robustSPM: robustSPM conducts a robustness test on the quality of fit of...

In haddonm/datalowSA: A Package to Faciliate Application of Data poor Stock Assessments

robustSPM conducts a robustness test on the quality of fit of an SPM

Description

robustSPM conducts a robustness test on the quality of fit of an SPM. This is done by using the original optimal model parameters or the original guessed parameter values, add random variation to each of them, and re-fit the model. This process needs to be repeated multiple times. This should enable an analysis of the stability of the modelling outcomes. If the optimum parameters are used then add more variation, if initial guesses are used you may need to select different starting points so that the random variation covers the parameter space reasonably well.

Usage

robustSPM(
  inpar,
  fish,
  glb,
  N = 10,
  scaler = 15,
  console = TRUE,
  schaefer = TRUE
)

Arguments

inpar	the parameter set to begin the trials with
fish	the fisheries data: at least year, catch, and cpue
glb	the global variables containing the spsname
N	the number of random trials to run; defaults to 10, which is too few
scaler	the divisor that sets the degree of normal random variation to add to the parameter values; default = 15 the smaller the value the more variable the outcome
console	print summary statistics to the screen? default = TRUE
schaefer	default = TRUE, which sets the analysis to the Schaefer model. setting it to FALSE applies the Fox model instead

Value

a list of results from each run, the range of values across runs, and the median values.

Examples

## Not run: 
  data(dataspm)
  fish <- dataspm$fish
  glb <- dataspm$glb
  props <- dataspm$props
  pars <- c(14,0.19,0.6)
  out <- robustSPM(pars,fish,glb,props)
  str(out)
  print(out$results)

## End(Not run)

haddonm/datalowSA documentation built on Nov. 5, 2023, 6:40 p.m.