\usepackage{lscape} \newcommand{\blandscape}{\begin{landscape}} \newcommand{\elandscape}{\end{landscape}}
aucTss<-function(om,mp){
  rtn=mydas:::roc(om,mp)

  data.frame("tss"=data.frame(with(rtn[abs(rtn$reference-1)==min(abs(rtn$reference-1)),],TPR-FPR))[1,],
  data.frame("auc"=pROC:::auc(as.character(rtn$label),rtn$reference)))}

dat=data.frame(om=rep(c(1,2),10),mp=rlnorm(20))

aucTss(dat$om>1,dat$mp)
## Setting levels: control = FALSE, case = TRUE
## Setting direction: controls < cases
##    tss  auc
## 1 -0.3 0.64

Figure 1 Life history parameters and the correlations between them.

Figure 2 Operating Models for base case.

Figure 3 Length data

Figure 4. Example of length based indicators and estimators for Base Case by species.

Figure 5. Example of length based indicators and estimators for Base Case by species.

Figure 6. Example of length based indicators and estimators for pollack across scenarios.

Figure 7. Example of length based indicators and estimators by scenarios contrasted with base case.

Figure 8. True Skill Score, analytical

Figure 9. True Skill Score, relative

Figure 10. True Skill Score, relative

Area under the ROC curve

Figure 11. ROC curves

Figure 12. Area under the ROC curves

Figure 13. Estimates of slope.

Figure 14 One and three step ahead fits.

Entropy

Are system dynamics the result of deterministic or stochastic processes [@pennekamp2019intrinsic]? I.e. can we predict the results of management actions based on a production function, as used to estimate reference points [@sissenwine1987alternative], or are the consequences depend on process error? i.e. processes not included in the assessment model.

Permutation entropy is a measure of the complexity of a time series [@bandt2002permutation] that is negatively correlated with a system’s predictability [@garland2018anomaly]. Permutation entropy quantifies the combined new and lost information, and is scaled to range between a minimum of 0 and a maximum of 1.

Figure 15. Entropy for base case

Figure 16. Entropy

Figure 17. Entropy by scenarios

Figure 18. Entropy by enytropy

Figure 19. Entropy v AUC

Figure 20. Status 100

Figure 21. Status 118

MSE

Figure 22. Comparison of Operating Model run for F with HCR run with perfect CPUE and with \(L_{Mean}\)

References

Author information

Laurence Kell.

Acknowledgements

Software Versions

  • R version 4.2.1 (2022-06-23)
  • FLCore: 2.6.18.9025
  • FLBRP: 2.5.8.9001
  • FLasher: 0.6.8.9003
  • FLife: 3.4.0
  • mydas: 1.2.2
  • Compiled: Mon Jul 11 13:38:56 2022

t looks like LBSPR can get you a reasonable estimate of F:FMSY from 5 years ago, if you collect data today I think what we can for jabba-z is look at what happens if i) you can go back and get data from past years ii) if you start collecting data tofday how long before you get a good estimate i.e. go from a single F/.FMSY point to multiple years this would be good for real data poor situations i.e. where the world bankl throws some money at a country and then goes away this would allow value to be obtained from past projects, or to design new ones