Nothing
summary_SP <- function(Assessment, state_space = FALSE) {
assign_Assessment_slots(Assessment)
current_status <- data.frame(Value = c(F_FMSY[length(F_FMSY)], B_BMSY[length(B_BMSY)],
B_B0[length(B_B0)]))
rownames(current_status) <- c("F/FMSY", "B/BMSY", "B/B0")
Value <- numeric(0)
Description <- character(0)
rownam <- character(0)
if ("log_dep" %in% names(obj$env$map)) {
Value <- c(Value, TMB_report$dep)
Description <- c(Description, "Initial depletion")
rownam <- c(rownam, "dep")
}
if ("log_n" %in% names(obj$env$map)) {
Value <- c(Value, TMB_report$n)
Description <- c(Description, "Production exponent")
rownam <- c(rownam, "n")
}
if (state_space && "log_tau" %in% names(obj$env$map)) {
Value <- c(Value, TMB_report$tau)
Description <- c(Description, "Biomass deviation SD (log-space)")
rownam <- c(rownam, "tau")
}
if (length(Value) == 0) input_parameters <- data.frame() else {
input_parameters <- data.frame(Value = Value, Description = Description, stringsAsFactors = FALSE)
rownames(input_parameters) <- rownam
}
derived <- data.frame(Value = c(TMB_report$r, TMB_report$K, TMB_report$BMSY, TMB_report$BMSY/TMB_report$K),
Description = c("Intrinsic rate of population increase", "Carrying capacity",
"Biomass at MSY", "Depletion at MSY"),
stringsAsFactors = FALSE)
rownames(derived) <- c("r", "K", "BMSY", "BMSY/B0")
model_estimates <- sdreport_int(SD)
if (!is.character(model_estimates)) {
rownames(model_estimates)[rownames(model_estimates) == "log_B_dev"] <- paste0("log_B_dev_", names(FMort)[as.logical(obj$env$data$est_B_dev)])
}
model_name <- "Surplus Production"
if (state_space) model_name <- paste(model_name, "(State-Space)")
output <- list(model = model_name, current_status = current_status,
input_parameters = input_parameters, derived_quantities = derived,
model_estimates = model_estimates,
log_likelihood = matrix(NLL, ncol = 1, dimnames = list(names(NLL), "Neg.LL")))
return(output)
}
rmd_SP <- function(Assessment, state_space = FALSE, ...) {
if (state_space) {
ss <- rmd_summary("Surplus Production (State-Space)")
} else ss <- rmd_summary("Surplus Production")
# Data section
data_section <- c(rmd_data_timeseries("Catch", header = "## Data\n"),
rmd_data_timeseries("Index", is_matrix = is.matrix(Assessment@Obs_Index), nsets = ncol(Assessment@Obs_Index)))
# Assessment
#### Pars and Fit
assess_fit <- c(rmd_FMSY(header = "## Assessment {.tabset}\n### Estimates and Model Fit\n"), rmd_MSY(),
rmd_F_FMSY_terminal(), rmd_B_BMSY_terminal(), rmd_B_B0_terminal(),
rmd_assess_fit_series(nsets = ncol(Assessment@Index)),
rmd_assess_fit("Catch", "catch", match = TRUE))
if (state_space) {
assess_fit2 <- c(rmd_residual("Dev", fig.cap = "Time series of biomass deviations.", label = Assessment@Dev_type),
rmd_residual("Dev", "SE_Dev", fig.cap = "Time series of biomass deviations with 95% confidence intervals.",
label = Assessment@Dev_type, conv_check = TRUE))
assess_fit <- c(assess_fit, assess_fit2)
}
#### Time Series
ts_output <- c(rmd_F(header = "### Time Series Output\n"), rmd_F_FMSY(FALSE), rmd_B(), rmd_B_BMSY(FALSE),
rmd_B_B0(FALSE), rmd_dynamic_SSB0("TMB_report$dynamic_SSB0"),
rmd_Kobe("B_BMSY", xlab = "expression(B/B[MSY])", conv_check = FALSE))
productivity <- c(rmd_yield_F("SP", FALSE, header = "### Productivity\n"), rmd_yield_depletion("SP", FALSE), rmd_sp(FALSE))
return(c(ss, data_section, assess_fit, ts_output, productivity))
}
profile_likelihood_SP <- function(Assessment, ...) {
dots <- list(...)
if (!"FMSY" %in% names(dots) && !"MSY" %in% names(dots)) stop("Sequence of neither FMSY nor MSY was found. See help file.")
