SS_fitbiasramp: Estimate bias adjustment for recruitment deviates

View source: R/SS_fitbiasramp.R

SS_fitbiasrampR Documentation

Estimate bias adjustment for recruitment deviates

Description

Uses standard error of estimated recruitment deviates to estimate the 5 controls (Methot and Taylor, 2011) for bias adjustment in Stock Synthesis.

Usage

SS_fitbiasramp(
  replist,
  verbose = FALSE,
  startvalues = NULL,
  method = "BFGS",
  twoplots = TRUE,
  transform = FALSE,
  plot = TRUE,
  print = FALSE,
  plotdir = "default",
  shownew = TRUE,
  oldctl = NULL,
  newctl = NULL,
  altmethod = "nlminb",
  exclude_forecast = FALSE,
  pwidth = 6.5,
  pheight = 5,
  punits = "in",
  ptsize = 10,
  res = 300,
  cex.main = 1
)

Arguments

replist

A list object created by SS_output().

verbose

Controls the amount of output to the screen. Default=FALSE.

startvalues

A vector of 5 values for the starting points in the minimization. Default=NULL.

method

A method to apply to the 'optim' function. See ?optim for options. Default="BFGS". By default, optim is not used, and the optimization is based on the input altmethod.

twoplots

Make a two-panel plot showing devs as well as transformed uncertainty, or just the second panel in the set? Default=TRUE.

transform

An experimental option to treat the transform the 5 quantities to improve minimization. Doesn't work well. Default=FALSE.

plot

Plot to active plot device?

print

Print to PNG files?

plotdir

Directory where PNG files will be written. By default it will be the directory where the model was run.

shownew

Include new estimated bias adjustment values on top of values used in the model? (TRUE/FALSE)

oldctl

Optional name of existing control file to modify. Default=NULL.

newctl

Optional name of new control file to create from old file with estimated bias adjustment values. Default=NULL.

altmethod

Optimization tool to use in place of optim, either "nlminb" or "psoptim". If not equal to either of these, then optim is used.

exclude_forecast

Exclude forecast values in the estimation of alternative bias adjustment inputs?

pwidth

Default width of plots printed to files in units of punits. The default is pwidth=6.5.

pheight

Height of plots printed to png files in units of punits. Default is designed to allow two plots per page, with pheight_tall used for plots that work best with a taller format and a single plot per page.

punits

Units for pwidth and pheight. Can be "px" (pixels), "in" (inches), "cm" (centimeters), or "mm" (millimeters). The default is punits="in".

ptsize

Point size for plotted text in plots printed to files (see help("png") in R for details).

res

Resolution of plots printed to files. The default is res = 300.

cex.main

Character expansion for plot titles. The default is cex.main=1.

Details

Implementation of the bias adjustment ramp within Stock Synthesis increases the likelihood that the estimated recruitment events, which are log-normally distributed, are mean unbiased and comparable to results from Markov chain Monte Carlo estimation routines (Methot and Taylor, 2011). Options to account for the fact that data typically do not equally represent all modelled time periods are as follows:

  1. fix the bias adjustment parameters at best-guess values informed by a previous assessment or model run;

  2. fix values based on data availability, such that the start of the ramp aligns with the availability of composition data, the ramp down begins the last year those data are informative about recruitment, and the adjustment level is informed by life history;

  3. set the adjustment level to 1.0 for all years to mimic how it was handled it Stock Synthesis prior to 2009; or

  4. set the adjustment level to 0.0 for all years, but this last option is not recommended because it will lead to biased results.

Author(s)

Ian Taylor

References

Methot, R.D. and Taylor, I.G., 2011. Adjusting for bias due to variability of estimated recruitments in fishery assessment models. Can. J. Fish. Aquat. Sci., 68:1744-1760.

See Also

SS_output()


r4ss documentation built on May 28, 2022, 1:11 a.m.