retrospective_AM (retrospective of Assessment model in MSE)

Plots the true retrospective of an assessment model during the closed-loop simulation. A series of time series estimates of SSB, F, and VB are plotted over the course of the MSE are plotted against the operating model (true) values (in black).

Usage

retrospective_AM(MSE, MP, sim = 1, plot_legend = FALSE)

Arguments

MSE: An object of class MSEtool::MSE.
MP: Character. The name of the management procedure created by make_MP() containing the assessment model.
sim: Integer between 1 and MSE@nsim. The simulation number for which the retrospectives will be plotted.
plot_legend: Logical. Whether to plot legend to reference year of assessment in the MSE.

Value

A series of figures for SSB, depletion, fishing mortality, and vulnerable biomass (VB) estimated in the MP over the course of the closed-loop simulation against the values generated in the operating model (both historical and projected).

Details

For assessment models that utilize annual exploitation rates (u), the instantaneous fishing mortality rates are obtained as F = -log(1 - u).

Note

This function only plots retrospectives from a single simulation in the MSE. Results from one figure may not be indicative of general assessment behavior and performance overall.

Author

Q. Huynh

Examples

# \donttest{
SP_40_10 <- make_MP(SP, HCR_MSY, diagnostic = "full")
OM <- MSEtool::testOM; OM@proyears <- 20
myMSE <- MSEtool::runMSE(OM = OM, MPs = "SP_40_10")
#> ℹ Checking OM for completeness
#> ✔ Loading operating model
#> ✔ Optimizing for user-specified movement
#> ✔ Calculating MSY reference points for each year
#> ✔ Optimizing for user-specified depletion in last historical year
#> ✔ Calculating historical stock and fishing dynamics
#> ✔ Calculating per-recruit reference points
#> ✔ Calculating B-low reference points
#> ✔ Calculating reference yield - best fixed F strategy
#> ✔ Simulating observed data
#> ✔ Running forward projections
#> ✔ 1 / 1  Running MSE for SP_40_10 
#> 
  |===                                               | 5 % ~02s          
  |======                                            | 11% ~01s          
  |========                                          | 16% ~01s          
  |===========                                       | 21% ~00s          
  |==============                                    | 26% ~01s          
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  |===================                               | 37% ~01s          
  |======================                            | 42% ~00s          
  |========================                          | 47% ~01s          
  |===========================                       | 53% ~00s          
  |=============================                     | 58% ~00s          
  |================================                  | 63% ~00s          
  |===================================               | 68% ~00s          
  |=====================================             | 74% ~00s          
  |========================================          | 79% ~00s          
  |===========================================       | 84% ~00s          
  |=============================================     | 89% ~00s          
  |================================================  | 95% ~00s          
  |==================================================| 100% elapsed=01s  
#> 
retrospective_AM(myMSE, MP = "SP_40_10", sim = 1)


# How to get all the estimates
library(dplyr)
assess_estimates <- lapply(1:myMSE@nMPs, function(m) {
  lapply(1:myMSE@nsim, function(x) {
    report <- myMSE@PPD[[m]]@Misc[[x]]$Assessment_report
    if (is.null(report)) {
      return(data.frame())
    } else {
      mutate(report, MP = myMSE@MPs[m], Simulation = x)
    }
  }) %>% bind_rows()
}) %>% bind_rows()
# }