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flr_mse_WKNSMSE_funs.R
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flr_mse_WKNSMSE_funs.R
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### ------------------------------------------------------------------------ ###
### functions for running FLR WKNSMSE ####
### ------------------------------------------------------------------------ ###
### ------------------------------------------------------------------------ ###
### calculate survey index/indices from stock ####
### ------------------------------------------------------------------------ ###
#' calculate survey index/indices from FLStock
#' This function calculates survey indices from the numbers at age of an
#' FLStock object
#' @param stk Object of class \linkS4class{FLStock} with stock and fishery data.
#' @param idx Object of class \linkS4class{FLIndices} or \linkS4class{FLIndex}.
#' @param use_q If \code{TRUE} index numbers are calculated by multiplying
#' numbers at age in the stock with the catchability.
#' @param use_time If \code{TRUE} observed numbers in the survey are corrected
#' for fishing and natural mortality.
#' @return An object of class \code{FLIndex} or \code{FLIndex} with the survey
#' index stored in the \code{index} slot.
#' @export
setGeneric("calc_survey", function(stk, idx, use_q = TRUE, use_time = TRUE) {
standardGeneric("calc_survey")
})
### stk = FLStock, idx = FLIndices
#' @rdname calc_survey
setMethod(f = "calc_survey",
signature = signature(stk = "FLStock", idx = "FLIndices"),
definition = function(stk, idx, use_q = TRUE, use_time = TRUE) {
### apply function to every element of FLIndices
lapply(X = idx, FUN = calc_survey_ind, stk = stk, use_q = use_q,
use_time = use_time)
})
#' @rdname calc_survey
setMethod(f = "calc_survey",
signature = signature(stk = "FLStock", idx = "FLIndices"),
definition = function(stk, idx, use_q = TRUE, use_time = TRUE) {
### apply function to every element of FLIndices
lapply(X = idx, FUN = calc_survey_ind, stk = stk, use_q = use_q,
use_time = use_time)
})
### stk = FLStock, idx = FLIndex
#' @rdname calc_survey
setMethod(f = "calc_survey",
signature = signature(stk = "FLStock", idx = "FLIndex"),
definition = function(stk, idx, use_q = TRUE, use_time = TRUE) {
calc_survey_ind(stk = stk, idx = idx, use_q = use_q, use_time = use_time)
})
#' @rdname calc_survey
setMethod(f = "calc_survey",
signature = signature(stk = "FLStock", idx = "FLIndex"),
definition = function(stk, idx, use_q = TRUE, use_time = TRUE) {
calc_survey_ind(stk = stk, idx = idx, use_q = use_q, use_time = use_time)
})
calc_survey_ind <- function(stk, idx,
use_q = TRUE, ### catchability
use_time = TRUE ### timing of survey
) {
### find ranges for years, ages & iters
ages <- intersect(dimnames(index(idx))$age, dimnames(stock.n(stk))$age)
years <- intersect(dimnames(index(idx))$year, dimnames(stock.n(stk))$year)
iter <- intersect(dimnames(index(idx))$iter, dimnames(stock.n(stk))$iter)
### timing of survey
if (isTRUE(use_time)) {
### use mean of fishing period
time <- mean(range(idx)[c("startf", "endf")])
} else {
### otherwise assume beginning of year
time <- 0
}
### If no age structure, use exploitable biomass index
if (isTRUE(length(ages) == 0) & isTRUE(time == 0.5)) {
### set age to 1, i.e. use first/only age
ages <- 1
### get index slot
index <- index(idx)
### get required quantities
Z_i <- m(stk)[, years,,,, iter] +
harvest(stk)[, years,,,, iter]
F_i <- harvest(stk)[, years,,,, iter]
CW_i <- catch.wt(stk)[, years,,,, iter]
N_i <- stock.n(stk)[, years,,,, iter]
### standardization by oldest age
### Fay / sumFy
W_i <- F_i %=% 0
W_i[] <- quantSums(F_i)
W_i[] <- F_i / W_i
### calculate FSB index
index <- quantSums(CW_i * W_i * N_i * exp(-time * Z_i))
### multiply by the number of age class to get index calculated by SAM
index <- index * dim(stk)[[1]]
### add catchability, if requested
if (isTRUE(use_q)) {
index <- index * index.q(idx)[, ac(years),,,, ac(iter)]
}
### insert values into index
index(idx)[, ac(years),,,, ac(iter)] <- index
} else {
### extract stock numbers for requested/available dimensions
index.n <- stock.n(stk)[ac(ages), ac(years),,,, ac(iter)]
### get Z = M & F
Z <- m(stk)[ac(ages), ac(years),,,, ac(iter)] +
harvest(stk)[ac(ages), ac(years),,,, ac(iter)]
### estimate stock numbers at time of survey
index.n <- index.n * exp(-time * Z)
### add catchability, if requested
if (isTRUE(use_q)) {
