Retrospective peels — retrospective • Rceattle

Calculate Mohn's rho and run retrospective peels for an Rceattle model. The function also evaluates retrospective forecast skill. To evaluate both retrospective bias and forecast skill, the function uses the map functionality of TMB to peel the model:

Filters data, filters fixed inputs, and maps out time-varying parameters for the peeled years. All time-varying parameters for the peeled years are set to the terminal year of the model for that peel.
Fits the peeled model.
Turns off all hindcast parameters, turns on F for the peeled years, and fits to the peeled catch series to update the "forecast" dynamics given projection assumptions and observed catch from the peeled years.

Usage

retrospective(Rceattle = NULL, peels = 5, rescale = FALSE, nyrs_forecast = 3)

Arguments

Rceattle: an Rceattle model fit using fit_mod
peels: the number of retrospective peels to use in the calculation of rho and for model estimation
rescale: TRUE/FALSE whether to subset and rescale environmental predictors for the range of peel years.
nyrs_forecast: Number of forecast years to calculate Mohn's Rho in addition to terminal year

Value

a list of 1. list of Rceattle models and 2. vector of Mohn's rho for each species

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
retro <- retrospective(ss_run, peels = 10)
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
#> `age_trans_matrix` data does not span range of age for species 1 will fill with 0s
# }