R/MSE_Plotting.R
In Blue-Matter/MSEtool: Management Strategy Evaluation Toolkit
Defines functions
Pplot2
Pplot
NOAA_plot
Kplot
join_plots
plot.MSE
Documented in
join_plots
Kplot
NOAA_plot
plot.MSE
Pplot
Pplot2
# ---- Plot MSE object ----
#' Plot MSE object
#' @param x object of class MSE
#' @param ... other parameters passed to plot (currently ignored)
#' @method plot MSE
#' @export
plot.MSE <- function(x, ...) {
Pplot(x, nam = deparse(substitute(x)))
Kplot(x)
Tplot(x, nam = deparse(substitute(x)))
}
#' Plot several plots with a shared legend
#'
#' @param plots list of plot objects of class `gg` or `ggplot`
#' @param ncol Optional number of columns
#' @param nrow Optional number of rows
#' @param position position of the legend ("bottom" or "right")
#' @param legend Logical. Use a legend?
#' @export
#'
#' @note modified from https://github.com/tidyverse/ggplot2/wiki/share-a-legend-between-two-ggplot2-graphs
join_plots <- function(plots, ncol = length(plots), nrow = 1, position = c("right", "bottom"),
legend=TRUE) {
if (!requireNamespace("gridExtra", quietly = TRUE)) {
stop("Package \"gridExtra\" needed for this function to work. Please install it.",
call. = FALSE)
}
position <- match.arg(position)
if (legend) {
g <- ggplot2::ggplotGrob(plots[[1]] + ggplot2::theme(legend.position = position))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
lwidth <- sum(legend$width)
gl <- lapply(plots, function(x) x + ggplot2::theme(legend.position="none"))
gl <- c(gl, ncol = ncol, nrow = nrow)
combined <- switch(position,
"bottom" = gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl),
legend,
ncol = 1,
heights = grid::unit.c(grid::unit(1, "npc") - lheight, lheight)),
"right" = gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl),
legend,
ncol = 2,
widths = grid::unit.c(grid::unit(1, "npc") - lwidth, lwidth)))
} else {
gl <- lapply(plots, function(x) x + ggplot2::theme(legend.position="none"))
gl <- c(gl, ncol = ncol, nrow = nrow)
combined <- gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl))
}
grid::grid.newpage()
grid::grid.draw(combined)
# return gtable invisibly
invisible(combined)
}
# #' Plot a barplot of MSE results
# #'
# #' @param height An object of class MSE. Generic function must have argument
# #' height. But note that this must be an MSE object.
# #' @param MSEobj Optional. An object of class MSE. Overides \code{height}
# #' @param PMs List of performance metrics. Options are \code{c('SSB_SSB0',
# #' 'SB_SBMSY', 'F_FMSY', 'AAVE', 'AAVY')}
# #' @param PLim Probability threshold
# #' @param lastYrs Last number of years in projection to calculate statistics
# #' @param maxMP Maximum number of MPs to include in each plot
# #' @param MPs Optional subset MSE object by MP
# #' @param Title Optional title for plot
# #' @param sims Optional subset MSE object by simulation
# #' @param msg Logical. Print out messages?
# #' @param incRef Logical. Include the reference methods?
# #' @param cex.names Size of names
# #' @param ... Optional additional arguments passed to \code{barplot}
# #' @author A. Hordyk
# #' @export
# barplot.MSE <- function(height, MSEobj = NULL, PMs = list(SB_SBMSY = 0.5,
# SSB_SSB0 = 0.2), PLim = 0.8, lastYrs = 10, maxMP = 14, MPs = NA, Title = NULL,
# sims = NULL, msg = TRUE, cex.names = 1.3, incRef = FALSE, ...) {
#
# MSEobj <- match.arg(MSEobj)
# if (is.null(MSEobj))
# MSEobj <- height
# if (!is.null(sims) & all(is.na(MPs)))
# MSEobj <- Sub(MSEobj, sims = sims)
# if (!is.null(sims) & all(!is.na(MPs)))
# MSEobj <- Sub(MSEobj, sims = sims, MPs = MPs)
# if (is.null(sims) & !all(is.na(MPs)))
# MSEobj <- Sub(MSEobj, MPs = MPs)
#
# if (!incRef) {
# mps <- MSEobj@MPs[!grepl("ref", MSEobj@MPs)]
# MSEobj <- Sub(MSEobj, MPs = mps)
# }
# DF <- list()
# if (length(lastYrs) > 1) {
# for (xx in seq_along(lastYrs)) DF[[xx]] <- MPStats(MSEobj, PMRefs = PMs,
# lastYrs = lastYrs[xx], UseMean = TRUE, msg = msg)$Perf
# } else {
# DF[[1]] <- MPStats(MSEobj, PMRefs = PMs, lastYrs = lastYrs, UseMean = TRUE,
# msg = msg)$Perf
# }
# lastYrs[lastYrs >= MSEobj@proyears] <- 10
# PosPMs <- c("SSB_SSB0", "SB_SBMSY", "F_FMSY", "AAVE", "AAVY")
# PMNames <- names(PMs)
# ind <- match(PMNames, PosPMs)
#
# pms <- paste0(names(PMs), "p")
#
# vars <- which(!is.na(match(names(DF[[1]]), pms)))
#
# temp <- lapply(DF, "[", vars)
# DF2 <- cbind(DF[[1]][, 1], do.call(cbind.data.frame, temp))
# names(DF2)[1] <- "MP"
#
# B0Ref <- unique(DF[[1]]$SSB_SSB0Ref)
# B_BMSYRef <- unique(DF[[1]]$SB_SBMSYRef)
# F_FMSYRef <- unique(DF[[1]]$F_FMSYRef)
# AAVERef <- unique(DF[[1]]$AAVERef)
# AAVYRef <- unique(DF[[1]]$AAVYRef)
#
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears)
