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Jitter analysis

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

Run's the Rceattle model at initial values that are +- N(0, sd) from the initial parameters.

Usage

jitter(
  Rceattle = NULL,
  njitter = 50,
  sd = 0.2,
  phase = FALSE,
  seed = 123,
  cores = NULL
)

Arguments

Rceattle

an Rceattle model fit using fit_mod

njitter

the number of jitters to run

sd

standard deviation for jitter (default = 0.2)

phase

as in fit_mod default = FALSE

seed

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

cores

Number of cores to use for parallel jitters. 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
jitters <- jitter(ss_run, njitter = 10)
# }