index.n <- index.n * index.q(idx)[ac(ages), ac(years),,,, ac(iter)]
}
### insert values into index
index(idx)[ac(ages), ac(years),,,, ac(iter)] <- index.n
}
return(idx)
}
### ------------------------------------------------------------------------ ###
### observations wrapper ####
### ------------------------------------------------------------------------ ###
oem_WKNSMSE <- function(stk,
deviances,
observations,
genArgs,
tracking,
catch_timing = -1, ### catch timing relative to ay
idx_timing = -1, ### index timing relative to ay
use_catch_residuals = FALSE, ### use residuals for
use_idx_residuals = FALSE, ### observations
use_stk_oem = FALSE, ### biological parameters, wts etc
...) {
### current (assessment) year
ay <- genArgs$ay
### create object for observed stock
if (!isTRUE(use_stk_oem)) {
### use OM as template
stk0 <- stk
} else {
### otherwise use observed stock provided in observations object
### this can include different biological data, e.g. weights at age
stk0 <- observations$stk
### update fishery data
catch.n(stk0) <- catch.n(stk)
catch(stk0) <- catch(stk)
discards.n(stk0) <- discards.n(stk)
discards(stk0) <- discards(stk)
landings.n(stk0) <- landings.n(stk)
landings(stk0) <- landings(stk)
}
### add uncertainty to catch
if (isTRUE(use_catch_residuals)) {
### implement for catch at age
catch.n(stk0) <- catch.n(stk) * deviances$stk$catch.dev
### split catch into discards and landings, based on landing fraction
landings.n(stk0) <- catch.n(stk0) * (landings.n(stk) / catch.n(stk))
discards.n(stk0) <- catch.n(stk0) * (1 - landings.n(stk) / catch.n(stk))
### update total catch/discards/landings
catch(stk0) <- computeCatch(stk0)
landings(stk0) <- computeLandings(stk0)
discards(stk0) <- computeDiscards(stk0)
}
### cut off years
### workaround for NS cod: survey until intermediate year, but catch stops
### 1 year earlier
### slots such as natural mortality, maturity need to be kept, otherwise
### SAM will fall over
if (any(idx_timing > catch_timing)) {
### keep stock until last survey data year
stk0 <- window(stk0, end = ay + max(idx_timing))
### find years to remove
yrs_remove <- (ay + catch_timing + 1):ay
### remove catch data
catch(stk0)[, ac(yrs_remove)] <- NA
catch.n(stk0)[, ac(yrs_remove)] <- NA
catch.wt(stk0)[, ac(yrs_remove)] <- NA
landings(stk0)[, ac(yrs_remove)] <- NA
landings.n(stk0)[, ac(yrs_remove)] <- NA
landings.wt(stk0)[, ac(yrs_remove)] <- NA
discards(stk0)[, ac(yrs_remove)] <- NA
discards.n(stk0)[, ac(yrs_remove)] <- NA
discards.wt(stk0)[, ac(yrs_remove)] <- NA
} else {
stk0 <- window(stk0, end = ay + catch_timing)
}
### calculate index values
observations$idx <- calc_survey(stk = stk, idx = observations$idx)
### observed survey
idx0 <- observations$idx
### add uncertainty to observed indices
if (isTRUE(use_idx_residuals)) {
idx0 <- lapply(seq_along(idx0), function(idx_i) {
idx_tmp <- idx0[[idx_i]]
index(idx_tmp) <- index(idx_tmp) * deviances$idx[[idx_i]]
return(idx_tmp)
})
}
### timing of survey
### 0: intermediate/assessment year
### <0: fewer years available & vice versa
if (length(idx_timing) < length(idx0)) {
idx_timing <- rep(idx_timing, length(idx0))
}
### restrict years for indices based on timing
idx0 <- lapply(seq_along(idx0), function(x) {
window(idx0[[x]], end = ay + idx_timing[x])
})
idx0 <- FLIndices(idx0) ### restore class
names(idx0) <- names(observations$idx)
### return observations
return(list(stk = stk0, idx = idx0, observations = observations,
tracking = tracking))
}
### ------------------------------------------------------------------------ ###
### stock assessment: SAM wrapper ####
### ------------------------------------------------------------------------ ###
### wrapper for calling SAM
### this makes used of the SAM wrapper in FLfse,
### which in turn calls SAM from the package stockassessment
SAM_wrapper <- function(stk, idx, tracking,
prop_biasN = 1.0,## bias in N-at-age in SAM fit
prop_biasF = 1.0,## bias in F-at-age in SAM fit
genArgs, ### contains ay (assessment year)
forecast = FALSE,
fwd_trgt = "fsq", ### what to target in forecast
fwd_yrs = 1, ### number of years to add
fwd_yrs_average = -3:0, ### years used for averages
fwd_yrs_rec_start = NULL, ### recruitment
fwd_yrs_sel = -3:-1, ### selectivity
fwd_yrs_lf_remove = -2:-1,
fwd_splitLD = TRUE,
parallel = FALSE,
conf = NULL, ### SAM configuration
par_ini = NULL, ### initial parameters
track_ini = FALSE, ### store ini for next year
...){
### get additional arguments
args <- list(...)