#
# B0Leg <- bquote(italic(B) > ~.(B0Ref) ~ italic(B[0]))
# BMSYLeg <- bquote(italic(B) > ~.(B_BMSYRef) ~ italic(B[MSY]))
# FMSYLeg <- bquote(italic(F) < ~.(F_FMSYRef) ~ italic(F[MSY]))
# AAVELeg <- bquote(italic(AAVE) > ~.(AAVERef) ~ "%")
# AAVYRef <- bquote(italic(AAVY) > ~.(AAVYRef) ~ "%")
# LegList <- list(B0Leg, BMSYLeg, FMSYLeg, AAVELeg, AAVYRef)
# Legend <- NULL
# Legend <- append(Legend, as.expression(LegList[ind]))
#
# if (length(Years) > 1) {
# temp <- NULL
# count <- 1
# for (xx in seq_along(Years)) {
# for (yy in seq_along(LegList[ind])) {
# temp[[count]] <- bquote(.(LegList[ind][[yy]]) ~ (Years ~
# .((MSEobj@proyears - lastYrs[xx]) + 1) ~ "-" ~ .(MSEobj@proyears)))
# count <- count + 1
# }
# }
# Legend <- append(NULL, as.expression(temp))
# }
#
# MPs <- as.character(DF2$MP)
# nMPs <- length(MPs)
# if (is.null(MPs))
# stop("Dataframe must a column named `MP`")
# ndat <- ncol(DF2) - 1
# # if (length(ProbLims) != ndat) stop('Must be a probablility limit for
# # each variable')
# Probs <- t(DF2[, 2:(ndat + 1)])
# colnames(Probs) <- MPs
# if (max(Probs) <= 1)
# Probs <- Probs * 100
# ProbLims <- VLine <- PLim
# if (max(ProbLims) <= 1)
# ProbLims <- ProbLims * 100
# if (nrow(Probs) > 1) {
# Ord <- order(apply(Probs, 2, mean))
# Probs <- Probs[, Ord]
# MPnames <- colnames(Probs)
# Pass <- as.logical(apply(Probs >= ProbLims, 2, prod))
# } else {
# MPnames <- colnames(Probs)
# Ord <- order(Probs)
# Probs <- t(as.matrix(Probs[Ord, drop = FALSE]))
# MPnames <- MPnames[Ord]
# colnames(Probs) <- MPnames
# Pass <- as.logical(Probs >= ProbLims)
# }
#
# # if (length(ProbLims) == 1) Pass <- Probs > ProbLims
#
# # bcols <- brewer.pal(8, 'Dark2')[1:length(vars)]
# cols <- c("#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02",
# "#A6761D", "#666666")
# bcols <- cols[1:ndat]
#
# nplots <- ceiling(nMPs/maxMP)
# Ncol <- ceiling(sqrt(nplots))
# Nrow <- ceiling(nplots/Ncol)
# if (!(Ncol * Nrow >= nplots))
# stop("Error in number of plots (you've found a bug in the function!)")
#
# tempmat <- matrix(1:(Ncol * Nrow), nrow = Nrow, byrow = TRUE)
# bspace <- max(nchar(MPs)) - 1
#
# op <- par(mfrow = c(Nrow, Ncol), oma = c(2.5, 1, 5, 2), mar = c(3, bspace,
# 0, 0))
# if (nplots == 1) {
# barplot(Probs, beside = TRUE, horiz = TRUE, names = MPnames, las = 1,
# xlim = c(0, 100), cex.axis = 1.5, cex.lab = 2, col = bcols,
# xlab = "Probability", xpd = NA, cex.names = cex.names, ...)
# if (!is.null(VLine)) {
# if (VLine < 1)
# VLine <- VLine * 100
# if (VLine > 0)
# abline(v = VLine, lty = 2, lwd = 2, col = "gray")
# }
# rng <- par("usr")
# lg <- legend(rng[1], rng[2], legend = Legend, bty = "n", cex = 1.25,
# horiz = TRUE, xpd = NA, title = "", plot = FALSE)
# legend(rng[1], rng[4] + lg$rect$h, legend = Legend, bty = "n",
# cex = 1.25, horiz = TRUE, xpd = NA, fill = bcols, title = "")
# }
# # Split the MPs over multiple plots
# if (length(vars) == 1)
# colnames(Probs) <- MPnames
# if (nplots > 1) {
# npplot <- min(ceiling(nMPs/nplots), maxMP) # number per plot
# xx <- 1
# xx2 <- npplot
# for (X in 1:nplots) {
# # if (length(Legend) > 1) splitdat <- Probs[,xx:xx2] if (length(Legend)
# # == 1) splitdat <- Probs[,xx:xx2]
# splitdat <- Probs[, xx:xx2]
# if (X == 1) {
# tt <- barplot(splitdat, beside = TRUE, ylab = "", xlim = c(0,
# 100), col = bcols, names.arg = NULL, cex.axis = 1.5,
# cex.lab = 2, las = 1, xpd = NA, horiz = TRUE, cex.names = cex.names,
# ...)
# if (!is.null(VLine)) {
# if (VLine < 1)
# VLine <- VLine * 100
# if (VLine > 0)
# abline(v = VLine, lty = 2, lwd = 2, col = "gray")
# }
# rng <- par("usr")
# lg <- legend(rng[1], rng[2], legend = Legend, bty = "n",
# cex = 1.25, horiz = TRUE, xpd = NA, fill = bcols, title = "",
# plot = FALSE)
# legend(rng[1], rng[4] + lg$rect$h, legend = Legend, bty = "n",
# cex = 1.5, horiz = TRUE, xpd = NA, fill = bcols, title = "",
# ...)
# } else {
# barplot(splitdat, beside = TRUE, ylab = "", xlim = c(0,
# 100), names.arg = NULL, cex.axis = 1.5, cex.lab = 2,
# las = 1, axes = TRUE, col = bcols, horiz = TRUE, cex.names = cex.names,
# ...)
# # abline(v=PLim, lty=2, col='gray', xpd=FALSE)
# }
# xx <- xx2 + 1
# xx2 <- min(xx + npplot - 1, nMPs)
# mtext(side = 1, outer = TRUE, "Probability", line = 1, cex = 1.4)
# if (!is.null(VLine)) {
# if (VLine < 1)
# VLine <- VLine * 100
# if (VLine > 0)
# abline(v = VLine, lty = 2, lwd = 2, col = "gray")
# }
# }
# }
# if (!is.null(Title))
# mtext(side = 3, outer = TRUE, title, line = 3.5, cex = 1.25)
#
# if (length(Years) == 1)
# mtext(side = 3, outer = TRUE, Years, line = 1.5)
# Pout <- t(Probs)
# if (is.null(rownames(Pout))) {
# MP <- colnames(Pout)
# colnames(Pout) <- NULL
# } else {
# MP <- rownames(Pout)
# rownames(Pout) <- NULL
# }
# MPclass <- MPtype(MPs)[,2]
# par(op)
# invisible(data.frame(MP = MP, Pout, Pass = Pass, MPClass = MPclass,
# stringsAsFactors = FALSE))
# }
#
# #' Boxplot of MP performance from MSE object
# #'
# #' @param x An object of class MSE
# #' @param MPs Optional subset MSE object by MP
# #' @param maxMP Maximum number of MPs to plot
# #' @param PMRefs List containing the Performance Metrics reference points.
# #' Options are \code{'SSB_SSB0', 'B_BMSY', 'F_FMSY', 'AAVE', 'AAVY'}
# #' @param lastYrs Last number of years in projection to calculate statistics
# #' @param cex.lab Size of axis label text
# #' @param cex.PM Size of performance metric text
# #' @param canMPs Optional character vector of MPs that can be applied (plotted
# #' in different colour)
# #' @param cols Optional vector of colours
# #' @param outline Logical. Include outliers in boxplot?
# #' @param CexName Size of the names
# #' @param incLine Logical. Include vertical line?
# #' @param incref Logical. Include reference methods?
# #' @param Names Logical. Include MP names in plot?
# #' @param ... Additional arguments to be passed to plotting functions
# #' @author A. Hordyk
# #' @export
# boxplot.MSE <- function(x, MPs = NA, maxMP = 8,
# PMRefs = list(B_BMSY = 1,
# SSB_SSB0 = 0.2, F_FMSY = 1, AAVY = 30, AAVE = 30),
# lastYrs = 10, cex.lab = 1.2,
# cex.PM = 0.75, canMPs = NULL, cols = TRUE, outline = FALSE, CexName = 1.25,
# incLine = TRUE, incref = FALSE, Names = TRUE, ...) {
#
# MSEobj <- x
# if (!all(is.na(MPs))) {
# if (!incref) {
# ind <- grep("FMSYref", MPs) # FMSYref methods
# ind <- c(ind, grep("NFref", MPs)) # No fishing reference
# if (length(ind) > 0)
# MPs <- MPs[-ind]
# }
# MSEobj <- Sub(MSEobj, MPs = MPs)
# }
# nmp <- MSEobj@nMPs
# Nyears <- MSEobj@nyears
# Pyears <- MSEobj@proyears
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
# nsim <- MSEobj@nsim
# RefYd <- MSEobj@OM$RefY
# nyrs <- MSEobj@proyears
#
# perf <- MPStats(MSEobj, lastYrs = lastYrs)
# if (lastYrs >= MSEobj@proyears)
# lastYrs <- 10
# yrs <- (nyrs - lastYrs + 1):nyrs
# perfdat <- perf[[1]]
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears,
# "(last", lastYrs, "years)")
#
# # MPtype <- perfdat$MPtype
# MPtypes <- MPtype(MPs)[,2]
# outmps <- perfdat[MPtypes == "Output", ]$MP
# inmps <- perfdat[MPtypes == "Input", ]$MP
# nOut <- ceiling(nmp/2)
# nIn <- floor(nmp - nOut)
# if (nMPs > nmp) {
# outDist <- perfdat[MPtypes == "Output", ]$Dist
# OutMPs <- perfdat[MPtypes == "Output", ]$MP[order(outDist)[1:nOut]]
# inDist <- perfdat[MPtypes == "Input", ]$Dist
# InMPs <- perfdat[MPtypes == "Input", ]$MP[order(inDist)[1:nIn]]
# } else {
# OutMPs <- perfdat[MPtypes == "Output", ]$MP
# InMPs <- perfdat[MPtypes == "Input", ]$MP
# }
#
# mseobj <- Sub(MSEobj, MPs = c(OutMPs, InMPs))
# nMPs <- mseobj@nMPs
# perf <- MPStats(mseobj, msg = FALSE)
# perfdat <- perf$Perf
# MPtypes <- MPtype(mseobj@MPs)[,2]
# # MPtype <- perfdat$MPtype
# rawVals <- perf$BySim
# MPs <- perfdat$MP
#
# # Reporting Distribution in last years
# BBMSY <- rawVals$B_BMSY[, , yrs]
# BBMSY <- aperm(BBMSY, c(1, 3, 2))
# dim(BBMSY) <- c(nsim * lastYrs, nMPs)
#
# BB0 <- rawVals$SSB_SSB0[, , yrs]
# BB0 <- aperm(BB0, c(1, 3, 2))
# dim(BB0) <- c(nsim * lastYrs, nMPs)
#
# FFMSY <- rawVals$F_FMSY[, , yrs]
# FFMSY <- aperm(FFMSY, c(1, 3, 2))
# dim(FFMSY) <- c(nsim * lastYrs, nMPs)
#
# LTY <- rawVals$LTY
# LTY <- aperm(LTY, c(1, 3, 2))
# dim(LTY) <- c(nsim * 10, nMPs)
#
# AAVY <- rawVals$AAVY
# AAVY <- aperm(AAVY, c(1, 3, 2))
# dim(AAVY) <- c(nsim * 10, nMPs)
#
# AAVE <- rawVals$AAVE
# # apply(AAVE[,,10], 2, mean, na.rm=TRUE)
# AAVE <- aperm(AAVE, c(1, 3, 2))
# dim(AAVE) <- c(nsim * 10, nMPs)
# # apply(AAVE, 2, mean, na.rm=TRUE)
#
# # Colors - to make the plot a bit more cheerful
# inputs <- which(MPtypes == "Input")
# outputs <- which(MPtypes == "Output")
# fonts <- rep(2, nmp)
# fonts[MPtypes == "Input"] <- 4
# NameCol <- rep("black", nmp)
# NameCol[MPs %in% canMPs] <- "green"
# index <- grep("ref", MPs)
# NameCol[index] <- "darkgray"
#
# if (class(cols) == "character")
# Col <- cols
# if (class(cols) == "logical" && !(cols))
# Col <- "lightblue"
#
# if (class(cols) == "logical" && cols) {
# cols1 <- rep(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",
# "#FFFF33", "#A65628", "#F781BF"), 4) # brewer.pal(8, 'Set1')
# cols2 <- rep(c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854",
# "#FFD92F", "#E5C494", "#B3B3B3"), 4) # brewer.pal(8, 'Set2')
# Col <- rep(cols1, length.out = nMPs)[1:nMPs]
# if (length(inputs) > 0)
# Col[inputs] <- cols2[1:length(inputs)]
# }
#
# Line <- 3.5
# CexName <- 1.25
# lncol <- "slategray"
# topcex <- cex.PM
# ncharmp <- max(nchar(MPs)) - 1.5
# multi <- 1.15
# op <- par(mfrow = c(2, 3), bty = "n", oma = c(4, ncharmp, 2, 0), mar = c(3,
# 2, 4, 3))
# # if (class(Title) == 'character') par(mfrow=c(2,3), bty='n',
# # oma=c(4,ncharmp,2,0), mar=c(3,2,4,3)) if (class(Title) !=
# # 'character') par(mfrow=c(2,3), bty='n', oma=c(4,ncharmp,0,0),
# # mar=c(3,2,4,3)) B/B0
# YLim <- c(0, 1)
# tt <- boxplot(BB0, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, cex.names = CexName,
# axes = FALSE, ylim = YLim)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE, las = 2)
# axis(side = 2, at = inputs, labels = FALSE, las = 2)
# if (Names)
# text(x = rep(-0.07, nmp), y = 1:nmp, labels = MPs, col = NameCol,
# cex = CexName, font = fonts, pos = 2, xpd = NA, srt = 0)
# mtext(side = 1, expression(italic(B/B[0])), cex = cex.lab, line = Line)
# mtext(side = 3, outer = TRUE, Years, line = -0.5)
# PosPMs <- c("SSB_SSB0", "B_BMSY", "F_FMSY", "AAVE", "AAVY")
# ind <- match(names(PMRefs), PosPMs)
# refs <- unlist(PMRefs[ind])
#
# if (incLine && refs[1] > 0) {
# ps <- round(apply(BB0 > refs[1], 2, sum, na.rm = TRUE)/(nrow(BB0)),
# 2)
# font <- rep(1, length(ps))
# ps <- sprintf("%3.2f", ps)
# if (refs[1] != 1)
# txt <- bquote(Prob. ~ italic(B) > ~.(refs[1]) ~ italic(B[0]))
# if (refs[1] == 1)
# txt <- bquote(Prob. ~ italic(B) > ~italic(B[0]))
#
# text(x = YLim[2] * 1.1, y = 1:nMPs, labels = ps, xpd = NA, font = font)
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# abline(v = refs[1], lty = 2, lwd = 2, col = lncol)
# }
#
# text(-0.2, nmp * 1.25, "Input Control", font = 4, xpd = NA, cex = 1.2,
# pos = 4)
# text(-0.2, nmp * 1.35, "Output Control", font = 2, xpd = NA, cex = 1.2,
# pos = 4)
# if (!is.null(canMPs)) {
# text(1, nmp * 1.25, "Available", font = 2, xpd = NA, cex = 1.2,
# col = "green", pos = 2)
# text(1, nmp * 1.35, "Not Available", font = 2, xpd = NA, cex = 1.2,
# pos = 2)
# }
#
# # B/BMSY
# YLim <- c(0, 3)
# tt <- boxplot(BBMSY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = YLim)
#
# mtext(side = 1, expression(italic(B/B[MSY])), cex = cex.lab, line = Line)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[2] > 0) {
# ps <- round(apply(BBMSY > refs[2], 2, sum, na.rm = TRUE)/(nrow(BBMSY)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# if (refs[2] != 1)
# txt <- bquote(Prob. ~ italic(B) > ~.(refs[2]) ~ italic(B[MSY]))
# if (refs[2] == 1)
# txt <- bquote(Prob. ~ italic(B) > ~italic(B[MSY]))
# # text(x=YLim[2]*1.1, y=1:nMPs, labels=ps, xpd=NA, font=font)
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# abline(v = refs[2], lty = 2, lwd = 2, col = lncol)
# }
#
#
# # F/FMSY
# YLim <- c(0, 2)
# tt <- boxplot(FFMSY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = YLim)
#
# mtext(side = 1, expression(italic(F/F[MSY])), cex = cex.lab, line = Line)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[3] > 0) {
# ps <- round(apply(FFMSY < refs[3], 2, sum, na.rm = TRUE)/(nrow(FFMSY)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# abline(v = refs[3], lty = 2, lwd = 2, col = lncol)
# if (refs[3] != 1)
# txt <- bquote(Prob. ~ italic(F) < ~.(refs[3]) ~ italic(F[MSY]))
# if (refs[3] == 1)
# txt <- bquote(Prob. ~ italic(F) < ~italic(F[MSY]))
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# }
#
#
# # AAVY
# if (refs[4] < 1)
# refs[4] <- refs[4] * 100
# AAVY <- AAVY * 100
# YLim <- c(0, 60)
# tt <- boxplot(AAVY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = YLim)
# mtext(side = 1, "Average Annual\n Variation Yield (%)", cex = cex.lab,
# line = Line + 1)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE, las = 2)
# axis(side = 2, at = inputs, labels = FALSE, las = 2)
# text(x = rep(-5, nmp), y = 1:nmp, labels = MPs, col = NameCol, cex = CexName,
# font = fonts, pos = 2, xpd = NA, srt = 0)
# if (incLine && refs[4] > 0) {
# ps <- round(apply(AAVY < refs[4], 2, sum, na.rm = TRUE)/(nrow(AAVY)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
# if (refs[4] != 1)
# txt <- bquote(Prob. ~ italic(AAVY) < ~.(refs[4]) ~ "%")
# if (refs[4] == 1)
# txt <- bquote(Prob. ~ italic(AAVY) < ~"%")
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# }
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
#
# # AAVE
# if (refs[5] < 1)
# refs[5] <- refs[5] * 100
# AAVE <- AAVE * 100
# tt <- boxplot(AAVE, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = c(0,
# 60))
# mtext(side = 1, "Average Annual\n Variation Effort (%)", cex = cex.lab,
# line = Line + 1)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[4] > 0) {
# ps <- round(apply(AAVE < refs[4], 2, sum, na.rm = TRUE)/(nrow(AAVE)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
