summary_DD_TMB <- function(Assessment, state_space = FALSE) {
assign_Assessment_slots()
if(conv) current_status <- c(U_UMSY[length(U_UMSY)], B_BMSY[length(B_BMSY)], B_B0[length(B_B0)])
else current_status <- c(NA, NA, B_B0[length(B_B0)])
current_status <- data.frame(Value = current_status)
rownames(current_status) <- c("U/UMSY", "B/BMSY", "B/B0")
Value <- c(unlist(info$data[c(2,3,4,6,7)]))
Description <- c("Unfished survival = exp(-M)", "alpha = Winf * (1-rho)",
"rho = (W_k+2 - Winf)/(W_k+1 - Winf)",
"Age of knife-edge selectivity",
"Weight at age k")
rownam <- c("S0", "alpha", "rho", "k", "w_k")
if(Assessment@obj$env$data$condition == "effort" && "log_omega" %in% names(obj$env$map)) {
Value <- c(Value, TMB_report$omega)
Description <- c(Description, "Catch SD (log-space)")
rownam <- c(rownam, "omega")
}
if(state_space && "log_tau" %in% names(obj$env$map)) {
Value <- c(Value, TMB_report$tau)
Description <- c(Description, "log-Recruitment deviation SD")
rownam <- c(rownam, "tau")
}
if("transformed_h" %in% names(obj$env$map)) {
Value <- c(Value, h)
Description <- c(Description, "Stock-recruit steepness")
rownam <- c(rownam, "h")
}
input_parameters <- data.frame(Value = Value, Description = Description, stringsAsFactors = FALSE)
rownames(input_parameters) <- rownam
if(conv) derived <- c(B0, N0, MSY, UMSY, BMSY, BMSY/B0)
else derived <- rep(NA, 6)
derived <- data.frame(Value = derived,
Description = c("Unfished biomass", "Unfished abundance", "Maximum sustainable yield (MSY)",
"Harvest rate at MSY", "Biomass at MSY", "Depletion at MSY"),
stringsAsFactors = FALSE)
rownames(derived) <- c("B0", "N0", "MSY", "UMSY", "BMSY", "BMSY/B0")
model_estimates <- sdreport_int(SD)
if(!is.character(model_estimates)) {
rownames(model_estimates)[rownames(model_estimates) == "log_rec_dev"] <- paste0("log_rec_dev_", names(Dev))
}
model_name <- "Delay-Difference"
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_DD_TMB <- function(Assessment, state_space = FALSE, ...) {
if(state_space) {
ss <- rmd_summary("Delay-Difference (State-Space)")
} else ss <- rmd_summary("Delay-Difference")
# Life History
age <- 1:Assessment@info$LH$maxage
k <- Assessment@info$data$k
mat <- ifelse(age < k, 0, 1)
LH_section <- c(rmd_LAA(age, Assessment@info$LH$LAA, header = "## Life History\n"), rmd_WAA(age, Assessment@info$LH$WAA),
rmd_LW(Assessment@info$LH$LAA, Assessment@info$LH$WAA),
rmd_mat(age, mat, fig.cap = "Assumed knife-edge maturity at age corresponding to length of 50% maturity."))
# 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_all <- c(rmd_R0(header = "## Assessment {.tabset}\n### Estimates and Model Fit\n"), rmd_h(),
rmd_sel(age, mat, fig.cap = "Knife-edge selectivity set to the age corresponding to the length of 50% maturity."))