### get current (assessment) year
ay <- genArgs$ay
### check if initial parameter values for SAM exist from last year's fit
### and reuse if possible
### this overrides generic initial parameters, if they exist in par_ini
### (they are only used in first simulation year)
if (isTRUE(track_ini) & !is.null(attr(tracking@units, "par_ini"))) {
par_ini <- attr(tracking@units, "par_ini")
}
### fit SAM to provided data
fit <- FLR_SAM(stk = stk, idx = idx, conf = conf, par_ini = par_ini,
conf_full = TRUE, DoParallel = parallel, ...)
### add a bias to stock N-at-age and F-at-age from sam fits
ssb_sam <- array(NA, dim=c(length(fit), 9))
for (i in 1:length(fit)) {
if (!is.null(fit[i]) & length(fit[i][[1]]$sdrep$estY)>0) {
fit[i][[1]]$sdrep$estY[1:8] <- fit[i][[1]]$sdrep$estY[1:8] + log(prop_biasN)
fit[i][[1]]$sdrep$estY[9:15] <- fit[i][[1]]$sdrep$estY[9:15] + log(prop_biasF)
### save ssbs from ay+previous years for retrospective analysis
ssb_sam[i,] <- ssbtable(fit[i][[1]])[(length(ssbtable(fit[i][[1]])[,1])-8):length(ssbtable(fit[i][[1]])[,1]),1]
}
}
colnames(ssb_sam) <- fit[1][[1]]$data$years[(length(fit[1][[1]]$data$years)-8):length(fit[1][[1]]$data$years)]
### store parameter values and provide them as initial values next year
### store in tracking object, this is the only object which does not get
### overriden next year
### weird workaround: save as attribute of "unit" slot of tracking,
### otherwise the attributes will be lost later...
if (isTRUE(track_ini)) {
attr(tracking@units, "par_ini") <- sam_getpar(fit)
}
### convert into FLStock
stk0 <- SAM2FLStock(object = fit, stk = stk)
### perform forecast to get SSB ay+1
if (isTRUE(forecast)) {
### check how to do forecast
### currently, can only do F status quo
if (fwd_trgt != "fsq") stop("only fsq supported in forecast")
### years for average values
ave.years <- range(stk0)[["maxyear"]] + fwd_yrs_average
### years for sampling of recruitment years
rec.years <- seq(from = fwd_yrs_rec_start, to = range(stk0)[["maxyear"]] - 1)
### years where selectivity is not used for mean in forecast
overwriteSelYears <- range(stk0)[["maxyear"]] + fwd_yrs_sel
lst_yr <- range(stk0)[["maxyear"]]
### extend stk0
stk0 <- window(stk0, end = range(stk0)[["maxyear"]] + fwd_yrs)
### modify fwd_yrs in case last data year does not include catch
if (all(is.na(catch(stk0)[, ac(lst_yr)]))) fwd_yrs <- fwd_yrs + 1
### forecast years
yrs <- seq(to = dims(stk0)$maxyear, length.out = fwd_yrs)
### coerce fit into list if only 1 iteration
if (is(fit, "sam")) {
fit <- list(fit)
class(fit) <- "sam_list"
}
### do forecast for all iterations
fc <- foreach(fit_i = fit, .errorhandling = "pass") %do% {
### overwrite landing fraction with last year, if requested
if (!is.null(fwd_yrs_lf_remove)) {
### index for years to remove/overwrite
idx_remove <- nrow(fit_i$data$landFrac) + args$fwd_yrs_lf_remove
### overwrite
fit_i$data$landFrac[idx_remove, ] <- fit_i$data$landFrac[rep(nrow(fit_i$data$landFrac), length(idx_remove)), ]
}
### run forecast
fc_i <- stockassessment::forecast(fit = fit_i, fscale = rep(1, fwd_yrs),
ave.years = ave.years,
rec.years = rec.years,
overwriteSelYears = overwriteSelYears,
splitLD = fwd_splitLD)
### get numbers at age for all forecast years
numbers <- lapply(seq(fwd_yrs), function(x) {
### index for numbers at age
idx <- seq(length(fit_i$conf$keyLogFsta[1, ]))
### get simulated numbers
n <- exp(fc_i[[x]]$sim[, idx])
### median
apply(n, 2, median)
})
numbers <- do.call(cbind, numbers)
return(list(stock.n = numbers))
}
### if forecast failed for a iteration, the list element will for this
### iteration will be an error message
### get numbers
fc_stock.n <- lapply(fc, "[[", "stock.n")
### insert stock numbers
for (iter_i in seq(dim(stk0)[6])) {
### do not insert numbers if forecast failed
if (!is.numeric(fc_stock.n[[iter_i]])) next()
stock.n(stk0)[, ac(yrs),,,, iter_i] <- fc_stock.n[[iter_i]]