# if (refs[5] != 1)
# txt <- bquote(Prob. ~ italic(AAVE) < ~.(refs[5]) ~ "%")
# if (refs[5] == 1)
# txt <- bquote(Prob. ~ italic(AAVE) < ~"%")
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
#
# }
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
#
#
# # LTY
# tt <- boxplot(LTY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE)
# mtext(side = 1, "Relative Long-Term\n Yield", cex = cex.lab, line = Line +
# 1)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[5] > 0)
# abline(v = refs[4], lty = 2, lwd = 2, col = "darkgray")
# par(op)
#
# }
#
#
# #' Plot the median biomass and yield relative to last historical year
# #'
# #' Compare median biomass and yield in first year and last 5 years of
# #' projection
# #'
# #' @param MSEobj An object of class MSE
# #' @param MPs Optional subset by MP
# #' @param lastYrs Last number of years of projection to calculate median
# #' @param XMin Optional minimum for the x-axis
# #' @param YMin Optional minimum for the y-axis
# #' @param ShowLabs Logical. Show the MP labels? Otherwise only plot points
# #' @return Invisibly returns a data frame containing information shown in the
# #' plot
# #' @author A. Hordyk
# #' @export Cplot
# Cplot <- function(MSEobj, MPs = NA, lastYrs = 5, XMin = NULL, YMin = NULL,
# ShowLabs = FALSE) {
# if (!all(is.na(MPs)))
# MSEobj <- Sub(MSEobj, MPs = MPs)
# nsim <- MSEobj@nsim
# Alpha <- 60
# if (nsim < 10)
# Alpha <- 180
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
# nyears <- MSEobj@nyears
# proyears <- MSEobj@proyears
#
# Stat <- MPStats(MSEobj, lastYrs = lastYrs)$BySim
# ny <- dim(Stat$Yield)[3]
# Stat$Yield <- Stat$Yield[, , , drop = FALSE]/Stat$Yield[, , rep(1,
# ny), drop = FALSE]
#
# RelYield <- apply(Stat$Yield, 2, median, na.rm = TRUE)
#
# Bcurr <- Stat$B_BMSY[, , 1] # Biomass at start of projections
# Bend <- apply((Stat$B_BMSY[, , (proyears - lastYrs + 1):proyears]),
# c(1, 2), median, na.rm = TRUE) # median biomass in last years
# RelBio <- apply(Bend/Bcurr, 2, median, na.rm = TRUE)
#
# XMin <- ifelse(is.null(XMin), 0, XMin)
# YMin <- ifelse(is.null(YMin), 0, YMin)
# XLim <- c(YMin, ceiling(max(RelBio)/0.5) * 0.5) * c(0.95, 1.05)
# YLim <- c(XMin, ceiling(max(RelYield)/0.5) * 0.5) * c(0.95, 1.05)
# op <- par(mfrow = c(1, 1), oma = c(3, 5, 1, 1), mar = c(2, 2, 0, 0))
# plot(RelBio, RelYield, xlim = XLim, ylim = YLim, type = "n", bty = "l",
# xlab = "", ylab = "", xaxs = "i", yaxs = "i", las = 1)
# if (ShowLabs)
# text(RelBio, RelYield, MSEobj@MPs)
# if (!ShowLabs)
# points(RelBio, RelYield, pch = 21, cex = 2, bg = "lightgray")
# abline(h = 1, lty = 3, col = "lightgray")
# abline(v = 1, lty = 3, col = "lightgray")
# mtext(side = 1, line = 3.5, paste("Median Biomass (last", lastYrs,
# "years)\n relative to current"), cex = 1.25)
# mtext(side = 2, line = 3, paste("Median Yield (last", lastYrs, "years)\n relative to current"),
# cex = 1.25)
# par(op)
# DF <- data.frame(MP = MSEobj@MPs, Biomass = RelBio, Catch = RelYield,
# stringsAsFactors = FALSE)
# invisible(DF)
#
# }
#
#
# #' Joint probability plot
# #'
# #' Calculates and plots the joint probability of meeting all performance
# #' metrics simultaneously
# #'
# #'
# #' @param MSEobj An object of class MSE
# #' @param PLim Probability limit (acceptable risk threshold; e.g., 0.8 for 80
# #' percent)
# #' @param YVar What to plot of the y-axis: choose from \code{c('LTY', 'STY',
# #' 'avgSSB_SSB0', 'avgB_BMSY')}
# #' @param PMRefs List containing the reference limits for each metric
# #' @param UseMean Logical. Calculate mean (TRUE) or median (FALSE)
# #' @param lastYrs Last number of years in projection period to calculate
# #' summary statistics
# #' @param AvailMPs Optional character vector of available MPs (plotted in a
# #' different colour)
# #' @param XLim Optional limits for the x-axis
# #' @param ShowCols Logical. Show the background colours?
# #' @param ShowLabs Logical. Show the MP labels?
# #' @param All Logical. Plot all MPs (TRUE) or only those above the probability
# #' limit (\code{PLim}))?
# #' @return Invisibly returns data frame containing statistics shown in the plot
# #' @author A. Hordyk
# #' @export Jplot
# Jplot <- function(MSEobj, PLim = 0.8, YVar = c("LTY", "STY", "avgSSB_SSB0",
# "avgB_BMSY"), PMRefs = list(B_BMSY = 0.5, SSB_SSB0 = 0.2), UseMean = TRUE,
# lastYrs = 10, AvailMPs = NULL, XLim = NULL, ShowCols = TRUE, ShowLabs = FALSE,
# All = TRUE) {
#
# nsim <- MSEobj@nsim
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
#
# PMs <- names(PMRefs)
# YVar <- match.arg(YVar, several.ok = FALSE)
# mYVar <- YVar
# mYVar[mYVar == "avgSSB_SSB0"] <- "SSB_SSB0m"
# mYVar[mYVar == "avgB_BMSY"] <- "B_BMSYm"
#
# perf <- MPStats(MSEobj, PMRefs = PMRefs, lastYrs = lastYrs, UseMean = UseMean)
# Probs <- perf$Probs
#
# if (lastYrs >= MSEobj@proyears)
# lastYrs <- 10
# index <- paste0(PMs, "ref")
# # Joint Prob above B refs and below F
# jointP <- Probs[[index[1]]]
# if (length(PMs) > 1) {
# for (X in 2:length(PMs)) {
# jointP <- jointP * Probs[[index[X]]]
# }
# }
# JP <- apply(jointP, 2, sum, na.rm = TRUE)/(lastYrs * nsim)
#
# # Probability Thresholds
# vl <- PLim
# hl <- 0
# if (vl < 1)
# vl <- vl * 100
#
# x <- JP
# if (max(x) <= 1)
# x <- x * 100
# y <- perf[[1]][, mYVar]
# if (max(y) <= 10)
# y <- y * 100
# adjj <- c(0.9, 1.1)
# if (is.null(XLim))
# XLim <- c(min(c(-10, min(x, na.rm = T) * adjj)), max(c(max(x, na.rm = T) *
# adjj, 110)))
# if (!All)
# XLim <- c(vl * 0.95, 105)
# YLim <- c(min(c(-10, min(y, na.rm = T) * adjj)), max(c(max(y, na.rm = T) *
# adjj, 110)))
#
# ## Legend
# BmsyRef <- unique(perf[[1]]$B_BMSYRef)
# B0Ref <- unique(perf[[1]]$SSB_SSB0Ref)
# FRef <- unique(perf[[1]]$F_FMSYRef)
# # legend text
# if (FRef == 1)
# leg1 <- bquote(italic(F) < ~italic(F[MSY]))
# if (FRef != 1)
# leg1 <- bquote(italic(F) < ~.(FRef) ~ italic(F[MSY]))
# if (B0Ref == 1)
# leg2 <- bquote(italic(B) > ~italic(B[0]))
# if (B0Ref != 1)
# leg2 <- bquote(italic(B) > ~.(B0Ref) ~ italic(B[0]))
# if (BmsyRef == 1)
# leg3 <- bquote(italic(B) > ~~italic(B[MSY]))
# if (BmsyRef != 1)
# leg3 <- bquote(italic(B) > ~.(BmsyRef) ~ italic(B[MSY]))
#
# legtex <- list(F_FMSY = leg1, SSB_SSB0 = leg2, B_BMSY = leg3)
# ind <- match(PMs, names(legtex))
# Legend <- NULL
# Legend <- append(Legend, as.expression(legtex[ind]))
#
# # Plot
# xlab <- "Probability > All Performance Limits"
# if (UseMean)
# ylab <- switch(YVar, LTY = paste0("Long-Term Yield (last ", lastYrs,
# " years)"), STY = paste0("Short-Term Yield (first ", lastYrs,
# " years)"), avgB_BMSY = paste0("Mean B/BMSY (%) (last ", lastYrs,
# " years)"), avgSSB_SSB0 = paste0("Mean B/B0 (%) (last ", lastYrs,
# " years)"))
# if (!UseMean)
# ylab <- switch(YVar, LTY = paste0("Long-Term Yield (last ", lastYrs,
# " years)"), STY = paste0("Short-Term Yield (first ", lastYrs,
# " years)"), avgB_BMSY = paste0("Median B/BMSY (%) (last ",
# lastYrs, " years)"), avgSSB_SSB0 = paste0("Median B/B0 (%) (last ",
# lastYrs, " years)"))
# op <- par(mfrow = c(1, 1), mar = c(4, 4, 1, 1), oma = c(1, 1, 0, 6))
# plot(NA, xlim = XLim, ylim = YLim, xlab = xlab, ylab = ylab, bty = "l",
# las = 1, cex.lab = 1.25)
#
# abline(v = vl, col = "#99999940", lwd = 2)
# Alpha <- 15
# # polygons
# LeftCol <- rgb(red = 255, green = 0, blue = 0, alpha = Alpha, names = NULL,
# maxColorValue = 255)
# RightCol <- rgb(red = 0, green = 255, blue = 0, alpha = Alpha, names = NULL,
# maxColorValue = 255)
# if (ShowCols) {
# polygon(x = c(0, vl, vl, 0), y = c(0, 0, hl, hl), col = LeftCol,
# border = NA)
# polygon(x = c(0, vl, vl, 0), y = c(0, 0, max(YLim), max(YLim)),
# col = LeftCol, border = NA)
# polygon(x = c(vl, max(XLim), max(XLim), vl), y = c(0, 0, max(YLim),
# max(YLim)), col = RightCol, border = NA)
# polygon(x = c(vl, max(XLim), max(XLim), vl), y = c(hl, hl, max(YLim),
# max(YLim)), col = RightCol, border = NA)
# }
#
# MPs <- MSEobj@MPs
# Pch <- rep(21, length(MPs))
# Pch[grep("FMSY", MPs)] <- 24
# Pch[grep("NFref", MPs)] <- 24
#
# # Which MPs meet minimum PMs
# ind <- which(x >= vl & y >= hl)
# coly <- rep("darkgray", length(MPs))
# coly[ind] <- "black"
# if (!is.null(AvailMPs))
# coly[MPs %in% AvailMPs & !(x >= vl & y >= hl)] <- "Medium Sea Green"
# if (!is.null(AvailMPs))
# coly[MPs %in% AvailMPs & (x >= vl & y >= hl)] <- "green"
# coly[grep("FMSY", MPs)] <- "lightgray"
# coly[grep("NFref", MPs)] <- "lightgray"
#
# MPtype <- sapply(1:nMPs, function(X) class(get(MPs[X])))
# fonts <- rep(2, nMPs)
# fonts[MPtype == "Input"] <- 4
# Cex <- 1.5
# if (!ShowLabs)
# points(x, y, bg = coly, pch = Pch, cex = Cex, col = "black")
# if (ShowLabs)
# text(x, y, MPs, font = fonts, col = coly, cex = 1)
#
# # Add Legend
# text(min(XLim), max(YLim), "Performance Limits", cex = 1.25, pos = 4)
# temp <- 0.95
# for (XX in 1:length(Legend)) {
# text(min(XLim), max(YLim) * temp, Legend[XX], pos = 4)
# temp <- temp - 0.05
# }
# Cvec <- c("darkgray", "black", "Medium Sea Green", "green", "lightgray")
# if (!ShowLabs)
# legend(100, max(YLim), pch = c(21, 21, 21, 21, 24), legend = c("Not Acceptable",
# "Acceptable", "Available", "Acceptable & Available", "Reference"),
# bty = "n", pt.bg = Cvec, pt.cex = Cex, xpd = NA)
#
# if (ShowLabs)
# legend(100, max(YLim), text.font = c(2, 4), legend = c("Output Control",
# "Input Control", "Not Acceptable", "Acceptable", "Available",
# "Acceptable & Available", "Reference"), bty = "n", text.col = c("black",
# "black", Cvec), pt.cex = Cex, xpd = NA)
#
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears,
# "(last", lastYrs, "years)")
# mtext(side = 3, Years, cex = 1.25)
#
# if (YVar == "B_BMSYm")
# abline(h = 100, col = "#99999940", lwd = 2)
# if (YVar == "SSB_SSB0m")
# abline(h = 50, col = "#99999940", lwd = 2)
# par(op)
# DF <- data.frame(MPs = MPs, Yield = y, Prob = x, Pass = x >= vl, stringsAsFactors = FALSE)
# invisible(DF[order(DF$Prob, decreasing = TRUE), ])
# }
#
#' KOBE plot: a projection by projection plot of F/FMSY and B/BMSY
#'
#' A standard KOBE plot by each method that also shows the percentage of
#' methods that ended up in each quadrant.
#'
#'
#' @param MSEobj An object of class MSE
#' @param maxsim Maximum number of simulations (lines) to plot on each panel.
#' @param MPs Optional subset MSE object by MP
#' @param sims Optional subset MSE object by simulation
#' @param maxMP Maximum number of MPs to include in plot
#' @param nam The name of the plot
#' @param cex.leg Size of legend
#' @note Apologies for the nauseating shading.
