Set up default parameters for a FIMS model

Initializing a FIMS model, i.e., initialize_fims() requires a FIMSFrame object and a tibble of parameters. The parameter tibble can be automatically generated using this function or by building up your own tibble from helper functions used within this function. The resulting tibble will have all of the necessary parameters, specific to your data, to run a FIMS model. Initializing a FIMS model, i.e., initialize_fims() requires a FIMSFrame object and a tibble of parameters. The parameter tibble can be automatically generated using this function or by building up your own tibble from helper functions used within this function. The resulting tibble will have all of the necessary parameters, specific to your data, to run a FIMS model.

Usage

setup_default_parameters(data)

Arguments

data

A FIMSFrame object returned from running FIMSFrame() on your long input data.

Value

A tibble containing default parameter values and metadata for your model. Key columns are listed below:

module_name:

The name of the FIMS module (e.g., "Data", "Selectivity", "Recruitment", "Growth", "Maturity"). These entries are always written in PascalCase to match the names used in the C++ code.

fleet:

The name of the fleet the module applies to. This will be NA for non-fleet-specific modules like "Recruitment".

module_type:

The specific type of the module (e.g., "Logistic" for a "Selectivity" module). This column will always be written in PascalCase to match the names used in the C++ code.

label:

The parameter name (e.g., "inflection_point").

age:

The age the parameter applies to.

length:

The length bin the parameter applies to.

timing:

The timing step (year) the parameter applies to.

value:

The initial value of the parameter.

estimation_type:

The estimation type (e.g., "constant", "fixed_effects", "random_effects").

distribution_type:

The type of distribution (e.g., "data", "process"), where a process distribution can refer to a fixed effect or a random effect but it does not fit to data, e.g., recruitment deviation.

distribution:

The distribution name (e.g., "Dlnorm", "Dmultinom"). This column will always be written in PascalCase to match the names used in the C++ code.

Details

The function builds module-specific defaults by calling helper functions for data, fleet, selectivity, recruitment, maturity, growth, and population components, then combines those defaults into one tibble. You can modify the returned tibble before fitting a model (for example, updating maturity and selectivity parameter values).

To create the default initial numbers at age, this function uses the defaults from setup_default_Population() and setup_default_Recruitment(), which are passed to setup_default_init_naa() to calculate initial numbers at age.

See also

• FIMSFrame()

• initialize_fims()

Examples

if (FALSE) { # \dontrun{
# Load the example dataset and create a FIMS data frame
data("data_big")
fims_frame <- FIMSFrame(data_big)

# Set up default parameters
default_parameters <- setup_default_parameters(data = fims_frame)

# Update selectivity parameters for survey1
updated_parameters <- default_parameters |>
  dplyr::rows_update(
    tibble::tibble(
      fleet = "survey1",
      label = c("inflection_point", "slope"),
      value = c(1.5, 2)
    ),
    by = c("fleet", "label")
  )

# Do the same as above except, model fleet1 with double logistic selectivity
# To see required parameters for double logistic selectivity, run
# show(DoubleLogisticSelectivity) and look at the Fields list
parameters_with_double_logistic <- updated_parameters |>
  dplyr::filter(!(fleet == "fleet1" & module_name == "Selectivity")) |>
  dplyr::bind_rows(
    setup_default_Selectivity(
      data = fims_frame,
      fleet = "fleet1",
      module_type = "DoubleLogistic"
    )
  )
} # }