead of number of individuals.
We use g3l_distribution_surveyindices_log() to perform linear regression to calculate likelihood score. We have fixed beta (the slope) of the regression, only alpha will be estimated. We could reverse this, or estimate both by setting to NULL.

Creating model functions and Parameterization

At this point, we are ready to convert our model into code:

# Create model objective function ####################

# Apply bounds in code - the other option would be using control = list(lower = g3_tmb_lower(params.in), ...)
model_code <- g3_to_tmb(c(actions, list(
    g3a_report_detail(actions),
    g3l_bounds_penalty(actions) )))

g3_to_tmb() will take our list of actions and convert it into C++ code suitable for use with TMB.

g3a_report_detail() and g3l_bounds_penalty() add further actions to the model, based on the actions already within it:

g3a_report_detail(): Adds abundance / catch reporting suitable for use with gadgetutils::g3_fit() and gadgetplots::gadget_plots()
g3l_bounds_penalty(): Adds a large likelihood penalty for any parameter straying outside the lower/upper bounds. This allows us to use the lower/upper bounds for parameters with optimising methods that don’t support them natively.

To be able to run this model, we need to provide values (or initial guesses) for parameters. Earlier we used g3_to_r() and saw the resultant parameter template. With g3_to_tmb() we can do the same, however the template is more complex:

simple_code <- g3_to_tmb(list(
    g3a_time(1990, 1991),
    g3a_naturalmortality(fish) ))
attr(simple_code, 'parameter_template')

##                      switch type value optimise random lower upper parscale
## retro_years     retro_years          0    FALSE  FALSE    NA    NA       NA
## fish.M.1           fish.M.1          0     TRUE  FALSE    NA    NA       NA
## fish.M.2           fish.M.2          0     TRUE  FALSE    NA    NA       NA
## fish.M.3           fish.M.3          0     TRUE  FALSE    NA    NA       NA
## fish.M.4           fish.M.4          0     TRUE  FALSE    NA    NA       NA
## fish.M.5           fish.M.5          0     TRUE  FALSE    NA    NA       NA
## project_years project_years          0    FALSE  FALSE    NA    NA       NA

The TMB parameter template has the following columns:

switch: The parameter name
type: Is the parameter a vector? Currently unused
value: The initial value for this parameter
optimise: Should this parameter be optimised or fixed
random: Should random effects be applied to this parameter? See vignette('random-effects')
lower: A lower bound for this parameter
upper: An upper bound for this parameter
parscale: Relative scale for this parameter vs. others

The model is expecting 5 parameters, fish.M.1 to fish.M.5, for each age group. We can either fix these to known values, or configure bounds to optimise within.

Filling in individual values can be tedious. The helper, g3_init_val(), will assist in filling in these values for you. Instead of setting individual values we can assign values using wildcard characters *, # (numeric), | (or):

attr(simple_code, "parameter_template") |>
    g3_init_val("*.M.#", 0.1) |>
    g3_init_val("*.M.3", 0.5) |>
    g3_init_val("*.M.2|4", 0.2)

##                      switch type value optimise random lower upper parscale
## retro_years     retro_years          0    FALSE  FALSE    NA    NA       NA
## fish.M.1           fish.M.1        0.1    FALSE  FALSE    NA    NA       NA
## fish.M.2           fish.M.2        0.2    FALSE  FALSE    NA    NA       NA
## fish.M.3           fish.M.3        0.5    FALSE  FALSE    NA    NA       NA
## fish.M.4           fish.M.4        0.2    FALSE  FALSE    NA    NA       NA
## fish.M.5           fish.M.5        0.1    FALSE  FALSE    NA    NA       NA
## project_years project_years          0    FALSE  FALSE    NA    NA       NA

Setting lower & upper bounds automatically turns on optimise, and fills in parscale:

attr(simple_code, "parameter_template") |>
    g3_init_val("*.M.#", 0.15, lower = 0.001, upper = 1)

##                      switch type value optimise random lower upper parscale
## retro_years     retro_years          0    FALSE  FALSE    NA    NA       NA
## fish.M.1           fish.M.1       0.15     TRUE  FALSE 0.001     1    0.999
## fish.M.2           fish.M.2       0.15     TRUE  FALSE 0.001     1    0.999
## fish.M.3           fish.M.3       0.15     TRUE  FALSE 0.001     1    0.999
## fish.M.4           fish.M.4       0.15     TRUE  FALSE 0.001     1    0.999
## fish.M.5           fish.M.5       0.15     TRUE  FALSE 0.001     1    0.999
## project_years project_years          0    FALSE  FALSE    NA    NA       NA