if(Assessment@obj$env$data$condition == "effort") {
assess_data <- c(rmd_assess_fit("Catch", "catch"), rmd_assess_resid("Catch"), rmd_assess_qq("Catch", "catch"))
} else {
assess_data <- rmd_assess_fit_series(nsets = ncol(Assessment@Index))
}
assess_fit <- c(assess_all, assess_data)
if(state_space) {
assess_fit2 <- c(rmd_residual("Dev", fig.cap = "Time series of recruitment deviations.", label = Assessment@Dev_type),
rmd_residual("Dev", "SE_Dev", fig.cap = "Time series of recruitment deviations with 95% confidence intervals.",
label = Assessment@Dev_type, conv_check = TRUE))
assess_fit <- c(assess_fit, assess_fit2)
}
#### Time Series
ts_output <- c(rmd_U(header = "### Time Series Output\n"), rmd_U_UMSY(), rmd_SSB(), rmd_SSB_SSBMSY(),
rmd_SSB_SSB0(), rmd_Kobe("SSB_SSBMSY", "U_UMSY", xlab = "expression(SSB/SSB[MSY])", ylab = "expression(U/U[MSY])"),
rmd_R(), rmd_N())
#### Productivity
ny <- Assessment@info$data$ny
SSB <- Assessment@SSB[1:ny]
Arec <- Assessment@TMB_report$Arec
Brec <- Assessment@TMB_report$Brec
if(Assessment@info$data$SR_type == "BH") expectedR <- Arec * SSB / (1 + Brec * SSB) else {
expectedR <- Arec * SSB * exp(-Brec * SSB)
}
first_recruit_year <- k + 1
last_recruit_year <- length(Assessment@info$Year) + k
ind_recruit <- first_recruit_year:last_recruit_year
rec_dev <- Assessment@R[ind_recruit]
productivity <- c(rmd_SR(SSB, expectedR, rec_dev, header = "### Productivity\n\n\n"),
rmd_SR(SSB, expectedR, rec_dev, fig.cap = "Stock-recruit relationship (trajectory plot).", trajectory = TRUE),
rmd_yield_U("DD"), rmd_yield_depletion("DD"), rmd_sp())
return(c(ss, LH_section, data_section, assess_fit, ts_output, productivity))
}
profile_likelihood_DD_TMB <- function(Assessment, ...) {
dots <- list(...)
if(!"R0" %in% names(dots) && !"h" %in% names(dots)) stop("Sequence of neither R0 nor h was not found. See help file.")
if(!is.null(dots$R0)) R0 <- dots$R0 else {
R0 <- Assessment@R0
profile_par <- "h"
}
if(!is.null(dots$h)) h <- dots$h else {
h <- Assessment@h
profile_par <- "R0"
}
map <- Assessment@obj$env$map
params <- Assessment@info$params
profile_grid <- expand.grid(R0 = R0, h = h)
joint_profile <- !exists("profile_par")
profile_fn <- function(i, Assessment, params, map) {
params$R0x <- log(profile_grid[i, 1] * Assessment@obj$env$data$rescale)
if(Assessment@info$data$SR_type == "BH") {
params$transformed_h <- logit((profile_grid[i, 2] - 0.2)/0.8)
} else {
params$transformed_h <- log(profile_grid[i, 2] - 0.2)
}
if(joint_profile) {
map$R0x <- map$transformed_h <- factor(NA)
} else {
if(profile_par == "R0") map$R0x <- factor(NA) else map$transformed_h <- factor(NA)
}
obj2 <- MakeADFun(data = Assessment@info$data, parameters = params, map = map, random = Assessment@obj$env$random,
DLL = "MSEtool", silent = TRUE)
opt2 <- optimize_TMB_model(obj2, Assessment@info$control)[[1]]
if(!is.character(opt2)) nll <- opt2$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("R0", "h")
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_DD_TMB <- function(Assessment, nyr, state_space = FALSE) {
assign_Assessment_slots(Assessment)
ny <- info$data$ny
k <- info$data$k
Year <- info$Year
moreRecruitYears <- max(Year) + 1:k
Year <- c(Year, moreRecruitYears)
# Array dimension: Retroyr, Year, ts
# ts includes: U, U/UMSY, B, B/BMSY, B/B0, R, VB
retro_ts <- array(NA, dim = c(nyr+1, ny+k, 7))
TS_var <- c("U", "U_UMSY", "B", "B_BMSY", "B_B0", "R", "VB")
dimnames(retro_ts) <- list(Peel = 0:nyr, Year = Year, Var = TS_var)
retro_est <- array(NA, dim = c(nyr+1, length(SD$par.fixed[names(SD$par.fixed) != "log_rec_dev"]), 2))