if (!is.null(dots$FMSY)) FMSY <- dots$FMSY else {
FMSY <- Assessment@FMSY
profile_par <- "MSY"
}
if (!is.null(dots$MSY)) MSY <- dots$MSY else {
MSY <- Assessment@MSY
profile_par <- "FMSY"
}
map <- Assessment@obj$env$map
params <- Assessment@info$params
profile_grid <- expand.grid(FMSY = FMSY, MSY = MSY)
joint_profile <- !exists("profile_par")
profile_fn <- function(i, Assessment, params, map) {
params$log_FMSY <- log(profile_grid[i, 1])
params$MSYx <- log(profile_grid[i, 2] * Assessment@obj$env$data$rescale)
if (joint_profile && length(Assessment@obj$par) == 2) {
nll <- Assessment@obj$fn(x = c(params$log_FMSY, params$MSYx))
} else {
if (joint_profile) map$MSYx <- map$log_FMSY <- factor(NA) else {
if (profile_par == "MSY") map$MSYx <- factor(NA) else map$log_FMSY <- factor(NA)
}
obj2 <- MakeADFun(data = Assessment@info$data, parameters = params, map = map,
random = Assessment@obj$env$random, DLL = "SAMtool", silent = TRUE)
high_F <- try(obj2$report(c(obj2$par, obj2$env$last.par[obj$env$random]))$penalty > 0 ||
any(is.na(obj2$report(c(obj2$par, obj2$env$last.par[obj$env$random]))$F)), silent = TRUE)
if (!is.character(high_F) && !is.na(high_F) && high_F) {
for(ii in 1:10) {
if (profile_par == "MSY") {
obj2$par["log_FMSY"] <- -0.5 + obj2$par["log_FMSY"]
} else {
obj2$par["MSYx"] <- 0.5 + obj2$par["MSYx"]
}
if (obj2$report(c(obj2$par, obj2$env$last.par[obj2$env$random]))$penalty > 0) break
}
}
opt2 <- optimize_TMB_model(obj2, Assessment@info$control, do_sd = FALSE)[[1]]
if (!is.character(opt2)) nll <- opt2$objective else nll <- NA
}
return(nll)
}
nll <- vapply(1:nrow(profile_grid), profile_fn, numeric(1), Assessment = Assessment, params = params, map = map) - Assessment@opt$objective
profile_grid$nll <- nll
if (joint_profile) {
pars <- c("FMSY", "MSY")
MLE <- vapply(pars, function(x, y) slot(y, x), y = Assessment, numeric(1))
} else {
pars <- profile_par
MLE <- slot(Assessment, pars)
}
output <- new("prof", Model = Assessment@Model, Name = Assessment@Name, Par = pars, MLE = MLE, grid = profile_grid)
return(output)
}
retrospective_SP <- function(Assessment, nyr, state_space = FALSE) {
assign_Assessment_slots(Assessment)
ny <- info$data$ny
Year <- info$Year
Year <- c(Year, max(Year) + 1)
# Array dimension: Retroyr, Year, ts
# ts includes: F, F/FMSY, B, B/BMSY, B/B0
retro_ts <- array(NA, dim = c(nyr + 1, ny + 1, 5))
TS_var <- c("F", "F_FMSY", "B", "B_BMSY", "B_B0")
dimnames(retro_ts) <- list(Peel = 0:nyr, Year = Year, Var = TS_var)
retro_est <- array(NA, dim = c(nyr + 1, length(SD$par.fixed[names(SD$par.fixed) != "log_B_dev"]), 2))
dimnames(retro_est) <- list(Peel = 0:nyr, Var = names(SD$par.fixed)[names(SD$par.fixed) != "log_B_dev"],
Value = c("Estimate", "Std. Error"))
lapply_fn <- function(i, info, obj, state_space) {
ny_ret <- ny - i
info$data$ny <- ny_ret
info$data$C_hist <- info$data$C_hist[1:ny_ret]
info$data$I_hist <- info$data$I_hist[1:ny_ret, , drop = FALSE]
info$data$I_sd <- info$data$I_sd[1:ny_ret, , drop = FALSE]
map <- obj$env$map
if (state_space) {
info$data$est_B_dev <- info$data$est_B_dev[1:ny_ret]
info$params$log_B_dev <- rep(0, ny_ret)
map$log_B_dev <- obj$env$map$log_B_dev[1:ny_ret]
}
obj2 <- MakeADFun(data = info$data, parameters = info$params, map = map, random = obj$env$random,
DLL = "SAMtool", silent = TRUE)
mod <- optimize_TMB_model(obj2, info$control)
opt2 <- mod[[1]]
SD <- mod[[2]]
if (!is.character(opt2)) {
report <- obj2$report(obj2$env$last.par.best)
FMort <- c(report$F, rep(NA, 1 + i))
F_FMSY <- FMort/report$FMSY
B <- c(report$B, rep(NA, i))
B_BMSY <- B/report$BMSY
B_B0 <- B/report$K
retro_ts[i+1, , ] <<- cbind(FMort, F_FMSY, B, B_BMSY, B_B0)
sumry <- summary(SD, "fixed")
sumry <- sumry[rownames(sumry) != "log_B_dev", drop = FALSE]
retro_est[i+1, , ] <<- sumry
return(SD$pdHess)
}
return(FALSE)
}
conv <- vapply(0:nyr, lapply_fn, logical(1), info = info, obj = obj, state_space = state_space)
if (any(!conv)) warning("Peels that did not converge: ", paste0(which(!conv) - 1, collapse = " "))
retro <- new("retro", Model = Assessment@Model, Name = Assessment@Name, TS_var = TS_var, TS = retro_ts,
Est_var = dimnames(retro_est)[[2]], Est = retro_est)
attr(retro, "TS_lab") <- c("Fishing mortality", expression(F/F[MSY]), "Biomass", expression(B/B[MSY]), expression(B/B[0]))
return(retro)
}
summary_SP_SS <- function(Assessment) summary_SP(Assessment, TRUE)
rmd_SP_SS <- function(Assessment, ...) rmd_SP(Assessment, TRUE, ...)
