ss3sim (version 1.0.3)

sample_agecomp: Sample age compositions from a Stock Synthesis data file

Description

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.

Usage

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

Value

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

Which arguments to specifiy in case files

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.

See Also

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

Examples

# NOT RUN {
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)
# }