dimnames(retro_est) <- list(Peel = 0:nyr, Var = names(SD$par.fixed)[names(SD$par.fixed) != "log_rec_dev"],
Value = c("Estimate", "Std. Error"))
lapply_fn <- function(i, info, obj, state_space) {
ny_ret <- ny - i
info$data$ny <- ny_ret
info$data$C_hist <- info$data$C_hist[1:ny_ret]
info$data$E_hist <- info$data$E_hist[1:ny_ret]
info$data$I_hist <- info$data$I_hist[1:ny_ret, , drop = FALSE]
info$data$I_sd <- info$data$I_sd[1:ny_ret, , drop = FALSE]
if(state_space) info$params$log_rec_dev <- rep(0, ny_ret - k)
obj2 <- MakeADFun(data = info$data, parameters = info$params, random = obj$env$random, map = obj$env$map,
inner.control = info$inner.control, DLL = "MSEtool", silent = TRUE)
mod <- optimize_TMB_model(obj2, info$control)
opt2 <- mod[[1]]
SD <- mod[[2]]
if(!is.character(opt2) && !is.character(SD)) {
report <- obj2$report(obj2$env$last.par.best)
ref_pt <- get_MSY_DD(info$data, report$Arec, report$Brec)
report <- c(report, ref_pt)
U <- c(report$U, rep(NA, k + i))
U_UMSY <- U/report$UMSY
B <- c(report$B, rep(NA, k - 1 + i))
B_BMSY <- B/report$BMSY
B_B0 <- B/B0
R <- c(report$R, rep(NA, i))
VB <- B
retro_ts[i+1, , ] <<- cbind(U, U_UMSY, B, B_BMSY, B_B0, R, VB)
sumry <- summary(SD, "fixed")
sumry <- sumry[rownames(sumry) != "log_rec_dev", drop = FALSE]
retro_est[i+1, , ] <<- sumry
return(SD$pdHess)
}
return(FALSE)
}
conv <- vapply(0:nyr, lapply_fn, logical(1), info = info, obj = obj, state_space = state_space)
if(any(!conv)) warning("Peels that did not converge: ", paste0(which(!conv) - 1, collapse = " "))
retro <- new("retro", Model = Assessment@Model, Name = Assessment@Name, TS_var = TS_var, TS = retro_ts,
Est_var = dimnames(retro_est)[[2]], Est = retro_est)
attr(retro, "TS_lab") <- c("Harvest rate", expression(U/U[MSY]), "Biomass", expression(B/B[MSY]), expression(B/B[0]),
"Recruitment", "Vulnerable biomass")
return(retro)
}
summary_DD_SS <- function(Assessment) summary_DD_TMB(Assessment, TRUE)
rmd_DD_SS <- function(Assessment, ...) rmd_DD_TMB(Assessment, TRUE, ...)
profile_likelihood_DD_SS <- profile_likelihood_DD_TMB
retrospective_DD_SS <- function(Assessment, nyr) retrospective_DD_TMB(Assessment, nyr, TRUE)
plot_yield_DD <- function(data, report, umsy, msy, xaxis = c("U", "Biomass", "Depletion")) {
xaxis <- match.arg(xaxis)
u.vector <- seq(0, 1, 0.01)
S0 <- data$S0
Alpha <- data$Alpha
wk <- data$wk
Rho <- data$Rho
SR_type <- data$SR_type
Arec <- report$Arec
Brec <- report$Brec
BMSY <- report$BMSY
Surv <- S0 * (1 - u.vector)
BPR <- (Surv * Alpha/(1 - Surv) + wk)/(1 - Rho * Surv)
if(SR_type == "BH") R <- (Arec * BPR - 1)/(Brec * BPR)
if(SR_type == "Ricker") R <- log(Arec * BPR)/(Brec * BPR)
Biomass <- BPR * R
Yield <- u.vector * BPR * R
ind <- R >= 0
if(xaxis == "U") {
plot(u.vector[ind], Yield[ind], typ = 'l', xlab = "Exploitation rate (U)",
ylab = "Equilibrium yield")
segments(x0 = umsy, y0 = 0, y1 = msy, lty = 2)
segments(x0 = 0, y0 = msy, x1 = umsy, lty = 2)
abline(h = 0, col = 'grey')
}
if(xaxis == "Biomass") {
plot(Biomass[ind], Yield[ind], typ = 'l', xlab = "Biomass",
ylab = "Equilibrium yield")
segments(x0 = BMSY, y0 = 0, y1 = msy, lty = 2)
segments(x0 = 0, y0 = msy, x1 = BMSY, lty = 2)
abline(h = 0, col = 'grey')
}
if(xaxis == "Depletion") {
plot(Biomass[ind]/report$B0, Yield[ind], typ = 'l',
xlab = expression(B/B[0]), ylab = "Equilibrium yield")
segments(x0 = BMSY/report$B0, y0 = 0, y1 = msy, lty = 2)
segments(x0 = 0, y0 = msy, x1 = BMSY/report$B0, lty = 2)
abline(h = 0, col = 'grey')
}
invisible(data.frame(U = u.vector[ind], Yield = Yield[ind], B = Biomass[ind], B_B0 = Biomass[ind]/report$B0))
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.