Skip to contents

Self test simulation analysis analysis

Source: R/9-retro_and_jitter.R
self_test.Rd

Simulates data from an Rceattle model and refits the model to the simulated data. TODO add process variation (i.e. random devs) to simulation.

Usage

self_test(
  Rceattle = NULL,
  nsim = 50,
  simulate = TRUE,
  seed = 123,
  cores = NULL
)

Arguments

Rceattle

an Rceattle model fit using fit_mod

nsim

number of simulations

simulate

passed to sim_mod. If TRUE (default), data are simulated with observation error; if FALSE, expected values from the model are used.

seed

random number seed. Each simulation i uses seed + i so results are reproducible under both sequential and parallel execution.

cores

Number of cores to use for parallel simulations. Default NULL picks parallel::detectCores() - 6, capped at 2 when running under R CMD check (which sets _R_CHECK_LIMIT_CORES_). Set to 1 to force sequential execution.

Value

a list of Rceattle models

Examples

# \donttest{
data(BS2017SS)
ss_run <- fit_mod(data_list = BS2017SS,
    inits = NULL, file = NULL,
    estimateMode = 0, random_rec = FALSE,
    msmMode = 0, avgnMode = 0,
    phase = FALSE, verbose = 0)
#> Warning: Passing ‘phase’, ‘verbose’ directly to fit_mod() is deprecated and will be removed in a future release. Bundle these into fit_control() instead, e.g. fit_control(phase = ..., verbose = ...). Forwarding for now.
#> 'Diet_loglike' are not included in data, assuming 'Multinomial'
#> 'Selectivity_dimension' not specified in 'fleet_control', assuming 'Age'
#> 'CAAL_weights' not specified in 'fleet_control', assuming 1
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
sims <- self_test(ss_run, nsim = 10)
# }