}
### extend stock characteristics required for calculation of SSB, weights, etc.
### find years to fill (do not fill years, if there is already data inside)
yrs_fill <- setdiff(yrs, lst_yr)
stock.wt(stk0)[, ac(yrs_fill)] <- yearMeans(stock.wt(stk0)[, ac(ave.years)])
m(stk0)[, ac(yrs_fill)] <- yearMeans(m(stk0)[, ac(ave.years)])
mat(stk0)[, ac(yrs_fill)] <- yearMeans(mat(stk0)[, ac(ave.years)])
harvest.spwn(stk0)[, ac(yrs_fill)] <- yearMeans(harvest.spwn(stk0)[, ac(ave.years)])
m.spwn(stk0)[, ac(yrs_fill)] <- yearMeans(m.spwn(stk0)[, ac(ave.years)])
harvest(stk0)[, ac(yrs_fill)] <- yearMeans(harvest(stk0)[, ac(ave.years)])
### PLEASE NOTE:
### SSB value slightly different from SSB value generated from SAM:
### SAM calculates SSB per simulation and gives median
### here: calculate median of numbers at age and calculate SSB from median numbers
### ISSUES: forecast fails if SAM did not converge, creates error and stops
}
### save convergence for all iterations
tracking["conv.est", ac(ay)] <- sapply(fit, function(x) x$opt$convergence)
### add perceived F and SSB
### done in mp()
### save model fit (list) as attribute in stk0
attr(stk0, "fit") <- fit
### return assessed stock, tracking & model fit
### (model fit required later for TAC calculation)
return(list(stk = stk0, tracking = tracking))
}
### ------------------------------------------------------------------------ ###
### phcr: parameterize HCR ####
### ------------------------------------------------------------------------ ###
phcr_WKNSMSE <- function(Btrigger = NULL, Ftrgt = NULL, Bpa = NULL, Fpa = NULL,
Blim = NULL, tracking, ...) {
### coerce existing values into FLPar
hcrpars <- list("Btrigger" = Btrigger, "Ftrgt" = Ftrgt, "Bpa" = Bpa,
"Fpa" = Fpa, "Blim" = Blim)
hcrpars <- hcrpars[!sapply(hcrpars, is.null)]
hcrpars <- do.call(FLPar, hcrpars)
### if more iterations provided than neccessary, subset
if (dims(hcrpars)$iter > dims(tracking)$iter) {
hcrpars <- hcrpars[, dimnames(tracking)$iter]
}
### return as list
### keep tracking unchanged
return(list(hcrpars = hcrpars, tracking = tracking))
}
### ------------------------------------------------------------------------ ###
### hcr: ICES advice rule and modificatinons ####
### ------------------------------------------------------------------------ ###
### target Ftrgt
### A: if SSB < Btrigger, target reduced: Ftrgt * SSB/Btrigger
### B & C: different behaviour if SSB < Blim
hcr_WKNSME <- function(stk, genArgs, hcrpars, tracking,
option = "A", ### WKNSMSE options
...) {
### target F
### reduce F if SSB below Btrigger
### get current (assessment) year
ay <- genArgs$ay
### number of iterations
it <- dim(stk)[6]
### get reference values and extend iteration dimension
Ftrgt <- propagate(FLPar(hcrpars["Ftrgt"]), it)
Btrigger <- propagate(FLPar(hcrpars["Btrigger"]), it)
### get Blim
### if Blim not specified, set to zero
if ("Blim" %in% dimnames(hcrpars)$params) {
Blim <- propagate(FLPar(hcrpars["Blim"]), it)
} else {
Blim <- propagate(FLPar(0), it)
}
### SSB status (last year only) relative to Btrigger
status_Btrigger <- tail(ssb(stk), 1) / Btrigger
### SSB status (last year only) relative to Blim
status_Blim <- tail(ssb(stk), 1) / Blim
### positions (iterations) where SSB is below Btrigger
pos_Btrigger <- which(status_Btrigger < 1)
### below Blim
pos_Blim <- which(status_Blim < 1)
