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Create default parameters for a FIMS model

Source: R/create_default_parameters.R
create_default_parameters.Rd

This function generates a tibble with all of the parameters necessary to run a FIMS model given the desired high-level configuration that is specified in configurations. The tibble contains default initial values and estimation settings required to build and run the model. You can edit the returned tibble if you want to changes things such as initial values to values more specific to your population before running your model. For example, the default maturity parameters will need modified.

Usage

create_default_parameters(configurations, data)

Arguments

configurations

A tibble of model configurations. Typically created by create_default_configurations(). Users can modify this tibble to customize the model structure before using it as input to this function.

data

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

Value

A nested tibble containing information on parameters for your model with the same top-level columns as the input tibble passed to configurations but with additional information in the nested data column. See below for more details:

model_family:

The specified model family (e.g., "catch_at_age").

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_name:

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

data:

A list-column containing a tibble with detailed parameters. Unnesting this column reveals:

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 name of the parameter (e.g., "inflection_point").

age:

The age the parameter applies to.

length:

The length bin the parameter applies to.

time:

The time step (i.e., year) the parameter applies to.

value:

The initial value of the parameter.

estimation_type:

The type of estimation (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 deviations.

distribution:

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

Details

The function processes the configurations tibble, which only contains high-level information for running your model by calling internal helper functions on each row and returning a multi-row parameter set for each input row. For example, if a selectivity for the first fleet is configured as "Logistic", it takes that single row of input information and returns a parameter set with two rows, one for each parameter, "inflection_point" and "slope".

See also

Examples

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

# Create default configurations
default_configurations <- create_default_configurations(fims_frame)

# Create default parameters
default_parameters <- create_default_parameters(
  configurations = default_configurations,
  data = fims_frame
) |>
  tidyr::unnest(cols = data)

# Update selectivity parameters for survey1
updated_parameters <- default_parameters |>
  dplyr::rows_update(
    tibble::tibble(
      fleet_name = "survey1",
      label = c("inflection_point", "slope"),
      value = c(1.5, 2)
    ),
    by = c("fleet_name", "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 <- default_configurations |>
  tidyr::unnest(cols = data) |>
  dplyr::rows_update(
    tibble::tibble(
      module_name = "Selectivity",
      fleet_name = "fleet1",
      module_type = "DoubleLogistic"
    ),
    by = c("module_name", "fleet_name")
  ) |>
  create_default_parameters(
    data = fims_frame
  )
} # }