Set up a new distribution for a data type or a process

Use methods::new() to set up a distribution within an existing module with the necessary linkages between the two. For example, a fleet module will need a distributional assumption for parts of the data associated with it, which requires the use of initialize_data_distribution(), and a recruitment module, like the Beverton–Holt stock–recruit relationship, will need a distribution associated with the recruitment deviations, which requires initialize_process_distribution().

Usage

initialize_data_distribution(
  module,
  family,
  sd = list(value = 1, estimated = FALSE),
  data_type = c("index", "agecomp", "lengthcomp")
)

initialize_process_distribution(
  module,
  par,
  family,
  sd = list(value = 1, estimated = FALSE),
  is_random_effect = FALSE
)

Arguments

module

An identifier to a C++ fleet module that is linked to the data of interest.

family

A description of the error distribution and link function to be used in the model. The argument takes a family class, e.g., stats::gaussian(link = "identity").

sd

A list of length two. The first entry is named "value" and it stores the initial values (scalar or vector) for the relevant standard deviations. The default is value = 1. The second entry is named "estimated" and it stores a vector of booleans (default = FALSE) is a scalar indicating whether or not standard deviation is estimated. If "value" is a vector and "estimated" is a scalar, the single value specified "estimated" value will be repeated to match the length of value. Otherwise, the dimensions of the two must match.

data_type

A string specifying the type of data that the distribution will be fit to. Allowable types include c, index, agecomp, lengthcomp and the default is c.

par

A string specifying the parameter name the distribution applies to. Parameters must be members of the specified module. Use methods::show(module) to obtain names of parameters within the module.

is_random_effect

A boolean indicating whether or not the process is estimated as a random effect.

Value

A reference class. is returned. Use methods::show() to view the various Rcpp class fields, methods, and documentation.

Examples

if (FALSE) { # \dontrun{
# Set up a new data distribution
n_years <- 30
# Create a new fleet module
fleet <- methods::new(Fleet)
# Create a distribution for the fleet module
fleet_distribution <- initialize_data_distribution(
  module = fishing_fleet,
  family = lognormal(link = "log"),
  sd = list(
    value = rep(sqrt(log(0.01^2 + 1)), n_years),
    estimated = rep(FALSE, n_years) # Could also be a single FALSE
  ),
  data_type = "index"
)

# Set up a new process distribution
# Create a new recruitment module
recruitment <- methods::new(BevertonHoltRecruitment)
# view parameter names of the recruitment module
methods::show(BevertonHoltRecruitment)
# Create a distribution for the recruitment module
recruitment_distribution <- initialize_process_distribution(
  module = recruitment,
  par = "log_devs",
  family = gaussian(),
  sd = list(value = 0.4, estimated = FALSE),
  is_random_effect = FALSE
)
} # }