### default ICES HCR (option A):
### if SSB<Btrigger => F = Ftrgt * SSB/Btrigger
if (option == "A") {
mult <- ifelse(status_Btrigger < 1, status_Btrigger, 1)
} else if (option == "B") {
### option B:
### if SSB<Btrigger => F = Ftrgt * SSB/Btrigger
### if SSB<Blim => F = Ftrgt * 0.25
### SSB < Btrigger
mult <- ifelse(status_Btrigger < 1, status_Btrigger, 1)
### set to 0.25 if SSB < Blim
if (length(pos_Blim) > 0) {
mult[,,,,, pos_Blim] <- 0.25
}
} else if (option == "C") {
### option C:
### if SSB<Btrigger => F = Ftrgt * SSB/Btrigger
### if SSB<Blim => F = max(Ftrgt * 0.25, Ftrgt * SSB/Btrigger)
### SSB < Btrigger
mult <- ifelse(status_Btrigger < 1, status_Btrigger, 1)
### if SSB < Blim, use maximum of SSB/Btrigger or 0.25
### i.e. limit ratio to 0.25
if (length(pos_Blim) > 0) {
mult[,,,,, pos_Blim] <- c(ifelse(mult[,,,,, pos_Blim] < 0.25, 0.25,
mult[,,,,, pos_Blim]))
}
} else {
### unknown options
mult <- 1
}
### new target
Ftrgt <- Ftrgt * mult
### create ctrl object
ctrl <- getCtrl(values = Ftrgt, quantity = "f", years = ay + 1, it = it)
### save in tracking
### done later
return(list(ctrl = ctrl, tracking = tracking))
}
### ------------------------------------------------------------------------ ###
### management implementation: short term forecast with SAM ####
### ------------------------------------------------------------------------ ###
### short term forecast with SAM
### including TAC constraint
is_WKNSMSE <- function(stk, tracking, ctrl,
genArgs, ### contains ay (assessment year)
TAC_constraint = c(FALSE, TRUE),
upper = Inf, lower = -Inf, Btrigger_cond = FALSE,
### short term forecast
fwd_trgt = c("fsq", "hcr"), ### target in forecast
fwd_yrs = 2, ### number of years to add
fwd_yrs_average = -3:0, ### years used for averages
fwd_yrs_rec_start = NULL, ### recruitment
fwd_yrs_sel = -3:-1, ### selectivity
fwd_yrs_lf_remove = -2:-1,
fwd_splitLD = TRUE,
### banking and borrowing
BB = FALSE, ### banking and borrowing
### check stock status before applying BB
BB_check_hcr = FALSE, ### check status before forecast
BB_check_fc = FALSE, ### check status after forecast
BB_rho, ### definition of BB
### reference points
hcrpars = list(),
...) {
### get current (assessment) year
ay <- genArgs$ay
### number of iterations
it <- dim(stk)[6]
### retrieve SAM model fit (list)
fit <- attr(stk, "fit")
### check class of model fit(s)
if (!class(fit) %in% c("sam", "sam_list"))
stop("attr(stk0, \"fit\") has to be class sam or sam_list")
### if single fit, turn into list
if (is(fit, "sam")) fit <- list(fit)
### if conditional banking & borrowing applied, extend forecast for one more
### year to check SSB in year after advice year
### for this additional forecast assume Fsq as target
### i.e. target F from HCR twice, in analogy to intermediate year assumption
if (isTRUE(BB) & isTRUE(BB_check_fc)) {
### duplicate last target value
fwd_trgt <- c(fwd_trgt, tail(fwd_trgt, 1))
}
### go through all model fits
fc <- foreach(fit_i = fit, iter_i = seq_along(fit), .errorhandling = "pass") %do% {
### overwrite landing fraction with last year, if requested
if (!is.null(fwd_yrs_lf_remove)) {
### index for years to remove/overwrite
idx_remove <- nrow(fit_i$data$landFrac) + fwd_yrs_lf_remove
### overwrite
fit_i$data$landFrac[idx_remove, ] <- fit_i$data$landFrac[rep(nrow(fit_i$data$landFrac), length(idx_remove)), ]
}
### check how to do forecast
### currently, can only do F status quo and F target from ctrl object
fscale <- ifelse(fwd_trgt == "fsq", 1, NA) ### scaled Fsq