#' @author T. Carruthers with some additions from A. Hordyk
#' @export Kplot
Kplot <- function(MSEobj, maxsim = 60, MPs = NA, sims = NULL, maxMP = 9,
nam = NA, cex.leg = 1.5) {
# png('Kplot.png')
if (!is.null(sims) & all(is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims)
if (!is.null(sims) & all(!is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims, MPs = MPs)
if (is.null(sims) & !all(is.na(MPs)))
MSEobj <- Sub(MSEobj, MPs = MPs)
nMPs <- MSEobj@nMPs
nsim <- MSEobj@nsim
if (is.null(sims) & nsim > maxsim)
MSEobj <- Sub(MSEobj, sims = 1:maxsim)
if (nMPs > maxMP) {
message("MSE object has more than ", maxMP, " MPs. Plotting the first ",
maxMP)
MSEobj <- Sub(MSEobj, MPs = 1:maxMP)
nMPs <- MSEobj@nMPs
}
nr <- floor((MSEobj@nMPs)^0.5)
nc <- ceiling((MSEobj@nMPs)/nr)
Cex <- 1.5
TitleCex <- 1.5
FMSYr <- quantile(MSEobj@F_FMSY, c(0.001, 0.9), na.rm = T)
BMSYr <- quantile(MSEobj@SB_SBMSY, c(0.001, 0.975), na.rm = T)
# dev.new2(width=nc*3,height=nr*3.6)
# par(mfrow=c(nr,nc),mai=c(0.45,0.45,0.45,0.01),omi=c(0.45,0.3,0.35,0.01))
# par(mfcol=c(nr,nc),mai=c(0.2,0.35,0.3,0.01),omi=c(0.5,0.4,0.4,0.05))
if (is.na(nam))
op <- par(mfrow = c(nr, nc), mar = c(2, 2, 3, 1), oma = c(3, 3.5, 1.2,
0))
if (!is.na(nam))
op <- par(mfrow = c(nr, nc), mar = c(2, 2, 3, 1), oma = c(3, 3.5, 3, 0))
colsse <- rainbow(MSEobj@proyears, start = 0.63, end = 0.95)[1:MSEobj@proyears]
colsse <- makeTransparent(colsse, 95)
XLim <- c(0, 3)
YLim <- c(0, 2.5)
pmat <- matrix(NA, nrow = nc, ncol = nr, byrow = FALSE)
pmat[1:nMPs] <- 1:nMPs
pmat <- t(pmat)
for (mm in 1:MSEobj@nMPs) {
plot(MSEobj@SB_SBMSY[1, mm, 1], MSEobj@F_FMSY[1,
mm, 1], xlim = XLim, ylim = YLim,
col = colsse[1], bty = "n", axes = FALSE)
if (nrow(pmat) > 1) {
if (mm %in% pmat[, 1]) {
axis(side = 2, labels = TRUE, las = 1)
} else axis(side = 2, labels = FALSE)
if (mm %in% pmat[nr, ]) {
axis(side = 1, labels = TRUE)
} else axis(side = 1, labels = FALSE)
nas <- apply(pmat, 1, sum)
rr <- which.max(nas)
nas2 <- apply(pmat, 2, sum)
cc <- which(is.na(nas2))
if (mm %in% pmat[rr, cc])
axis(side = 1, labels = TRUE)
} else {
if (mm == 1)
axis(side = 2, labels = TRUE, las = 1)
if (mm != 1)
axis(side = 2, labels = FALSE)
axis(side = 1, labels = TRUE)
}
OO <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] < 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] > 1, na.rm = T)/MSEobj@nsim * 100, 1)
OU <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] > 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] > 1, na.rm = T)/MSEobj@nsim * 100, 1)
UO <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] < 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] < 1, na.rm = T)/MSEobj@nsim * 100, 1)
UU <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] > 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] < 1, na.rm = T)/MSEobj@nsim * 100, 1)
# alp<-80
# polygon(c(1,-1000,-1000,1),c(1,1,1000,1000),col=makeTransparent('orange',alp),border=makeTransparent('orange',alp))
# polygon(c(1,1000,1000,1),c(1,1,1000,1000),col=makeTransparent('yellow',alp),border=makeTransparent('yellow',alp))
# polygon(c(1,-1000,-1000,1),c(1,1,-1000,-1000),col=makeTransparent('yellow',alp),border=makeTransparent('yellow',alp))
# polygon(c(1,1000,1000,1),c(1,1,-1000,-1000),col=makeTransparent('green',alp),border=makeTransparent('yellow',alp))
abline(h = 1, col = "grey", lwd = 3)
abline(v = 1, col = "grey", lwd = 3)
# abline(v=c(0.1,0.5),col='grey',lwd=2)
y <- 1:(MSEobj@proyears - 1)
rng <- 1:min(maxsim, MSEobj@nsim)
points(MSEobj@SB_SBMSY[rng, mm, 1], MSEobj@F_FMSY[rng, mm, 1], pch = 19,
cex = 0.8, col = colsse[1])
points(MSEobj@SB_SBMSY[rng, mm, MSEobj@proyears], MSEobj@F_FMSY[rng,
mm, MSEobj@proyears], pch = 19, cex = 0.8, col = colsse[MSEobj@proyears])
if (mm == 1)
legend("right", c("Start", "End"), bty = "n", text.col = c(colsse[1],
colsse[MSEobj@proyears]), pch = 19, col = c(colsse[1],
colsse[MSEobj@proyears]))
legend("topleft", paste(OO, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
legend("topright", paste(OU, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
legend("bottomleft", paste(UO, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
legend("bottomright", paste(UU, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
mtext(MSEobj@MPs[mm], 3, line = 0.6, cex = TitleCex)
}
mtext(expression(B/B[MSY]), 1, outer = T, line = 2, cex = Cex)
mtext(expression(F/F[MSY]), 2, outer = T, line = 1.2, cex = Cex)
# if(is.na(nam))mtext(deparse(substitute(MSEobj)),3,outer=T,line=0.25,font=2,
# cex=TitleCex)
# if(!is.na(nam))mtext(MSEobj@Name,3,outer=T,line=0.25,font=2,
# cex=TitleCex)
if (!is.na(nam))
mtext(nam, 3, outer = TRUE, line = 0.25, font = 2, cex = TitleCex)
# dev.off()
par(op)
}
#' National Oceanographic and Atmospheric Administration default plot 1
#'
#' A preliminary plot for returning trade-offs plots and performance table for
#' total yield, variability in yield, probability of overfishing and likelihood
#' of biomass dropping below 50 per cent BMSY
#'
#'
#' @param MSEobj An object of class MSE
#' @param nam Title of plot
#' @param type Plots full range of data if NA. Plots a subset that meet
#' thresholds if not NA.
#' @param panel Should a two panel plot be made or should plots be made in
#' sequence.
#' @return A table of performance metrics.
#' @author T. Carruthers
#' @export NOAA_plot
NOAA_plot <- function(MSEobj, nam = NA, type = NA, panel = T) {
Yd <- rep(NA, MSEobj@nMPs)
B50 <- rep(NA, MSEobj@nMPs)
PNOF <- rep(NA, MSEobj@nMPs)
LTY <- rep(NA, MSEobj@nMPs)
STY <- rep(NA, MSEobj@nMPs)
VY <- rep(NA, MSEobj@nMPs)
y1 <- 1:(MSEobj@proyears - 1)
y2 <- 2:MSEobj@proyears
yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
PNOF[mm] <- round(mean(MSEobj@F_FMSY[, mm, ] <= 1, na.rm = T) * 100, 1)
B50[mm] <- round(mean(MSEobj@SB_SBMSY[, mm, ] >= 0.5, na.rm = T) * 100, 1)
LTY[mm] <- round(mean(MSEobj@Catch[, mm, yend]/RefYd >= 0.5, na.rm = T), 3) * 100
AAVY <- apply((((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])/MSEobj@Catch[, mm, y2])^2)^0.5, 1, mean, na.rm = T)
VY[mm] <- round(mean(AAVY <= 0.15, na.rm = T), 3) * 100
}
# dev.new2(width=7,height=7)
if (panel)
op <- par(mfrow = c(1, 2), mai = c(1.5, 1.5, 0.1, 0.1), omi = c(0.1, 0.1, 0.4, 0))
if (is.na(type)) {
tradeoffplot(PNOF, LTY, "Prob. of not overfishing (%)", "Long-term yield ",
MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
tradeoffplot(B50, VY, "Prob. biomass above half BMSY (%)", "Prob. AAVY less than 15%",
MSEobj@MPs[1:MSEobj@nMPs], vl = 80, hl = 50)
} else {
tradeoffplot3(PNOF, LTY, "Prob. of not overfishing (%)", "Long-term yield",
MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100, xlim = c(45,
105), ylim = c(0, 105))
tradeoffplot3(B50, VY, "Prob. biomass above half BMSY (%)", "Prob. AAVY less than 15%",
MSEobj@MPs[1:MSEobj@nMPs], vl = 80, hl = 50, xlim = c(75, 105),
ylim = c(45, 105))
}
# if(is.na(nam))mtext(deparse(substitute(MSEobj)),3,outer=T,line=0.3,font=2)
# if(!is.na(nam) &
# !is.character(nam))mtext(MSEobj@Name,3,outer=T,line=0.3,font=2)
# if(!is.na(nam) &
# is.character(nam))mtext(nam,3,outer=T,line=0.3,font=2)
if (panel) par(op)
temp <- data.frame(PNOF, B50, LTY, VY)
row.names(temp) <- MSEobj@MPs[1:MSEobj@nMPs]
temp
}
#' A projection by projection plot of F/FMSY and B/BMSY
#'
#' A shorter version of the plot method for MSEs that just shows the projected
#' trends in stock status and over exploitation
#'
#'
#' @param MSEobj An object of class MSE
#' @param nam Title of plot
#' @param maxMP The maximum number of MPs to plot (defaults to the first 10)
#' @param MPs A character vector of MPs to plot
#' @param maxsims Integer, the maximum number of simulations to plot
#' @author T. Carruthers
#' @export Pplot
Pplot <- function(MSEobj, nam = NA, maxMP = 10,MPs=NA,maxsims=20) {
op <- par(no.readonly = TRUE)
on.exit(par(op))
if(!is.na(MPs)){
maxMP<-length(MPs)
MSEobj<-Sub(MSEobj,MPs=MPs)
} else{
if(MSEobj@nMPs>maxMP)MSEobj<-Sub(MSEobj,MPs=MSEobj@MPs[1:maxMP])
}
maxsims <- min(maxsims, MSEobj@nsim)
MSEobj<-Sub(MSEobj,sims=1:maxsims)
FMSYr <- quantile(MSEobj@F_FMSY, c(0.001, 0.9), na.rm = T)
BMSYr <- quantile(MSEobj@SB_SBMSY, c(0.001, 0.975), na.rm = T)
colsse <- rainbow(100, start = 0, end = 0.36)[1:100]
colB <- rep(colsse[100], ceiling(BMSYr[2] * 100))
colB[1:100] <- colsse
colB <- makeTransparent(colB, 60)
colsse <- rainbow(200, start = 0, end = 0.36)[200:1]
colF <- rep(colsse[200], ceiling(FMSYr[2] * 100))
colF[1:200] <- colsse
colF <- makeTransparent(colF, 60)
Yd <- rep(NA, MSEobj@nMPs)
P10 <- rep(NA, MSEobj@nMPs)
P50 <- rep(NA, MSEobj@nMPs)
P100 <- rep(NA, MSEobj@nMPs)
POF <- rep(NA, MSEobj@nMPs)
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
Yd[mm] <- round(mean(apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd,
na.rm = T) * 100, 1)
# cbind(MSEobj@Catch[,mm,yind],unlist(MSEobj@OM$MSY))
POF[mm] <- round(sum(MSEobj@F_FMSY[, mm, ] > 1, na.rm = T)/prod(dim(MSEobj@F_FMSY[,
mm, ]), na.rm = T) * 100, 1)
P10[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
mm, ])) * 100, 1)
P50[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.5, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
mm, ])) * 100, 1)
P100[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
mm, ])) * 100, 1)
}
nr <- ceiling(MSEobj@nMPs/8)
nc <- ceiling(MSEobj@nMPs/nr)
nr <- nr * 2
MSEcols <- c("red", "green", "blue", "orange", "brown", "purple", "dark grey",
"violet", "dark red", "pink", "dark blue", "grey")
temp <- array(0, c(nr * 2 + (nr/2 - 1), nc * 2))
i <- 0
for (c in 1:nc) {
for (r in 1:nr) {
i <- i + 1
temp[(ceiling(r/2) - 1) + (1:2) + (r - 1) * 2, (1:2) + (c - 1) * 2] <- ((c - 1) * nr) + r
}
}
par(mfcol = c(nr, nc), mar = c(2, 2, 2, 1), oma = c(3, 2, 2, 0))
layout(temp)
# dev.new2(width=nc*3,height=nr*3)
lwdy <- 2.5
for (mm in 1:MSEobj@nMPs) {
plot(MSEobj@F_FMSY[1, mm, ], ylim = FMSYr, col = colF[ceiling(mean(MSEobj@F_FMSY[1,
mm, ], na.rm = T) * 100)], type = "l", lwd = lwdy)
for (i in 1:MSEobj@nsim) lines(MSEobj@F_FMSY[i, mm, ], col = colF[ceiling(mean(MSEobj@F_FMSY[i,
mm, ], na.rm = T) * 100)], lwd = lwdy)
abline(h = 100, col = "grey", lwd = 3)
mtext(MSEobj@MPs[mm], 3, outer = F, line = 0.6)
legend("topright", c(paste(POF[mm], "% POF", sep = ""), paste(Yd[mm],
"% FMSY yield", sep = "")), bty = "n", cex = 0.8)
if (mm %in% (1:(nr/2)))
mtext("F/FMSY", 2, line = 2.5, outer = F)
abline(h = 1, col = makeTransparent("grey", 30), lwd = 2.5)
plot(MSEobj@SB_SBMSY[1, mm, ], ylim = BMSYr, col = colB[ceiling(MSEobj@SB_SBMSY[1,
mm, MSEobj@proyears] * 100)], type = "l", lwd = lwdy)
for (i in 1:MSEobj@nsim) lines(MSEobj@SB_SBMSY[i, mm, ], col = colB[ceiling(MSEobj@SB_SBMSY[i,
mm, MSEobj@proyears] * 100)], lwd = lwdy)
abline(h = 100, col = "grey", lwd = 3)
legend("topright", c(paste(P100[mm], "% < BMSY", sep = ""), paste(P50[mm],
"% < 0.5BMSY", sep = ""), paste(P10[mm], "% < 0.1BMSY", sep = "")),
bty = "n", cex = 0.8)
if (mm %in% (1:(nr/2)))
mtext("B/BMSY", 2, line = 2.5, outer = F)
abline(h = 1, col = makeTransparent("grey", 30), lwd = 2.5)
}
mtext("Projection year", 1, outer = T, line = 1.2)
if (is.na(nam))
mtext(deparse(quote(MSEobj)), 3, outer = T, line = 0.3, font = 2)
if (!is.na(nam) & !is.character(nam))
mtext(MSEobj@Name, 3, outer = T, line = 0.3, font = 2)
if (!is.na(nam) & is.character(nam))
mtext(nam, 3, outer = T, line = 0.3, font = 2)
return(invisible())
}
#' A projection by projection plot of F/FMSY, B/BMSY, B/B0, and yield
#'
#'
#' @param MSEobj An object of class MSE
#' @param YVar What to plot on the y-axis? Options are: \code{c('SSB_SSB0',
#' 'SSB_SSBMSY', 'F_FMSY', 'Yield')}
#' @param MPs Optional subset by MP
#' @param sims Optional subset by simulation
#' @param traj Plot all projections (\code{all}), only quantiles
#' (\code{quant}), or both projections and median (\code{both})
#' @param quants Numeric vector of length 2 specifying the quantiles (e.g.,
#' 10th and 90th. Median is always included)
#' @param incquant Logical. Include the quantiles or only plot median?
