MSEtool (version 3.7.3)

tune_MP: Tune MP

Description

A generic function that uses optimize to tune a single MP parameter to minimize a user-specified function (e.g. squared distance from a mean yield, PGK = 60%, etc.)

Usage

tune_MP(Hist_list, MP, MP_parname, interval, minfunc, tol = 0.01, parallel = F)

Value

A function of class MP with argument MP_parname tuned by optim to minimize minfunc

Arguments

Hist_list

A list of objects of class Hist - created by runMSE(..., Hist=T)

MP

A character string that is the name of the MP to be tuned

MP_parname

A character string that is the argument (parameter) of the MP to be tuned

interval

A numeric vector two positions long that is the c(lower.bound, upper.bound) for the parameter to be tuned (MP_parname)

minfunc

A function to be minimized (e.g. the squared difference between mean yield obtained by the MP and a desired yield) that takes a list of MSE objects as its first argument.

tol

A positive numerical value that is the tolerance for the optimize procedure (default is 1E-2)

parallel

Logical: should the MSE projections (over the Hist objects in Hist_list) be calculated in parallel?

Author

T. Carruthers

Examples

if (FALSE) {
testOM@cpars$Data = new('Data')
testOM@cpars$Data@MPrec=2000
Hist_1 = runMSE(testOM,Hist=T)
testOM2 = testOM
testOM2@D = testOM@D / 2
Hist_2 = runMSE(testOM2,Hist=T)

myMP = function(x, Data, reps=1, rate = 1){
  CpI = mean(Data@Cat[x,46:50]) / mean(Data@Ind[x,46:50],na.rm=T)
  I = Data@Ind[x,]
  recI = mean(I[length(I)-((5-1):0)])
  Rec=new('Rec')
  Rec@TAC = recI * CpI * rate
  Rec
}
class(myMP) = "MP"

C1000 = function(MSE_list){
  mucat = mean(sapply(MSE_list,function(X){mean(X@Catch)}))
  cat(paste0("mean catch = ",round(mucat,3),"\n"))
  (mucat - 1000)^2 # try to match 1,250t mean yield
}

myMP_t = tune_MP(list(Hist_1,Hist_2), MP = "myMP", MP_parname = "rate", 
                 interval = c(1,1.5), minfunc = C1000, tol=1E-3, parallel =F)

formals(myMP_t)$rate
}