### target F values
fval <- ifelse(fwd_trgt == "hcr", ctrl@trgtArray[, "val", iter_i], NA)
### years for average values
ave.years <- max(fit_i$data$years) + fwd_yrs_average
### years for sampling of recruitment years
if (is.null(fwd_yrs_rec_start)) {
rec.years <- fit_i$data$years ### use all years, if not defined
} else {
rec.years <- seq(from = fwd_yrs_rec_start, max(fit_i$data$years))
}
### years where selectivity is not used for mean in forecast
overwriteSelYears <- max(fit_i$data$years) + fwd_yrs_sel
### forecast
fc_i <- stockassessment::forecast(fit = fit_i, fscale = fscale, fval = fval,
ave.years = ave.years,
rec.years = rec.years,
overwriteSelYears = overwriteSelYears,
splitLD = fwd_splitLD)
### return forecast table
return(attr(fc_i, "tab"))
}
### if forecast fails, error message returned
### replace error message with NA
### extract catch target
catch_target <- sapply(fc, function(x) {
if (is(x, "error")) {
return(NA)
} else {
return(x[ac(ay + 1), "catch:median"])
}
})
### get reference points
hcrpars <- hcrpars[!sapply(hcrpars, is.null)]
hcrpars <- do.call(FLPar, hcrpars)
### if more iterations provided than neccessary, subset
if (dims(hcrpars)$iter > dims(tracking)$iter) {
hcrpars <- hcrpars[, dimnames(tracking)$iter]
} else if (isTRUE(dim(hcrpars)[2] < it)) {
hcrpars <- propagate(hcrpars, it)
}
### ---------------------------------------------------------------------- ###
### TAC constraint ####
if (isTRUE(TAC_constraint)) {
### target year
yr_target <- ctrl@target$year
### get previous target from tracking
catch_prev <- tracking["metric.is", ac(yr_target - 1), drop = TRUE]
### change in advice, % of last advice
change <- (catch_target / catch_prev) * 100
### limit changes
changes_new <- change
### upper limit
changes_new <- ifelse(changes_new > upper, upper, changes_new)
### lower limit
changes_new <- ifelse(changes_new < lower, lower, changes_new)
### find positions which exceed limits
pos <- which(changes_new != change)
### conditional constraint based on SSB>=Btrigger?
if (isTRUE(Btrigger_cond)) {
### get SSB in TAC year from forecast
SSB_TACyr <- sapply(fc, function(x) {
if (is(x, "error")) {
return(NA)
} else {
return(x[ac(ay + 1), "ssb:median"])
}
})
### iterations where SSB is at or above Btrigger at start of TAC year
pos_Btrigger <- which(SSB_TACyr >= c(hcrpars["Btrigger"]))
### only apply TAC constraint if both
### - TAC change exceeds limit
### - stock at or above Btrigger
pos <- intersect(pos, pos_Btrigger)
}
### modify advice
catch_target[pos] <- catch_prev[pos] * changes_new[pos]/100
}
### ---------------------------------------------------------------------- ###
### banking and borrowing ####
if (isTRUE(BB)) {
### get current rho
BB_rho_i <- tail(rep(BB_rho, length.out = (ay - genArgs$y0)), 1)
### get catch borrowed last year
BB_return <- tracking["BB_borrow", ac(ay - 1)]
### assume nothing borrowed if NA
BB_return <- ifelse(!is.na(BB_return), BB_return, 0)
### get catch banked last year
BB_bank_use <- tracking["BB_bank", ac(ay - 1)]
BB_bank_use <- ifelse(!is.na(BB_bank_use), BB_bank_use, 0)
### bank for next year
if (BB_rho_i < 0) {
BB_bank <- catch_target * abs(BB_rho_i)
} else {
BB_bank <- rep(0, it)
}
### borrow from next year
if (BB_rho_i > 0) {
BB_borrow <- catch_target * abs(BB_rho_i)
} else {
BB_borrow <- rep(0, it)