#' @param quantcol Colour of the quantile polygon
#' @param ref.lines Numeric vector of y-values for horizontal reference lines. Set to NULL to remove lines.
#' @param RefYield Should yield be relative to long-term optimum (\code{lto})
#' or last historical year (\code{curr})
#' @param LastYr Logical. Include the last historical year in the yield
#' projections?
#' @param maxMP Maximum number of MPs to plot
#' @param alpha Alpha for transparency of lines
#' @param cex.axis Size of axis text
#' @param cex.lab Size of axis label
#' @param YLab Optional label for y-axis
#' @param incMP Logical. Include name of MP?
#' @param MPcex Size of MP label
#' @param MPcol Optional character vector of colors for MP labels
#' @param incLeg Logical. Include a legend?
#' @param cex.leg Size of legend text
#' @param legPos Legend position
#' @param yline Optional horizontal lines
#' @param xline Optional vertical lines
#' @param parOR Logical to over-ride the par parameters
#' @param xaxis Logical. Should x-axis labels be displayed?
#' @param yaxis Logical. Should y-axis labels be displayed?
#' @param oneIt Logical. Should one iteration be plotted on the quantile plot?
#' @param ... Additional arguments to be passed to plotting functions
#' @author T. Carruthers & A.Hordyk
#' @export Pplot2
Pplot2 <- function(MSEobj, YVar = c("F_FMSY", "SSB_SSBMSY"), MPs = NA, sims = NULL,
traj = c("all", "quant", "both"), quants = c(0.1, 0.9), incquant = TRUE,
quantcol = "lightgray",
RefYield = c("lto", "curr"), LastYr = TRUE,
ref.lines=c(0.5, 1, 1.5), maxMP = 6, alpha = 60,
cex.axis = 1, cex.lab = 1, YLab = NULL, incMP = TRUE, MPcex = 1,
MPcol='black',
incLeg = TRUE, cex.leg = 1.5, legPos = "topleft", yline = NULL, xline=NULL, parOR = FALSE,
xaxis = TRUE, yaxis = TRUE, oneIt=TRUE, ...) {
old_par <- par(no.readonly = TRUE)
on.exit(par(old_par))
YVars <- c("SSB_SSB0", "SSB_SSBMSY", "F_FMSY", "Yield")
YVar <- match.arg(YVar, choices = YVars, several.ok = TRUE)
op <- par(no.readonly=TRUE)
if (!is.null(YLab) & length(YLab) != length(YVar))
stop("Length of YLab must equal length of YVar")
if (!is.null(sims) & all(is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims)
if (!is.null(sims) & all(!is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims, MPs = MPs)
if (is.null(sims) & !all(is.na(MPs)))
MSEobj <- Sub(MSEobj, MPs = MPs)
nsim <- MSEobj@nsim
if (nsim < 10)
alpha <- 180
nMPs <- MSEobj@nMPs
if (nMPs > maxMP) {
message("MSE object has more than ", maxMP, " MPs. Plotting the first ",
maxMP)
MSEobj <- Sub(MSEobj, MPs = 1:maxMP)
nMPs <- MSEobj@nMPs
}
MPs <- MSEobj@MPs
proyears <- MSEobj@proyears
RefYd <- MSEobj@OM$RefY
RefYield <- match.arg(RefYield)
traj <- match.arg(traj)
# Calculate Statistics Biomass/B0
temp <- as.matrix(expand.grid(1:nsim, 1:nMPs, 1:proyears))
Deplet <- array(NA, dim = dim(MSEobj@SB_SBMSY))
Deplet[temp] <- (MSEobj@SB_SBMSY[temp] * MSEobj@OM$SSBMSY_SSB0[temp[, 1]])
# Yield - including last historical year (current year)
# pastC <- apply(MSEobj@CB_hist[, , , , drop = FALSE], c(1, 3), sum,
# na.rm = TRUE)/RefYd
pastC <- MSEobj@CB_hist/RefYd
temp <- aperm(replicate(nMPs, pastC), c(1, 3, 2))
lastYr <- temp[, , MSEobj@nyears, drop = FALSE]
Yield <- abind::abind(lastYr, MSEobj@Catch[, , , drop = FALSE]/RefYd, along = 3)
Dat <- list(SSB_SSB0 = Deplet, SSB_SSBMSY = MSEobj@SB_SBMSY, F_FMSY = MSEobj@F_FMSY,
Yield = Yield)
Dat <- Dat[YVar]
if ("Yield" %in% YVar & RefYield == "curr") {
ny <- dim(Dat$Yield)[3]
Dat$Yield <- Dat$Yield[, , , drop = FALSE]/Dat$Yield[, , rep(1,
ny), drop = FALSE]
}
if ("Yield" %in% YVar & !LastYr) {
Dat$Yield <- Dat$Yield[, , 2:proyears, drop = FALSE]
}
nr <- length(Dat)
nc <- nMPs
dots <- list(...)
ylims <- cbind(0, unlist(lapply(Dat, quantile, 0.9, na.rm = TRUE)))
if ("SSB_SSB0" %in% YVar) {
index <- which(YVar == "SSB_SSB0")
ylims[index, ] <- c(0, max(1, max(ylims[index, ])))
}
if (length(dots$ylim) != 0) ylims <- matrix(rep((dots$ylim), length(Dat)), nrow = nr, byrow = TRUE)
colrange <- matrix(unlist(lapply(Dat, quantile, c(0.001, 0.975), na.rm = TRUE)),
nrow = nr, byrow = TRUE)
colsse <- rainbow(100, start = 0, end = 0.36)[1:100]
# Col<-rep(colsse[100],ceiling(colrange[2]*100)) Col[1:100]<-colsse
Col <- makeTransparent(colsse, alpha)
if (length(dots$lwd) == 0) lwd <- 3
if (length(dots$lwd) != 0) lwd <- dots$lwd
YLabs <- list(expression(SSB/SSB[0]), expression(SSB/SSB[MSY]), expression(F/F[MSY]),
"Yield relative\n to Long-Term\n Optimum")
if ("Yield" %in% YVar & RefYield == "curr")
YLabs[[4]] <- expression(Yield/Yield[current])
YLabs <- YLabs[match(YVar, YVars)]
if (!is.null(YLab))
YLabs <- YLab
if (!parOR) {
if ("Yield" %in% YVar & RefYield != "curr") {
op <- par(mfrow = c(nr, nc), bty = "n", mar = c(2, 2, 0, 0), oma = c(4, 8, 2, 1))
} else op <- par(mfrow = c(nr, nc), bty = "n", mar = c(2, 2, 0, 0), oma = c(4, 4, 2, 1))
}
if (parOR) {
nr <- par()$mfrow[1]
nc <- par()$mfrow[2]
}
for (X in 1:length(Dat)) {
Col2 <- Col
dat <- Dat[[X]]
ylim <- ylims[X, ]
ylab <- YLabs[[X]]
if (grepl("F_FMSY", YVar[X])) Col2 <- rev(Col)
for (mm in 1:nMPs) {
plot(1:length(dat[1, mm, ]), dat[1, mm, ], ylim = ylim, type = "n",
axes = FALSE, xlab = "", ylab = "")
# add reference lines
if (!is.null(ref.lines)) {
for (h in ref.lines) abline(h=h, lty=3, lwd=1, col="darkgray")
}
if (traj == "all" | traj=="both")
for (i in 1:MSEobj@nsim) lines(dat[i, mm, ],
col = Col2[min(100, ceiling(dat[i, mm, length(dat[i, mm, ])] * 100))], lwd = lwd)
if (traj == "quant" | traj=="both") {
stats <- apply(dat[, mm, , drop = FALSE], 3, quantile,
c(quants[1], 0.5, quants[2]), na.rm = TRUE)
if (length(quants) == 4)
stats2 <- apply(dat[, mm, , drop = FALSE], 3, quantile,
c(quants[3], quants[4]), na.rm = TRUE)
if (length(quants) != 4) stats2 <- NULL
if (traj=="both") {
stats2 <- NULL
incquant <- FALSE
}
if (!incquant) lines(1:length(stats[2, ]), stats[2, ], lwd = 3)
if (incquant) {
if (!is.null(stats2)) {
if (length(quantcol) < 2)
quantcol <- c(quantcol, "darkgray")
polygon(x = c(1:length(stats2[2, ]), length(stats2[2, ]):1),
y = c(stats2[1, ], rev(stats2[2, ])), col = quantcol[2],
border = FALSE)
}
polygon(x = c(1:length(stats[2, ]), length(stats[2, ]):1),
y = c(stats[1, ], rev(stats[3, ])), col = quantcol[1],
border = FALSE)
lines(1:length(stats[2, ]), stats[2, ], lwd = 3)
if (oneIt) lines(dat[2, mm, , drop = FALSE] , lwd=1)
}
}
if (X == nr & xaxis)
axis(side = 1, labels = TRUE, cex.axis = cex.axis)
if (X != nr | !xaxis)
axis(side = 1, labels = FALSE)
if (mm == 1 & yaxis) {
axis(side = 2, labels = TRUE, cex.axis = cex.axis, las = 1)
mtext(side = 2, ylab, cex = cex.lab, line = 3)
}
if (parOR) {
if (xaxis)
axis(side = 1, labels = TRUE, cex.axis = cex.axis, las = 1)
if (yaxis & mm == 1)
axis(side = 2, labels = TRUE, cex.axis = cex.axis, las = 1)
}
axis(side = 2, labels = FALSE)
MPcol <- rep(MPcol, MSEobj@nMPs)[1:MSEobj@nMPs]
if (incMP & X == 1 & !parOR)
mtext(side = 3, MSEobj@MPs[mm], cex = MPcex, col=MPcol[mm])
if (incMP & parOR)
mtext(side = 3, MSEobj@MPs[mm], cex = MPcex, col=MPcol[mm])
# Legend #
if (mm == 1 & incLeg & (traj == "quant"||traj=="both") & X == 1) {
if (incquant) {
if (is.null(stats2)) {
pquants <- quants
if (max(quants) < 1) pquants <- quants * 100
legend(legPos, legend = c(expression("50"^"th" * " (median)"),
bquote(.(pquants[1])^th ~ and ~ .(pquants[2])^th)),
pch = 22, title = "Percentile", pt.bg = c("black", quantcol[1], quantcol[2]),
bty = "n", cex = cex.leg, xpd = NA, col = "black")
}
if (!is.null(stats2)) {
pquants <- quants
if (max(quants) < 1)
pquants <- quants * 100
legend(legPos, legend = c(expression("50"^"th" * " (median)"),
bquote(.(pquants[1])^th ~ and ~ .(pquants[2])^th),
bquote(.(pquants[3])^th ~ and ~ .(pquants[4])^th)),
pch = 22, title = "Percentile", pt.bg = c("black", quantcol[1], quantcol[2]),
bty = "n", cex = cex.leg, xpd = NA, col = "black")
}
} else {
legend(legPos, legend = "Median", pch = 15, col = "black", bty = "n", cex = 1.25)
}
}
if (!is.null(yline))
abline(h = yline[X], lwd = 2, col = "darkgray", lty = 2)
if (!is.null(xline)) {
for (xx in 1:length(xline)) {
abline(v = xline[xx], lwd = 2, col = "darkgray", lty = xx+1)
}
}
}
}
mtext(side = 1, "Projection Years", line = 2, cex = cex.lab, outer = TRUE)
if (!parOR) par(op)
invisible(Dat)
}
#' Performance Whisker Plot
#'
#' A NAFO / ICCAT / SSB style MSE performance whisker plot
#'
#'
#' @param MSEobj An object of class MSE
#' @return A box plot of performance
#' @author T. Carruthers
#' @export PWhisker
PWhisker<-function(MSEobj){#},Pnames=c("POF","C30","D30","LD","DNC","LDNC","PGK","AAVC")){
P10 <- P50 <- PNOF <- LTY <- STY <- AAVY <- VY <- array(NA,c(MSEobj@nsim, MSEobj@nMPs))
Pnames<-c("B10","B50","PNOF","LTY","AAVY")
nsim<-MSEobj@nsim
nMPs<-MSEobj@nMPs
nperf<-length(Pnames)
store<-array(NA,c(MSEobj@nMPs,nperf,5))
y1 <- 1:(MSEobj@proyears - 1)
y2 <- 2:MSEobj@proyears
yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
P10[,mm] <- round(apply(MSEobj@SB_SBMSY[, mm, ] <= 0.1,1,mean, na.rm = T) * 100, 1)
P50[,mm] <- round(apply(MSEobj@SB_SBMSY[, mm, ] <= 0.5,1,mean, na.rm = T) * 100, 1)
PNOF[,mm] <- round(apply(MSEobj@F_FMSY[, mm, ] <= 1,1,mean, na.rm = T) * 100, 1)
LTY[,mm] <- round(apply(MSEobj@Catch[, mm, yend]/RefYd >= 0.5,1,mean, na.rm = T), 3) * 100
AAVY[,mm] <- apply((((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])/MSEobj@Catch[, mm, y2])^2)^0.5, 1, mean, na.rm = T)
#VY[,mm] <- round(apply(AAVY <= 0.15, na.rm = T), 3) * 100
store[mm,1,]<-quantile(P10[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,2,]<-quantile(P50[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,3,]<-quantile(PNOF[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,4,]<-quantile(LTY[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,5,]<-quantile(AAVY[,mm],c(0.05,0.25,0.5,0.72,0.95))
#store[mm,6,]<-quantile(VY[,mm],c(0.05,0.25,0.5,0.72,0.95))
}
op <- par(mfrow=c(nperf,1),mai=c(0.1,0.3,0.01,0.05),omi=c(1.2,0.4,0.05,0.01))
for(i in 1:nperf){
xlab=F
if(i==nperf)xlab=T
custombar(dat=store[,i,],MPnams=MSEobj@MPs,xlab=xlab)
mtext(Pnames[i],2,line=3,font=2)
}
mtext("Management Procedure",1,line=6.8,font=2,outer=T)
on.exit(par(op))
