summary_VPA <- function(Assessment) {
assign_Assessment_slots(Assessment)
if(conv) current_status <- c(F_FMSY[length(F_FMSY)], SSB_SSBMSY[length(SSB_SSBMSY)], SSB_SSB0[length(SSB_SSB0)])
else current_status <- rep(NA, 3)
current_status <- data.frame(Value = current_status)
rownames(current_status) <- c("F/FMSY", "SSB/SSBMSY", "SSB/SSB0")
Value <- c(h, info$data$M[1], min(info$ages), max(info$ages), info$LH$Linf, info$LH$K, info$LH$t0,
info$LH$a * info$LH$Linf ^ info$LH$b, info$LH$A50, info$LH$A95)
Description = c("Stock-recruit steepness", "Natural mortality", "Minimum age (minus-group)", "Maximum age (plus-group)", "Asymptotic length",
"Growth coefficient", "Age at length-zero", "Asymptotic weight", "Age of 50% maturity", "Age of 95% maturity")
rownam <- c("h", "M", "minage", "maxage", "Linf", "K", "t0", "Winf", "A50", "A95")
input_parameters <- data.frame(Value = Value, Description = Description, stringsAsFactors = FALSE)
rownames(input_parameters) <- rownam
if(!info$fix_h) input_parameters <- input_parameters[-1, ]
if(conv) Value <- c(VB0, SSB0, MSY, FMSY, VBMSY, SSBMSY, SSBMSY/SSB0)
else Value <- rep(NA, 7)
Description <- c("Unfished vulnerable biomass",
"Unfished spawning stock biomass (SSB)", "Maximum sustainable yield (MSY)", "Fishing mortality at MSY",
"Vulnerable biomass at MSY", "SSB at MSY", "Spawning depletion at MSY")
derived <- data.frame(Value = Value, Description = Description, stringsAsFactors = FALSE)
rownames(derived) <- c("VB0", "SSB0", "MSY", "UMSY", "VBMSY", "SSBMSY", "SSBMSY/SSB0")
model_estimates <- sdreport_int(SD)
output <- list(model = "Virtual Population Analysis (VPA)",
current_status = current_status, input_parameters = input_parameters,
derived_quantities = derived, model_estimates = model_estimates,
log_likelihood = matrix(NLL, ncol = 1, dimnames = list(names(NLL), "Neg.LL")))
return(output)
}
class(VPA) <- "Assessment"
rmd_VPA <- function(Assessment, ...) {
ss <- rmd_summary("Virtual Population Analysis (VPA)")
# Life History
age <- Assessment@info$ages
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, Assessment@info$data$mat, fig.cap = "Maturity at age. Length-based maturity parameters were converted to the corresponding ages."))
# Data section
data_section <- c(rmd_data_timeseries("Catch", header = "## Data\n"), rmd_data_timeseries("Index"),
rmd_data_age_comps("bubble", ages = vector2char(age)),
rmd_data_age_comps("annual", ages = vector2char(age), annual_yscale = "\"raw\"", annual_ylab = "\"Catch-at-age\""))
# Assessment
#### Pars and Fit
assess_fit <- c("## Assessment {.tabset}\n### Estimates and Model Fit\n",
rmd_sel(age, Assessment@Selectivity[nrow(Assessment@Selectivity), ], fig.cap = "Estimated selectivity at age."),
rmd_assess_fit("Index", "index"), rmd_assess_resid("Index"), rmd_assess_qq("Index", "index"),
rmd_fit_age_comps("annual", ages = vector2char(age), match = TRUE))
#### Time Series
ts_output <- c(rmd_F(header = "### Time Series Output\n", fig.cap = "apical fishing mortality"), rmd_F_FMSY(),
rmd_sel_annual(age), rmd_sel_persp(age),
rmd_U(fig.cap = "harvest rate (ratio of catch and vulnerable biomass)"),
rmd_U_UMSY(fig.cap = "U/UMSY, where UMSY = MSY/VBMSY"),
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(), rmd_N_at_age(), rmd_C_at_age(), rmd_C_mean_age())
# Productivity
Arec <- Assessment@TMB_report$Arec
Brec <- Assessment@TMB_report$Brec
SSB <- Assessment@SSB[1:(length(Assessment@SSB)-1)]
SR <- Assessment@info$SR
if(SR == "BH") expectedR <- Arec * SSB / (1 + Brec * SSB) else {
expectedR <- Arec * SSB * exp(-Brec * SSB)
}
estR <- Assessment@R[as.numeric(names(Assessment@R)) > Assessment@info$Year[1]]
productivity <- c(rmd_SR(SSB, expectedR, estR, ylab = paste0("Recruitment (age- ", min(age), ")"),
header = "### Productivity\n\n\n", conv_check = TRUE),
rmd_SR(SSB, expectedR, estR, ylab = paste0("Recruitment (age- ", min(age), ")"),
fig.cap = "Stock-recruit relationship (trajectory plot).", trajectory = TRUE, conv_check = TRUE),
rmd_yield_F("VPA"), rmd_yield_depletion("VPA"), rmd_sp(depletion = FALSE))
return(c(ss, LH_section, data_section, assess_fit, ts_output, productivity))
}
plot_yield_VPA <- function(data, report, fmsy, msy, xaxis = c("F", "Biomass", "Depletion")) {
plot_yield_SCA(data = data, report = report, fmsy = fmsy, msy = msy, xaxis = xaxis)
}
#' @importFrom reshape2 acast
profile_likelihood_VPA <- function(Assessment, figure = TRUE, save_figure = TRUE, save_dir = tempdir(), ...) {
dots <- list(...)