}
### conditional banking and borrowing?
### first condition: for HCR option A (stability option D2)
### apply BB only if HCR option A1 (not A2) is applied
### i.e. stop BB if SSB is below Btrigger in TAC year
### find iterations where SSB is below Btriggerin TAC year
if (isTRUE(BB_check_hcr)) {
pos_hcr <- which(c(tail(ssb(stk), 1)) < c(hcrpars["Btrigger"]))
} else {
pos_hcr <- integer(0)
}
### second condition: for HCR options B & C (stability option E2)
### stop BB if either
### - if SSB is below Bpa AND F above Fpa in TAC year
### - if SSB is below Bpa in TAC year and year after
### if TAC restricted by TAC constraint, additional forecast required
### to estimate stock status when only TAC is fished
pos_fc <- integer(0)
if (any(c(BB_bank, BB_borrow) > 0) & isTRUE(BB_check_fc)) {
### if TAC constraint activated, do a forecast
### check if TAC constraint used
if (isTRUE(TAC_constraint)) {
### check where TAC constraint is implemented
pos_constr <- which(changes_new != change)
if (isTRUE(length(pos_constr) > 0)) {
### go through model fits of requested iterations
fc_new <- foreach(fit_i = fit[pos_constr],
iter_i = seq_along(fit)[pos_constr],
.errorhandling = "pass") %do% {
### overwrite landing fraction with last year, if requested
if (!is.null(fwd_yrs_lf_remove)) {
### index for years to remove/overwrite
idx_remove <- nrow(fit_i$data$landFrac) + fwd_yrs_lf_remove
### overwrite
fit_i$data$landFrac[idx_remove, ] <- fit_i$data$landFrac[rep(nrow(fit_i$data$landFrac), length(idx_remove)), ]
}
### create vector with targets
### target Fsq in intermediate year, and year after TAC year
fscale <- c(1, 1, NA, 1)
### target TAC in TAC year
cval <- c(NA,NA, catch_target[iter_i], NA)
### years for average values
ave.years <- max(fit_i$data$years) + fwd_yrs_average
### years for sampling of recruitment years
if (is.null(fwd_yrs_rec_start)) {
rec.years <- fit_i$data$years ### use all years, if not defined
} else {
rec.years <- seq(from = fwd_yrs_rec_start, max(fit_i$data$years))
}
### years where selectivity is not used for mean in forecast
overwriteSelYears <- max(fit_i$data$years) + fwd_yrs_sel
### forecast
fc_i <- stockassessment::forecast(fit = fit_i,
fscale = fscale,
catchval = cval,
ave.years = ave.years,
rec.years = rec.years,
overwriteSelYears = overwriteSelYears,
splitLD = fwd_splitLD)
### return forecast table
return(attr(fc_i, "tab"))
}
### overwrite updated forecasts
fc[pos_constr] <- fc_new
### get SSB in TAC year
SSB_TACyr <- sapply(fc, function(x) {
if (is(x, "error")) {
return(NA)
} else {
return(x[ac(ay + 1), "ssb:median"])
}
})
### get SSB in year after TAC year
SSB_TACyr1 <- sapply(fc, function(x) {
if (is(x, "error")) {
return(NA)
} else {
return(x[ac(ay + 2), "ssb:median"])
}
})
### get F in TAC year
F_TACyr <- sapply(fc, function(x) {
if (is(x, "error")) {
return(NA)
} else {
return(x[ac(ay + 1), "fbar:median"])
}
})
### check if SSB in TAC year below Bpa AND F above Fpa
pos_fc1 <- which(SSB_TACyr < c(hcrpars["Bpa"]) &
F_TACyr > c(hcrpars["Fpa"]))
### check if SSB below Bpa in TAC year and year after
pos_fc2 <- which(SSB_TACyr < c(hcrpars["Bpa"]) &
SSB_TACyr1 < c(hcrpars["Bpa"]))
### combine both conditions
pos_fc <- union(pos_fc1, pos_fc2)
}
}
}
### get position where BB is stopped
pos_stop <- union(pos_hcr, pos_fc)
### if status evaluated as not precautionary
### stop banking and borrowing
### (but still pay back/use from last year)
BB_bank[pos_stop] <- 0
BB_borrow[pos_stop] <- 0
### correct target catch later in iem module
# catch_target <- catch_target - c(BB_return) +
# c(BB_bank_use) - c(BB_bank) + c(BB_borrow)
} else {
### if B&B not applied, store 0
BB_return <- BB_bank_use <- BB_bank <- BB_borrow <- 0
}
### save B&B transfers in tracking
tracking["BB_return", ac(ay)] <- BB_return
tracking["BB_bank_use", ac(ay)] <- BB_bank_use
tracking["BB_bank", ac(ay)] <- BB_bank
tracking["BB_borrow", ac(ay)] <- BB_borrow