}
# #' Scatter plot of B/BMSY or B/B0 and F/FMSY for lastYrs
# #'
# #' Scatter plot of B/BMSY or B/B0 and F/FMSY for lastYrs
# #'
# #'
# #' @param MSEobj An object of class MSE
# #' @param MPs Optional subset by MP
# #' @param All Logical. Plot all points or just the mean?
# #' @param Var What to plot on the y-axis: \code{B_BMSY} or \code{SSB_SSB0}
# #' @param lastYrs Last number of years in projection to calculate statistics
# #' @param Fref Location of F statistic reference line
# #' @param BMSYref Location of B_MSY statistic reference line
# #' @param B0ref Location of B_0 statistic reference line
# #' @param cex.MP size of MP label
# #' @param Fbg Logical. Include background colours for F-statistic?
# #' @param Bbg Logical. Include background colours for B-statistic?
# #' @param Props Logical. Display the proportion of points in each quadrant?
# #' @param TP Logical. Use transparent colours?
# #' @author A. Hordyk
# #' @export Splot
# Splot <- function(MSEobj = NULL, MPs = NA, All = TRUE, Var = c("B_BMSY",
# "SSB_SSB0"), lastYrs = 10, Fref = 1, BMSYref = 1, B0ref = 0.4, cex.MP = 1,
# Fbg = FALSE, Bbg = FALSE, Props = FALSE, TP = FALSE) {
#
# Var <- match.arg(Var)
# if (!any(is.na(MPs)))
# MSEobj <- Sub(MSEobj, MPs = MPs)
#
# nMPs <- MSEobj@nMPs
# nyrs <- MSEobj@proyears
# nsim <- MSEobj@nsim
#
# perf <- MPStats(MSEobj)
# if (lastYrs > MSEobj@proyears)
# lastYrs <- 10
# yrs <- (nyrs - lastYrs + 1):nyrs
#
# ord <- perf[[1]]
# MPord <- match(ord$MP, MSEobj@MPs)
# sims <- perf$BySim
# Nrow <- ceiling(sqrt(nMPs))
# Ncol <- ceiling(nMPs/Nrow)
# Yvar <- sims[["F_FMSY"]]
# Xvar <- sims[[Var]]
# YLim <- c(0, 2)
# XMax <- ceiling(min(max(apply(Xvar[, , yrs], c(1, 2), mean), na.rm = TRUE),
# 3)/0.5) * 0.5
# XLim <- c(0, XMax)
# YLab <- expression(F/F[MSY])
# XLab <- switch(Var, SSB_SSB0 = expression(B/B[0]), B_BMSY = expression(B/B[MSY]))
#
# Bref <- switch(Var, SSB_SSB0 = B0ref, B_BMSY = BMSYref)
#
# ColVec <- colorRampPalette(c("green", "orange", "red"))(nsim)
# op <- par(mfrow = c(Nrow, Ncol), mar = c(1, 1, 2, 0), oma = c(4, 5, 2, 1))
#
# pmat <- matrix(NA, nrow = Ncol, ncol = Nrow)
# pmat[1:nMPs] <- 1:nMPs
# pmat <- t(pmat)
#
#
# for (mm in seq_along(MPord)) {
# xx <- MPord[mm]
# if (!All)
# Xs <- apply(Xvar[, xx, yrs], 1, mean)
# if (!All)
# Ys <- apply(Yvar[, xx, yrs], 1, mean)
# if (All)
# Xs <- as.numeric(Xvar[, xx, yrs])
# if (All)
# Ys <- as.numeric(Yvar[, xx, yrs])
# colN <- ceiling((Xs/max(XLim)) * nsim) + 1
# colN[colN > nsim] <- nsim
# Cols <- ColVec[colN]
# if (TP)
# Cols <- makeTransparent(Cols, alpha = 95)
# plot(Xs, Ys, xlab = "", ylab = "", bty = "n", xlim = XLim, ylim = YLim,
# axes = FALSE, pch = 18, col = Cols)
#
# if (nrow(pmat) > 1) {
# if (mm %in% pmat[, 1]) {
# axis(side = 2, labels = TRUE, las = 1)
# } else axis(side = 2, labels = FALSE)
# if (mm %in% pmat[Nrow, ]) {
# axis(side = 1, labels = TRUE)
# } else axis(side = 1, labels = FALSE)
# nas <- apply(pmat, 1, sum)
# rr <- which.max(nas)
# nas2 <- apply(pmat, 2, sum)
# cc <- which(is.na(nas2))
# if (mm %in% pmat[rr, cc])
# axis(side = 1, labels = TRUE)
# } else {
# if (mm == 1)
# axis(side = 2, labels = TRUE, las = 1)
# if (mm != 1)
# axis(side = 2, labels = FALSE)
# axis(side = 1, labels = TRUE)
# }
#
# abline(v = Fref, lty = 1, col = makeTransparent("grey", 150))
# abline(h = Bref, lty = 1, col = makeTransparent("grey", 150))
# mtext(side = 3, MSEobj@MPs[xx], cex = cex.MP)
#
# # Background colours
# Alpha <- 30
# # polygons
# OutCol <- rgb(red = 255, green = 0, blue = 0, alpha = Alpha, names = NULL,
# maxColorValue = 255)
# vl <- Fref
# hl <- Bref
# if (Bbg)
# polygon(x = c(0, max(max(Xs), XLim[2]), max(max(Xs), XLim[2]),
# 0), y = c(0, 0, hl, hl), col = OutCol, border = NA)
# if (Fbg)
# polygon(x = c(vl, max(max(Xs), XLim[2]), max(max(Xs), XLim[2]),
# vl), y = c(0, 0, max(max(Ys), YLim[2]), max(max(Ys), YLim[2])),
# col = OutCol, border = NA)
#
# if (Props) {
# P1 <- paste0(round(sum(Xs <= Fref & Ys >= Bref)/length(Xs) *
# 100, 1), "%")
# P2 <- paste0(round(sum(Xs > Fref & Ys >= Bref)/length(Xs) *
# 100, 1), "%")
# P3 <- paste0(round(sum(Xs > Fref & Ys < Bref)/length(Xs) *
# 100, 1), "%")
# P4 <- paste0(round(sum(Xs <= Fref & Ys < Bref)/length(Xs) *
# 100, 1), "%")
# legend("topleft", P1, bty = "n", cex = 1.25)
# legend("topright", P2, bty = "n", cex = 1.25)
# legend("bottomright", P3, bty = "n", cex = 1.25)
# legend("bottomleft", P4, bty = "n", cex = 1.25)
# }
# }
# mtext(side = 1, outer = TRUE, XLab, cex = 1.25, line = 2.5)
# mtext(side = 2, outer = TRUE, YLab, cex = 1.25, line = 2)
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears,
# "(last", lastYrs, "years)")
# mtext(side = 3, Years, cex = 1.25, outer = TRUE)
# par(op)
# }
#
# #' Trade-off plots for an MSE object
# #'
# #' Three figures showing trade-offs between fishing mortality, biomass, and yield.
# #'
# #' @param MSEobj An object of class 'MSE'
# #' @param nam Name of the plot
# #' @author T. Carruthers & A. Hordyk
# #' @seealso \link{TradePlot} \link{PerformanceMetric}
# #' @describeIn Tplot Used in the plot method for MSE objects that shows trade-off between
# #' yield versus probability of overfishing and biomass levels (relative to BMSY).
# #' @export
# Tplot_old <- function(MSEobj, nam = NA) {
# old_par <- par(no.readonly = TRUE)
# on.exit(par(old_par))
#
# FMSYr <- quantile(MSEobj@F_FMSY, c(0.001, 0.9), na.rm = T)
# BMSYr <- quantile(MSEobj@SB_SBMSY, c(0.001, 0.975), na.rm = T)
#
# colsse <- rainbow(100, start = 0, end = 0.36)[1:100]
# colB <- rep(colsse[100], ceiling(BMSYr[2] * 100))
# colB[1:100] <- colsse
# colB <- makeTransparent(colB, 60)
# colsse <- rainbow(200, start = 0, end = 0.36)[200:1]
# colF <- rep(colsse[200], ceiling(FMSYr[2] * 100))
# colF[1:200] <- colsse
# colF <- makeTransparent(colF, 60)
#
# Yd <- rep(NA, MSEobj@nMPs)
# P10 <- rep(NA, MSEobj@nMPs)
# P50 <- rep(NA, MSEobj@nMPs)
# P100 <- rep(NA, MSEobj@nMPs)
# POF <- rep(NA, MSEobj@nMPs)
# yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
# RefYd <- MSEobj@OM$RefY
#
# for (mm in 1:MSEobj@nMPs) {
# Yd[mm] <- round(mean(apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd,
# na.rm = T) * 100, 1)
# # cbind(MSEobj@Catch[,mm,yind],unlist(MSEobj@OM$MSY))
# POF[mm] <- round(sum(MSEobj@F_FMSY[, mm, ] >= 1, na.rm = T)/prod(dim(MSEobj@F_FMSY[,
# mm, ]), na.rm = T) * 100, 1)
# P10[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
# mm, ])) * 100, 1)
# P50[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.5, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
# mm, ])) * 100, 1)
# P100[mm] <- round(sum(MSEobj@B_BMSY[, mm, ] <= 1, na.rm = T)/prod(dim(MSEobj@B_BMSY[,
# mm, ])) * 100, 1)
# }
#
# # dev.new2(width=7,height=7)
# par(mfrow = c(2, 2), mar = c(5, 4, 1, 1), oma = c(0, 0, 2, 0))
#
# tradeoffplot(POF, Yd, "Prob. of overfishing (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
# tradeoffplot(P100, Yd, "Prob. biomass < BMSY (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
# tradeoffplot(P50, Yd, "Prob. biomass < 0.5BMSY (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
# tradeoffplot(P10, Yd, "Prob. biomass < 0.1BMSY (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
#
# if (is.na(nam))
# mtext(deparse(substitute(MSEobj)), 3, outer = T, line = 0.3, font = 2)
# if (!is.na(nam) & !is.character(nam))
# mtext(MSEobj@Name, 3, outer = T, line = 0.3, font = 2)
# if (!is.na(nam) & is.character(nam))
# mtext(nam, 3, outer = T, line = 0.3, font = 2)
# return(invisible())
# }
# #' @describeIn Tplot Simpler plot that compares long-term yield (LTY:
# #' fraction of simulations getting over half FMSY yield in the last ten years
# #' of the projection), short-term yield (STY: fraction of simulations getting
# #' over half FMSY yield in the first ten years of the projection), variability
# #' in yield (VY: fraction of simulations where average annual variability in
# #' yield is less than 10 per cent) and biomass level (B10: the fraction of
# #' simulations in which biomass stays above 10 percent of BMSY).
# #' @export
# Tplot2_old <- function(MSEobj, nam = NA) {
# old_par <- par(no.readonly = TRUE)
# on.exit(par(old_par))
#
# LTY <- rep(NA, MSEobj@nMPs)
# STY <- rep(NA, MSEobj@nMPs)
# VY <- rep(NA, MSEobj@nMPs)
# B10 <- rep(NA, MSEobj@nMPs)
# yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
# ystart <- 1:5
# RefYd <- MSEobj@OM$RefY
# y1 <- 1:(MSEobj@proyears - 1)
# y2 <- 2:MSEobj@proyears
# for (mm in 1:MSEobj@nMPs) {
# LTY[mm] <- round(sum(MSEobj@Catch[, mm, yend]/RefYd > 0.5, na.rm = T)/(MSEobj@nsim *
# length(yend)), 3) * 100
# STY[mm] <- round(sum(MSEobj@Catch[, mm, ystart]/RefYd > 0.5, na.rm = T)/(MSEobj@nsim *
# length(ystart)), 3) * 100
# AAVY <- apply(((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])^2)^0.5,
# 1, mean, na.rm = T)/apply(MSEobj@Catch[, mm, y2], 1, mean, na.rm = T)
# VY[mm] <- round(sum(AAVY < 0.1, na.rm = T)/MSEobj@nsim, 3) * 100
# B10[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] > 0.1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
# mm, ])), 3) * 100
# }
# par(mfrow = c(1, 2), mar = c(5, 4, 1, 1), oma = c(0, 0, 2, 0))
#
# tradeoffplot(STY, LTY, "P(Short term yield > 0.5 FMSY)", "P(Long term yield > 0.5 FMSY)",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 1, hl = 1)
# tradeoffplot(B10, VY, "P(Biomass > 0.1 BMSY)", "P(CV in yield < 0.1)",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 1, hl = 1)
# if (is.na(nam))
# mtext(deparse(substitute(MSEobj)), 3, outer = T, line = 0.3, font = 2)
# if (!is.na(nam))
# mtext(MSEobj@Name, 3, outer = T, line = 0.3, font = 2)
# return(invisible())
# }
#
#
# #' @describeIn Tplot By default, trade-off plots among LTY, STY, and biomass level B50
# #' (fraction of simulations in which biomass stays above 50 percent of BMSY), and
# #' Average Annual Variability in Yield (AAVY).