if(!"F_term" %in% names(dots)) stop("Sequence of F_term was not found. See help file.")
F_term <- dots$F_term
params <- Assessment@info$params
map <- Assessment@obj$env$map
map$logF_term <- factor(NA)
profile_fn <- function(i, Assessment, params, map) {
params$logF_term <- log(F_term[i])
obj2 <- MakeADFun(data = Assessment@info$data, parameters = params, map = map, DLL = "MSEtool", silent = TRUE)
opt2 <- optimize_TMB_model(obj2, Assessment@info$control)[[1]]
if(!is.character(opt2)) nll <- opt2$objective else nll <- NA
return(nll)
}
nll <- vapply(1:length(F_term), profile_fn, numeric(1), Assessment = Assessment, params = params, map = map) - Assessment@opt$objective
profile_grid <- data.frame(F_term = F_term, nll = nll)
pars <- "F_term"
MLE <- Assessment@SD$value["F_term"]
output <- new("prof", Model = Assessment@Model, Name = Assessment@Name, Par = pars, MLE = MLE, grid = profile_grid)
return(output)
}
#' @importFrom gplots rich.colors
retrospective_VPA <- function(Assessment, nyr) {
assign_Assessment_slots(Assessment)
n_y <- info$data$n_y
Year <- c(info$Year, max(info$Year) + 1)
# Array dimension: Retroyr, Year, ts
# ts includes: F, U, SSB, R, VB
retro_ts <- array(NA, dim = c(nyr+1, n_y + 1, 5))
TS_var <- c("F", "U", "SSB", "R", "VB")
dimnames(retro_ts) <- list(Peel = 0:nyr, Year = Year, Var = TS_var)
retro_est <- array(NA, dim = c(nyr+1, dim(summary(SD))))
dimnames(retro_est) <- list(Peel = 0:nyr, Var = rownames(summary(SD)), Value = c("Estimate", "Std. Error"))
#fix_h <- ifelse(is.null(info$h), FALSE, TRUE)
lapply_fn <- function(i, info, obj) {
n_y_ret <- n_y - i
info$data$n_y <- n_y_ret
info$data$I_hist <- info$data$I_hist[1:n_y_ret]
info$data$CAA_hist <- info$data$CAA_hist[1:n_y_ret, ]
obj2 <- MakeADFun(data = info$data, parameters = info$params, map = obj$env$map, 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) %>% projection_VPA_internal(info, info$data$n_Rpen)
FMort <- c(apply(report$F, 1, max), rep(NA, i + 1))
Z_mat <- t(report$F) + info$data$M
VB_mid <- t(report$N[-ncol(report$N), ]) * (1 - exp(-Z_mat))/Z_mat
U <- c(colSums(t(report$CAApred) * info$data$weight)/colSums(VB_mid), rep(NA, i + 1))
SSB <- c(report$E, rep(NA, i))
R <- c(report$N[, 1], rep(NA, i))
VB <- c(report$VB, rep(NA, i))
retro_ts[i+1, , ] <<- cbind(FMort, U, SSB, R, VB)
retro_est[i+1, , ] <<- summary(SD)
return(SD$pdHess)
}
return(FALSE)
}
conv <- vapply(0:nyr, lapply_fn, logical(1), info = info, obj = obj)
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("Apical fishing mortality", "Harvest rate", "Spawning biomass", "Recruitment", "Vulnerable biomass")
return(retro)
}
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