Learn R Programming

SAMtool (version 1.8.1)

projection: Projections for assessment models

Description

This function takes an assessment model and runs a stochastic projection based on future F or catch.

Usage

projection(
  Assessment,
  constrain = c("F", "Catch"),
  Ftarget,
  Catch,
  p_years = 50,
  p_sim = 200,
  obs_error,
  process_error,
  max_F = 3,
  seed = 499
)

Value

An object of class project that contains future predicted values of F, catch, biomass, recruitment, etc.

Arguments

Assessment

An object of class Assessment.

constrain

Whether to project on future F or catch. By default, projects on F.

Ftarget

The projection F, either of length 1 for constant F for the entirety of the projection or length p_years.

Catch

The projection catch, either of length 1 for constant catch for the entirety of the projection or length p_years.

p_years

Integer for the number of projection years.

p_sim

Integer for the number of simulations for the projection.

obs_error

A list of length two. In the first entry, a vector of length nsurvey giving the standard deviations of each future index, or alternatively an array of dimension p_sim, p_years, and nsurvey giving the deviates. The second entry is the standard deviation of the projected catch. Alternatively, a matrix of simulation and year-specific error structure for the catch (p_sim rows and p_year columns; a matrix of ones indicates perfect data).

process_error

Numeric, standard deviation for process error (e.g., recruitment or biomass deviates). If NULL, uses values from assessment model. Alternatively, a matrix of simulation and year-specific recruitment deviates (p_sim rows and p_year columns, a matrix of ones indicates no recruitment deviates).

max_F

The maximum allowable F if the projection is constrained on catch.

seed

An integer to set the seed for the sampling observation and process error deviates.

Examples

Run this code
# \donttest{
myAssess <- SP(Data = swordfish)
do_projection <- projection(myAssess, Ftarget = myAssess@FMSY)
# }

Run the code above in your browser using DataLab