#
# #' @export
# Tplot3_old <- function(MSEobj, ..., lims=c(0.2, 0.2, 0.8, 0.8)) {
# PMlist <- unlist(list(...))
# if(length(PMlist) == 0) PMlist <- c("LTY", "STY", "P50", "AAVY")
# if (class(PMlist) != 'character') stop("Must provide names of PM methods")
# # check
#
# for (X in seq_along(PMlist))
# if (!PMlist[X] %in% avail("PM")) stop(PMlist[X], " is not a valid PM method")
# if (length(PMlist)<2) stop("Must provided more than 1 PM method")
#
# runPM <- vector("list", length(PMlist))
# for (X in 1:length(PMlist)) runPM[[X]] <- eval(call(PMlist[X], MSEobj))
#
# PlotList <- combn(unique(PMlist), 2)
# lims <- rep(lims, 100)[1:length(PMlist)]
#
# n.col <- ceiling(sqrt(ncol(PlotList)))
# n.row <- ceiling(ncol(PlotList)/n.col)
#
# m <- matrix(1:(n.col*n.row), ncol=n.col, nrow=n.row, byrow=FALSE)
# xmin <- xmax <- ymin <- ymax <- x <- y <- Class <- label <- fontface <- NULL
# plots <- listout <- list()
# for (pp in 1:ncol(PlotList)) {
# yPM <- PlotList[1,pp]
# yvals <- runPM[[match(yPM, PMlist)]]@Mean
# ycap <- runPM[[match(yPM, PMlist)]]@Caption
# yname <- runPM[[match(yPM, PMlist)]]@Name
# yline <- lims[match(yPM, PMlist)]
#
# xPM <- PlotList[2,pp]
# xvals <- runPM[[match(xPM, PMlist)]]@Mean
# xcap <- runPM[[match(xPM, PMlist)]]@Caption
# xname <- runPM[[match(xPM, PMlist)]]@Name
# xline <- lims[match(xPM, PMlist)]
#
# xlim <- c(0, max(max(xvals, 1)))
# ylim <- c(0, max(max(yvals, 1)))
#
# xrect <- data.frame(xmin=0, xmax=xline, ymin=0, ymax=max(ylim))
# yrect <- data.frame(xmin=0, xmax=max(xlim), ymin=0, ymax=yline)
#
# MPType <- MPtype(MSEobj@MPs)
# Class <- MPType[match(MSEobj@MPs, MPType[,1]),2]
#
# df <- data.frame(x=xvals, y=yvals, label=MSEobj@MPs, Class=Class,
# pass=xvals>xline & yvals>yline, fontface="plain", xPM=xPM, yPM=yPM)
# df$fontface <- as.character(df$fontface)
# df$fontface[!df$pass] <- "italic"
# df$fontface <- factor(df$fontface)
# listout[[pp]] <- df
# plots[[pp]] <- ggplot2::ggplot() +
# ggplot2::geom_rect(data=xrect, ggplot2::aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax), fill='gray80', alpha=0.4) +
# ggplot2::geom_rect(data=yrect, ggplot2::aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax), fill='gray80', alpha=0.4)
#
# # plots[[pp]] <- ggplot(df, aes(x, y, shape=Class, color=Class, label=label)) +
# plots[[pp]] <- plots[[pp]] +
# ggplot2::geom_point(data=df, ggplot2::aes(x, y, shape=Class, color=Class), size=2) +
# ggrepel::geom_text_repel(data=df, ggplot2::aes(x, y, color=Class, label=label, fontface = fontface), show.legend=FALSE) +
# ggplot2::xlab(xcap) + ggplot2::ylab(ycap) +
# ggplot2::xlim(xlim) + ggplot2::ylim(ylim) +
# ggplot2::theme_classic() +
# ggplot2::theme(axis.title.x = ggplot2::element_text(size=11),
# axis.title.y = ggplot2::element_text(size=11),
# legend.text=ggplot2::element_text(size=12)) +
# ggplot2::labs(shape= "MP Type", color="MP Type")
#
# }
# out <- do.call("rbind", listout)
# tab <- table(out$label, out$pass)
# passall <- rownames(tab)[tab[,ncol(tab)] == ncol(PlotList)]
# tt <- summary(MSEobj, PMlist, silent=TRUE)
# tt$Satisificed <- FALSE
# tt$Satisificed[match(passall, tt$MP)] <- TRUE
#
# join_plots(plots, n.col, n.row)
# tt
#
# }
#
#
#
# #' Generic Trade-off Plot
# #'
# #' Creates a trade-off plot (up to four panels) of built-in performance
# #' metrics.
# #'
# #' Returns a list containing the names of performance metrics that meet the
# #' minimum performance metrics for each trade-off, and ranks the MPs by
# #' increasing distance from the top-right corner.
# #'
# #' @param MSEobj Object of class MSE, output of the runMSE function
# #' @param XAxis Character string describing the performance metrics for the
# #' x-axis (or x-axes if vector; max 4). Must be chosen for list of existing PMs
# #' and same length as YAxis. See \code{PMs}
# #' @param YAxis Character string describing the performance metrics for the
# #' y-axis (or y-axes if vector; max 4). Must be chosen for list of existing PMs
# #' and same length as XAxis. See \code{PMs}
# #' @param XThresh Minimum threshold values in percent (i.e., 50 = 50\%) for the
# #' x-axes (must be same length as XAxis)
# #' @param YThresh Minimum threshold values in percent (i.e., 50 = 50\%) for the
# #' y-axes (must be same length as YAxis)
# #' @param maxVar Reference for average annual variability in yield in percent
# #' @param BmsyRef Reference level of BMSY, in proportion, i.e., 0.5 = 0.5BMSY
# #' @param B0Ref Reference level of B0, in proportion, i.e., 0.2 = 0.2B0
# #' @param AvailMPs vector of MPs that *could* be applied to the fishery, i.e.,
# #' sufficient data exists. These a plotted with different symbol
# #' @param ShowLabs Logical to specify if MP labels are shown
# #' @param ShowCols Logical to specify if background colors are shown
# #' @author A. Hordyk
# #' @export
# TradePlot_old <- function(MSEobj, XAxis = c("Overfishing", "Biomass:BMSY"),
# YAxis = c("Long-term Yield", "AnnualVar"), XThresh = c(30, 80),
# YThresh = c(0, 50), maxVar = 15, BmsyRef = 0.5, B0Ref = 0.2,
# AvailMPs = NULL, ShowLabs = FALSE, ShowCols = TRUE) {
# PMs <- c("Long-term Yield", "Short-term Yield", "Overfishing", "Biomass:BMSY",
# "Biomass:B0", "AnnualVar")
# op <- par(no.readonly=TRUE)
# on.exit(par(op))
# # Error Checks
# if (prod(XAxis %in% PMs) != 1) {
# message("Available Performance Metrics")
# print(PMs)
# stop("Invalid XAxis Performance Metrics")
# }
# if (prod(YAxis %in% PMs) != 1) {
# message("Available Performance Metrics")
# print(PMs)
# stop("Invalid YAxis Performance Metrics")
# }
# if (length(XAxis) > 4)
# stop("Too many Performance Metrics (max 4)")
# if (length(YAxis) > 4)
# stop("Too many Performance Metrics (max 4)")
# if (length(XAxis) != length(YAxis))
# stop("XAxis must be of same length as YAxis")
# if (length(XThresh) != length(XAxis) | length(YThresh) != length(XAxis))
# warning("Risk Threshold not same length as number of PMs")
#
# Yd <- rep(NA, MSEobj@nMPs)
# BMSYref <- rep(NA, MSEobj@nMPs)
# B0ref <- rep(NA, MSEobj@nMPs)
# PNOF <- rep(NA, MSEobj@nMPs)
# LTY <- rep(NA, MSEobj@nMPs)
# STY <- rep(NA, MSEobj@nMPs)
# VY <- rep(NA, MSEobj@nMPs)
#
# y1 <- 1:(MSEobj@proyears - 1)
# y2 <- 2:MSEobj@proyears
#
# ystart <- 1:5
# yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
#
# RefYd <- MSEobj@OM$RefY
# if (maxVar < 1)
# maxVar <- maxVar * 100
#
# for (mm in 1:MSEobj@nMPs) {
# PNOF[mm] <- round(sum(MSEobj@F_FMSY[, mm, ] <= 1, na.rm = T)/prod(dim(MSEobj@F_FMSY[,
# mm, ]), na.rm = T) * 100, 1)
# BMSYref[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] > BmsyRef, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[, mm, ])) * 100, 1)
# B0ref[mm] <- round(sum((MSEobj@SB_SBMSY[, mm, ] * MSEobj@OM$SSBMSY_SSB0) >
# B0Ref, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[, mm, ])) * 100, 1)
# # LTY[mm]<-round(sum(MSEobj@Catch[,mm,yend]/RefYd>0.5,na.rm=T)/(MSEobj@nsim*length(yend)),3)*100
# # STY[mm]<-round(sum(MSEobj@Catch[,mm,ystart]/RefYd>0.5,na.rm=T)/(MSEobj@nsim*length(ystart)),3)*100
# LTY[mm] <- round(mean(apply(MSEobj@Catch[, mm, yend], 1, mean, na.rm = T)/RefYd,
# na.rm = T) * 100, 1)
# STY[mm] <- round(mean(apply(MSEobj@Catch[, mm, ystart], 1, mean, na.rm = T)/RefYd,
# na.rm = T) * 100, 1)
# AAVY <- apply((((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])/MSEobj@Catch[,
# mm, y2])^2)^0.5, 1, mean, na.rm = T)
# VY[mm] <- round(sum(AAVY < (maxVar/100), na.rm = T)/MSEobj@nsim,
# 3) * 100
# }
#
# for (xx in seq_along(XAxis)) {
# name <- paste0("X", xx)
# name1 <- paste0("XLab", xx)
# assign(name, GetStat(XAxis[xx], LTY, STY, PNOF, BMSYref, B0ref,
# VY))
# assign(name1, StatLab(XAxis[xx], maxVar, BmsyRef, B0Ref))
# name <- paste0("Y", xx)
# name1 <- paste0("YLab", xx)
# assign(name, GetStat(YAxis[xx], LTY, STY, PNOF, BMSYref, B0ref,
# VY))
# assign(name1, StatLab(YAxis[xx], maxVar, BmsyRef, B0Ref))
# }
#
# Nplot <- length(XAxis)
# if (Nplot == 1)
# par(mfrow = c(1, 1), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
# if (Nplot == 2)
# par(mfrow = c(1, 2), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
# if (Nplot == 3)
# par(mfrow = c(1, 3), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
# if (Nplot == 4)
# par(mfrow = c(2, 2), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
#
# OutList <- list()
# for (xx in seq_along(XAxis)) {
# Xname <- paste0("X", xx)
# XLab <- paste0("XLab", xx)
# Yname <- paste0("Y", xx)
# YLab <- paste0("YLab", xx)
# rr <- tradeoffplot4(x = get(Xname), y = get(Yname), get(XLab),
# get(YLab), labs = MSEobj@MPs[1:MSEobj@nMPs], vl = XThresh[xx],
# hl = YThresh[xx], ShowLabs = ShowLabs, ShowCols = ShowCols,
# AvailMPs = AvailMPs)
#
# labs <- MSEobj@MPs[1:MSEobj@nMPs]
# ind <- which(labs %in% rr)
# tempDF <- data.frame(MP = rr, X = get(Xname)[ind], Y = get(Yname)[ind])
# Dist <- NULL # calculate distance from corner
# for (X in 1:length(tempDF[, 2])) Dist[X] <- euc.dist(c(tempDF[X,
# 2], tempDF[X, 3]), c(100, 100))
# tempDF <- tempDF[order(Dist), ]
# rownames(tempDF) <- 1:nrow(tempDF)
# OutList[[xx]] <- tempDF
# }
#
#
# OutList
#
# }
#' Calculate Value Of Information
#'
#' A function that relates operating model parameters and parameters of the
#' observation model to yield (by default). A user can also specific their own
#' utility values (Ut) which is arranged in a matrix of nsim rows and nMP
#' columns.