profile_likelihood_SP_SS <- profile_likelihood_SP
retrospective_SP_SS <- function(Assessment, nyr) retrospective_SP(Assessment, nyr, TRUE)
plot_yield_SP <- function(data = NULL, report, fmsy, msy, xaxis = c("F", "Biomass", "Depletion"), relative_yaxis = FALSE) {
BKratio <- seq(0, 1, 0.01)
K <- report$K
n <- report$n
BMSY <- report$BMSY
if (n == 1) {
Yield <- ifelse(BKratio == 0, 0, -exp(1) * msy * BKratio * log(BKratio))
} else {
gamma.par <- n^(n/(n-1))/(n-1)
Yield <- gamma.par * msy * (BKratio - BKratio^n)
}
Biomass <- BKratio * K
F.vector <- Yield/Biomass
if (relative_yaxis) {
Yield <- Yield/max(Yield)
ylab <- "Relative Equilibrium Yield"
} else ylab <- "Equilibrium Yield"
if (xaxis == "F") {
plot(F.vector, Yield, typ = 'l', xlab = "Fishing Mortality F", ylab = ylab)
segments(x0 = fmsy, y0 = 0, y1 = max(Yield), lty = 2)
segments(x0 = 0, y0 = max(Yield), x1 = fmsy, lty = 2)
abline(h = 0, col = 'grey')
}
if (xaxis == "Biomass") {
plot(Biomass, Yield, typ = 'l', xlab = "Biomass", ylab = ylab)
segments(x0 = BMSY, y0 = 0, y1 = max(Yield), lty = 2)
segments(x0 = 0, y0 = max(Yield), x1 = BMSY, lty = 2)
abline(h = 0, col = 'grey')
}
if (xaxis == "Depletion") {
plot(BKratio, Yield, typ = 'l', xlab = expression(B/B[0]), ylab = ylab)
segments(x0 = BMSY/K, y0 = 0, y1 = max(Yield), lty = 2)
segments(x0 = 0, y0 = max(Yield), x1 = BMSY/K, lty = 2)
abline(h = 0, col = 'grey')
}
invisible(data.frame(F = F.vector, Yield = Yield, B = Biomass, B_B0 = BKratio))
}
#' Find the production parameter based on depletion that produces MSY
#'
#' For surplus production models, this function returns the production exponent n corresponding
#' to BMSY/K (Fletcher 1978).
#'
#' @param depletion The hypothesized depletion that produces MSY.
#' @param figure Local, plots figure of production function as a function of depletion (B/K)
#'
#' @author Q. Huynh
#' @references
#' Fletcher, R. I. 1978. On the restructuring of the Pella-Tomlinson system. Fishery Bulletin 76:515:521.
#' @note May be useful for parameterizing `n` in [SP] and [SP_SS].
#' @examples SP_production(0.5)
#' @return The production function exponent n (numeric).
#' @examples
#' SP_production(0.5)
#' @seealso [SP] [SP_SS]
#' @export SP_production
SP_production <- function(depletion, figure = TRUE) {
if (length(depletion) > 1) {
depletion <- depletion[1]
message_oops(paste("Function is not vectorized. Depletion value of", depletion, "is used."))
}
if (depletion <= 0 || depletion >= 1) stop(paste("Proposed depletion =", depletion, "but value must be between 0 and 1."))
calc_depletion <- function(n) ifelse(n == 1, 1/exp(1), n^(1/(1-n))) # Depletion at BMSY
n_solver <- function(x) calc_depletion(x) - depletion
get_n <- uniroot(f = n_solver, interval = c(0, 1e3))
n_answer <- round(get_n$root, 3)
if (figure) {
fmsy <- 0.1
msy <- fmsy * depletion
plot_yield_SP(report = list(n = n_answer, BMSY = depletion, K = 1), fmsy = fmsy,
msy = msy, xaxis = "Depletion", relative_yaxis = TRUE)
title(paste0("Production exponent n = ", n_answer))
}
return(n_answer)
}
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