sample_agecomp: Sample age compositions from a Stock Synthesis data file
In ss3sim: Fisheries Stock Assessment Simulation Testing with Stock Synthesis

Description Usage Arguments Value Which arguments to specifiy in case files Author(s) See Also Examples

Extract age-composition data from a .ss_new data file and sample the data. It is assumed that the composition data will be expected values as written by Stock Synthesis in the second section of the data file, but one can also sample input data. The resulting age-composition data are assumed to represent observed age composition and will overwrite the age data in dat_list, which is returned invisibly. The data file can also be written to the disk, if a file path is provided to outfile, and used as simulated data by an estimation model. If used with run_ss3sim, the case file should be named agecomp. A suggested (default) case letter is D for data.

1 2	sample_agecomp(dat_list, outfile = NULL, fleets, Nsamp, years, cpar = 1, ESS = NULL, keep_conditional = TRUE)

`dat_list`	An SS data list object as read in from `SS_readdat` in the r4ss package. Make sure you select option `section=2`.
`outfile`	A character string specifying the file name to use when writing the information to the disk. The string must include the proper file extension. No file is written using the default value of `NULL`, which leads to increased speed because writing the file takes time and computing resources.
`fleets`	*A vector of integers specifying which fleets to include. The order of the fleets pertains to the input order of other arguments. An entry of `fleets=NULL` leads to zero samples for any fleet.
`Nsamp`	*A numeric list of the same length as `fleets`. Either single values or vectors of the same length as the number of years can be passed through. Single values are repeated for all years. If no fleet collected samples, keep the value to `Nsamp=NULL`.
`years`	*A list the same length as `fleets` giving the years as numeric vectors. If no fleet collected samples, keep the value to `years=NULL`.
`cpar`	A numeric value or vector the same length as `fleets` controlling the variance of the Dirichlet distribution used for sampling. A value of `1` leads to the same standard deviation as a multinomial of the given `Nsamp`, `2` indicates twice, etc. Values greater than one indicate overdispersion, and less underdispersion. `NULL` or `NA` for a given fleet will lead to no dispersion.
`ESS`	The final effective sample size (ESS) associated with the simulated data. The ESS is not used to generate the simulated data but can be used as an input sample size in subsequent models that estimate population parameters or status. The default, `NULL`, leads to the true (internally calculated) #' ESS being used, which is `Nsamp` for the multinomial case or given by the formula under `cpar` for the Dirichlet case. At least one value must be provided for each fleet or a vector of year-specific values can be used for any given fleet. The argument accepts a list with entries, either a single integer or a vector of integers, for each fleet.
`keep_conditional`	A logical if conditional age-at-length data should be kept or removed entirely from the data file. `sample_agecomp` only works on the age-composition data and not on the conditional age-at-length data. To sample the conditional data, set `keep_conditional` to `TRUE` and use `sample_calcomp`.

A modified .dat file if !is.null(outfile). A list object containing the modified .dat file is returned invisibly.

All function argument descriptions that start with an asterisk (*) will be passed through the case files to run_ss3sim. If one of these arguments is not specified in a case file, then a value of NULL will be passed, which may or may not be an appropriate value. Other arguments will be ignored if specified.

Cole Monnahan and Kotaro Ono

Other sampling functions: clean_data, sample_calcomp, sample_index, sample_lcomp, sample_mlacomp, sample_wtatage

d <- system.file("extdata", package = "ss3sim")
f_in <- file.path(d, "models", "cod-om", "codOM.dat")
dat_list <- r4ss::SS_readdat(f_in, version = NULL, verbose = FALSE)

## Turn off age comps by specifying fleets=NULL
test <- sample_agecomp(dat_list = dat_list, fleets = NULL)

## Generate with a smaller number of fleet taking samples
ex1 <- sample_agecomp(dat_list = dat_list, outfile = NULL,
  fleets = 2, Nsamp = list(c(10, 50)), years = list(c(26, 27)))
NROW(ex1$agecomp) == 2

## Generate with varying Nsamp by year for first fleet
ex2 <- sample_agecomp(dat_list = dat_list, outfile = NULL,
  fleets = c(1, 2),
  Nsamp = list(c(rep(50, 5), rep(100, 5)), 50),
  years = list(seq(26, 44, 2), c(26:100)))

## Run three  cases showing Multinomial, Dirichlet(1), and over-dispersed
## Dirichlet for different levels of sample sizes
op <- par(mfrow = c(1, 3))
set.seed(1)
true <- prop.table(dat_list$agecomp[
  dat_list$agecomp$FltSvy == 1 & dat_list$agecomp$Yr == 50, -(1:9)])
cpars <- c(NA, 1, 4)
for (samplesize in c(30, 100, 1000)) {
  if (samplesize > 30) par(mar = c(5.1, 1, 4.1, 2.1))
  plot(dat_list$agebin_vector, true, type = "b", ylim = c(0, 1),
    col = 4, lwd = 2, xlab = "Age",
    ylab = ifelse(samplesize == 30, "Proportion", ""),
    main = paste("Sample size =", samplesize))
  if (samplesize == 30) {
    legend("topright", lty = 1, col = 1:4, bty = "n",
      legend = c("Multinomial", "Dirichlet(1)", "Dirichlet(4)", "Truth"))
  }
  for (i in seq_along(cpars)) {
    ex <- sample_agecomp(dat_list = dat_list, outfile = NULL, fleets = 1,
      Nsamp = list(samplesize), years = list(50), cpar = cpars[i])$agecomp
    lines(dat_list$agebin_vector, prop.table(ex[1, -(1:9)]),
      col = i, type = "b")
  }
}
par(op)