We can also use spread as a shorthand for lower = value * (1 - spread), upper = value * (1 + spread):

attr(simple_code, "parameter_template") |>
    g3_init_val("*.M.#", 0.15, spread = 0.5)

##                      switch type value optimise random lower upper parscale
## retro_years     retro_years          0    FALSE  FALSE    NA    NA       NA
## fish.M.1           fish.M.1       0.15     TRUE  FALSE 0.075 0.225     0.15
## fish.M.2           fish.M.2       0.15     TRUE  FALSE 0.075 0.225     0.15
## fish.M.3           fish.M.3       0.15     TRUE  FALSE 0.075 0.225     0.15
## fish.M.4           fish.M.4       0.15     TRUE  FALSE 0.075 0.225     0.15
## fish.M.5           fish.M.5       0.15     TRUE  FALSE 0.075 0.225     0.15
## project_years project_years          0    FALSE  FALSE    NA    NA       NA

This allows us to fill in parameters without worrying too much about stock/fleet naming:

# Guess l50 / linf based on stock sizes
estimate_l50 <- g3_stock_def(fish, "midlen")[[length(g3_stock_def(fish, "midlen")) / 2]]
estimate_linf <- max(g3_stock_def(fish, "midlen"))
estimate_t0 <- g3_stock_def(fish, "minage") - 0.8

attr(model_code, "parameter_template") |>
  # fish.init.scalar & fish.rec.scalar: Overall scalar for recruitment/initial conditions, see g3a_renewal_normalparam()
  g3_init_val("*.rec|init.scalar", 10, lower = 0.001, upper = 200) |>
  # fish.rec.(age): Per-age recriutment scalar, see g3a_renewal_normalparam()
  g3_init_val("*.init.#", 10, lower = 0.001, upper = 200) |>
  # fish.rec.(year): Recruitment level year-on-year, see g3a_renewal_normalparam()
  g3_init_val("*.rec.#", 100, lower = 1e-6, upper = 1000) |>
  # fish.rec.sd: Standard deviation for recruitment, see g3a_renewal_normalparam()
  g3_init_val("*.rec.sd", 5, lower = 4, upper = 20) |>
  # init.F: Offset for initial M, see g3a_renewal_initabund()
  g3_init_val("init.F", 0.5, lower = 0.1, upper = 1) |>

  # fish.M.(age): per-age M for our species, see g3a_naturalmortality()
  g3_init_val("*.M.#", 0.15, lower = 0.001, upper = 1) |>

  # fish.Linf, fish.K, fish.t0: VonB parameters for our species, see g3a_renewal_vonb_t0(), g3a_grow_lengthvbsimple()
  g3_init_val("*.Linf", estimate_linf, spread = 0.2) |>
  g3_init_val("*.K", 0.3, lower = 0.04, upper = 1.2) |>
  g3_init_val("*.t0", estimate_t0, spread = 2) |>

  # fish.walpha, fish.wbeta: Age/weight relationship for initialconditions, renewal, see g3a_renewal_normalparam()
  g3_init_val("*.walpha", 0.01, optimise = FALSE) |>
  g3_init_val("*.wbeta", 3, optimise = FALSE) |>

  # fish.f_surv.alpha, fish.f_surv.l50: Curve/l50 for fishing suitability, see g3_suitability_exponentiall50()
  g3_init_val("*.*.alpha", 0.07, lower = 0.01, upper = 0.2) |>
  g3_init_val("*.*.l50", estimate_l50, spread = 0.25) |>

  # fish.bbin: Beta for beta-binomial distribution for fish growth, see g3a_grow_impl_bbinom()
  g3_init_val("*.bbin", 100, lower = 1e-05, upper = 1000) |>

  # identity() is a do-nothing function, but it lets us finish on a new line
  identity() -> params.in

Finally we are ready for optimisation runs. g3_tmb_adfun() is a wrapper around TMB::MakeADFun() and TMB::compile, producing a TMB objective function. gadgetutils::g3_iterative() then optimises based on iterative reweighting

# Optimise model ################################
obj.fn <- g3_tmb_adfun(model_code, params.in)

params.out <- gadgetutils::g3_iterative(getwd(),
    wgts = "WGTS",
    model = model_code,
    params.in = params.in,
    grouping = list(
        fleet = c("ldist_f_surv", "aldist_f_surv"),
        abund = c("dist_si_cpue")),
    method = "BFGS",
    control = list(maxit = 1000, reltol = 1e-10),
    cv_floor = 0.05)

Once this has finished, we can view the output using gadgetplots::gadget_plots().

# Generate detailed report ######################
fit <- gadgetutils::g3_fit(model_code, params.out)
gadgetplots::gadget_plots(fit, "figs", file_type = "html")

Once finished, you can view the output in your web browser:

utils::browseURL("figs/model_output_figures.html")