### create ctrl object
ctrl <- getCtrl(values = catch_target, quantity = "catch",
years = ctrl@target$year, it = it)
### return catch target and tracking
return(list(ctrl = ctrl, tracking = tracking))
}
### ------------------------------------------------------------------------ ###
### implementation error: banking and borrowing ####
### ------------------------------------------------------------------------ ###
### so far only banking and borrowing (B&B) implemented
iem_WKNSMSE <- function(tracking, ctrl,
genArgs, ### contains ay (assessment year)
BB = FALSE, ### apply banking and borrowing
...) {
if (isTRUE(BB)) {
### get current (assessment) year
ay <- genArgs$ay
### retrieve banking and borrowing values
BB_return <- tracking["BB_return", ac(ay)]
BB_bank_use <- tracking["BB_bank_use", ac(ay)]
BB_bank <- tracking["BB_bank", ac(ay)]
BB_borrow <- tracking["BB_borrow", ac(ay)]
### replace NAs with 0
BB_return <- ifelse(!is.na(BB_return), BB_return, 0)
BB_bank_use <- ifelse(!is.na(BB_bank_use), BB_bank_use, 0)
BB_bank <- ifelse(!is.na(BB_bank), BB_bank, 0)
BB_borrow <- ifelse(!is.na(BB_borrow), BB_borrow, 0)
### get catch target(s)
catch_target <- ctrl@trgtArray[, "val", ]
### implement B&B
catch_target <- catch_target - c(BB_return) + c(BB_bank_use) -
c(BB_bank) + c(BB_borrow)
### save in ctrl object
ctrl@trgtArray[, "val", ] <- catch_target
}
### return catch target and tracking
return(list(ctrl = ctrl, tracking = tracking))
}
### ------------------------------------------------------------------------ ###
### forward projection ####
### ------------------------------------------------------------------------ ###
### including process error on stock.n
### implemented by simply multiplying numbers at age from fwd with noise factor
fwd_WKNSMSE <- function(stk, ctrl,
sr, ### stock recruitment model
sr.residuals, ### recruitment residuals
sr.residuals.mult = TRUE, ### are res multiplicative?
maxF = 2, ### maximum allowed Fbar
proc_res = NULL, ### process error noise
...) {
### project forward with FLash::fwd
stk[] <- fwd(object = stk, control = ctrl, sr = sr,
sr.residuals = sr.residuals,
sr.residuals.mult = sr.residuals.mult,
maxF = maxF)
### add process error noise, if supplied
if (!is.null(proc_res)) {
### projected years
yrs_new <- seq(from = ctrl@target[, "year"], to = range(stk)[["maxyear"]])
### workaround to get residuals
### they are saved in the "fitted" slot of sr...
if (!isTRUE(proc_res == "fitted")) {
stop("survival process error inacessible")
} else {
### implement process error
stock.n(stk)[, ac(yrs_new)] <- stock.n(stk)[, ac(yrs_new)] *
fitted(sr)[, ac(yrs_new)]
### update stock biomass
stock(stk) <- computeStock(stk)
}
}
### return stock
return(list(object = stk))
}
### ------------------------------------------------------------------------ ###
### extract uncertainty from SAM object ####
### ------------------------------------------------------------------------ ###
### function for creating iterations based on estimation of uncertainty in SAM
### note: SAM works on a log scale and all reported parameters are also on a
### scale, even standard deviations.
### Therefore, the values returned from this function are exponentiated
SAM_uncertainty <- function(fit, n = 1000, print_screen = FALSE) {
if (!is(fit, "sam")) stop("fit has to be class \"sam\"")
### index for fishing mortality ages
idxF <- fit$conf$keyLogFsta[1, ] + 1# +dim(stk)[1]
idxF <- idxF[idxF != 0] ### remove 0s
### index for F variances (usually some ages are bound)
#idxNVar <- fit$conf$keyVarLogN
### get ages used for calculating fbar
#bAges <- fit$conf$fbarRange
#bAges <- do.call(':', as.list(bAges))
### index for stock numbers ages
#idxN <- 1:ncol(natural.mortality)
#idxN <- seq(min(fit$data$minAgePerFleet), max(fit$data$maxAgePerFleet))
### index for observation variances
idxObs <- fit$conf$keyVarObs # starts at 0