#'
#'
#' @param MSEobj An object of class MSE
#' @param ncomp Maximum number of variables to examine per MP
#' @param nbins Number of percentile bins for sampled parameters of the
#' operating model or observation model, which is used for calculating
#' variability in utility across the sampled range of each parameter
#' @param maxrow maximum number of MPs per plot
#' @param Ut A matrix of user-specified utility values of nsim rows and nMPs
#' columns
#' @param Utnam The name of the utility measure for plotting
#' @param plot Logical. Show the plot?
#' @author T. Carruthers
#' @export VOI
VOI <- function(MSEobj, ncomp = 6, nbins = 8, maxrow = 8, Ut = NA, Utnam = "Utility", plot=TRUE) {
objnam <- deparse(substitute(MSEobj))
nsim <- MSEobj@nsim
if (is.na(Ut[1])) {
Ut <- array(NA, c(nsim, MSEobj@nMPs))
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
Ut[, mm] <- apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd * 100
# POF[,mm]<-apply(MSEobj@F_FMSY[,mm,]>1,1,sum)/MSEobj@proyears
# P10[,mm]<-apply(MSEobj@B_BMSY[,mm,]<0.1,1,sum)/MSEobj@proyears
}
Utnam <- "Long-term yield relative to MSY (%)"
}
MPs <- MSEobj@MPs
nMPs <- MSEobj@nMPs
onlycor <- c("RefY", "A", "MSY", "Linf", "t0", "OFLreal", "Spat_targ")
vargood <- (apply(MSEobj@OM, 2, sd)/(apply(MSEobj@OM, 2, mean)^2)^0.5) > 0.005
# MSEobj@OM<- MSEobj@OM[,(!names(MSEobj@OM)%in%onlycor)&vargood]
vargood[grep("qvar", names(MSEobj@OM))] <- FALSE
MSEobj@OM <- MSEobj@OM[, which((!names(MSEobj@OM) %in% onlycor) & vargood)]
OMp <- apply(MSEobj@OM, 2, quantile, p = seq(0, 1, length.out = nbins + 1), na.rm = TRUE)
Obsp <- apply(MSEobj@Obs, 2, quantile, p = seq(0, 1, length.out = nbins + 1), na.rm = TRUE)
OMv <- array(NA, c(nMPs, ncol(MSEobj@OM), nbins))
Obsv <- array(NA, c(nMPs, ncol(MSEobj@Obs), nbins))
for (mm in 1:nMPs) {
for (j in 1:nbins) {
for (i in 1:ncol(MSEobj@OM)) {
cond <- MSEobj@OM[, i] > OMp[j, i] & MSEobj@OM[, i] < OMp[j + 1, i]
OMv[mm, i, j] <- mean(Ut[cond, mm], na.rm = T)
}
for (i in 1:ncol(MSEobj@Obs)) {
cond <- MSEobj@Obs[, i] > Obsp[j, i] & MSEobj@Obs[, i] < Obsp[j + 1, i]
Obsv[mm, i, j] <- mean(Ut[cond, mm], na.rm = T)
}
}
}
# cbind(names(MSEobj@OM), OMs[mm,])
# -- Operating model variables
OMs <- apply(OMv, 1:2, sd, na.rm = T)
OMstr <- array("", c(nMPs * 2, ncomp + 1))
for (mm in 1:nMPs) {
ind <- order(OMs[mm, ], decreasing = T)[1:ncomp]
OMstr[1 + (mm - 1) * 2, 1] <- MPs[mm]
OMstr[1 + (mm - 1) * 2, 2:(1 + ncomp)] <- names(MSEobj@OM[ind])
OMstr[2 + (mm - 1) * 2, 2:(1 + ncomp)] <- round(OMs[mm, ind], 2)
}
OMstr <- data.frame(OMstr)
names(OMstr) <- c("MP", 1:ncomp)
# -- Observation model variables
slots <- c("Cat", "Cat", "AvC", "AvC", "CAA", "CAA", "CAL", "CAL",
"Ind", "Dep", "Dep", "Dt", "Dt", "Mort", "FMSY_M", "SSBMSY_SSB0", "L50",
"L95", "LFC", "LFS", "Abun", "Abun", "vbK", "vbt0", "vbLinf", "Steep",
"Iref", "Cref", "Bref", "ML")
Obsnam <- c("Cbias", "Csd", "Cbias", "Csd", "CAA_nsamp", "CAA_ESS",
"CAL_nsamp", "CAL_ESS", "Isd", "Dbias", "Derr", "Dbias", "Derr",
"Mbias", "FMSY_Mbias", "BMSY_B0bias", "lenMbias", "lenMbias", "LFCbias",
"LFSbias", "Abias", "Aerr", "Kbias", "t0bias", "Linfbias", "hbias",
"Irefbias", "Crefbias", "Brefbias", "")
Obss <- apply(Obsv, 1:2, sd, na.rm = T)
Obsstr <- array("", c(nMPs * 2, ncomp + 1))
for (mm in 1:nMPs) {
relobs <- Obsnam[slots %in% unlist(strsplit(Required(MPs[mm])[,
2], split = ", "))]
ind <- (1:ncol(MSEobj@Obs))[match(relobs, names(MSEobj@Obs))]
pos <- names(MSEobj@Obs)[ind] # possible observation processes
maxy <- min(max(1, length(pos)), ncomp, na.rm = T)
ind2 <- order(Obss[mm, ind], decreasing = T)[1:maxy]
Obsstr[1 + (mm - 1) * 2, 1] <- MPs[mm]
Obsstr[1 + (mm - 1) * 2, 2:(1 + maxy)] <- pos[ind2]
Obsstr[2 + (mm - 1) * 2, 2:(1 + maxy)] <- round(Obss[mm, ind][ind2],
2)
}
Obsstr <- data.frame(Obsstr)
names(Obsstr) <- c("MP", 1:ncomp)
ncols <- 40
# colsse<-makeTransparent(rainbow(ncols,start=0,end=0.36),95)[ncols:1]
colsse <- makeTransparent(rainbow(ncols, start = 0, end = 0.36), 90)[ncols:1]
minsd <- 0
maxsd <- max(OMs, na.rm = T)
coly <- ceiling(OMs/maxsd * ncols)
# Operating model variables
mbyp <- split(1:nMPs, ceiling(1:nMPs/maxrow))
ylimy = c(0, max(OMv, na.rm = T) * 1.2)
if (plot) {
for (pp in 1:length(mbyp)) {
op <- par(mfrow = c(length(mbyp[[pp]]), ncomp), mai = c(0.15, 0.1, 0.15,
0.05), omi = c(0.1, 0.9, 0.3, 0.05))
for (mm in mbyp[[pp]]) {
for (cc in 1:ncomp) {
rind <- (mm - 1) * 2 + 1
y <- Ut[, mm]
cind <- match(OMstr[rind, 1 + cc], names(MSEobj@OM))
x <- MSEobj@OM[, cind]
plot(x, y, col = "white", axes = F, ylim = ylimy)
axis(1, pretty(OMp[, cind]), pretty(OMp[, cind]), cex.axis = 0.8,
padj = -1.5)
abline(v = OMp[, cind], col = "#99999960")
points(x, y, col = colsse[coly[mm, cind]], pch = 19, cex = 0.8)
x2 <- (OMp[1:nbins, cind] + OMp[2:(nbins + 1), cind])/2
y2 <- OMv[mm, cind, ]
lines(x2, y2)
legend("bottomright", legend = round(OMs[mm, cind], 2), bty = "n", cex = 0.8)
legend("topleft", legend = OMstr[rind, 1 + cc], bty = "n", cex = 0.85)
if (cc == 1) {
mtext(MPs[mm], 2, font = 2, outer = F, cex = 0.8, line = 2)
ytick <- pretty(seq(ylimy[1], ylimy[2] * 1.3, length.out = 10))
axis(2, ytick, ytick, cex.axis = 0.8)
} # only first column
} # parameters (columns)
} # MPs (rows)
mtext(Utnam, 2, outer = T, cex = 0.9, line = 3.5)
mtext(paste("Operating model parameters: ", objnam, "@OM", sep = ""), 3, outer = T, font = 2, cex = 0.9)
} # Plots
# Observation model values
ylimy = c(0, max(Obsv, na.rm = T) * 1.2)
minsd <- 0
maxsd <- max(Obss)
coly <- ceiling(Obss/maxsd * ncols)
if (sum(is.na(Obsstr) | Obsstr == "") < (ncomp + 1) * nMPs * 2 - nMPs)
{
# only if there is data to plot
for (pp in 1:length(mbyp)) {
op <- par(mfrow = c(length(mbyp[[pp]]), ncomp), mai = c(0.15,
0.1, 0.15, 0.05), omi = c(0.1, 0.9, 0.3, 0.05),no.readonly=TRUE)
on.exit(par(op))
for (mm in mbyp[[pp]]) {
rind <- (mm - 1) * 2 + 1
npres <- sum(Obsstr[rind + 1, ] != "")
for (cc in 1:ncomp) {
if (!is.na(npres) & cc < (npres + 1)) {
y <- Ut[, mm]
cind <- match(Obsstr[rind, 1 + cc], names(MSEobj@Obs))
x <- MSEobj@Obs[, cind]
plot(x, y, col = "white", axes = F, ylim = ylimy)
axis(1, pretty(Obsp[, cind]), pretty(Obsp[, cind]), cex.axis = 0.8, padj = -2)
abline(v = Obsp[, cind], col = "#99999960")
points(x, y, col = colsse[coly[mm, cind]], pch = 19, cex = 0.8)
x2 <- (Obsp[1:nbins, cind] + Obsp[2:(nbins + 1), cind])/2
y2 <- Obsv[mm, cind, ]
lines(x2, y2)
legend("bottomright", legend = round(Obss[mm, cind], 2), bty = "n", cex = 0.8)
legend("topleft", legend = Obsstr[rind, 1 + cc], bty = "n", cex = 0.75)
if (cc == 1) {
mtext(MPs[mm], 2, font = 2, outer = F, cex = 0.6, line = 2)
ytick <- pretty(seq(ylimy[1], ylimy[2] * 1.3, length.out = 10))
axis(2, ytick, ytick, cex.axis = 0.8)
} # only first column
} else {
plot(0, type = "n", axes = FALSE, ann = FALSE)
if (cc == 1) {
mtext(MPs[mm], 2, font = 2, outer = F, cex = 0.6, line = 2)
} # only first column
}
} # parameters (columns)
} # MPs (rows)
mtext(Utnam, 2, outer = T, cex = 0.9, line = 3.5)
mtext(paste("Observation model parameters: ", objnam, "@Obs", sep = ""), 3, outer = T, font = 2, cex = 0.9)
} # Plots
} # if there is data to plot
}
list(OMstr, Obsstr)
} # VOI
#' Calculate Value Of Information 2
#'
#' A function that relates operating model parameters and parameters of the
#' observation model to relative yield (yield over last 5 years of projection
#' relative to a 'best F' scenario that maximizes yield).
#'
#' @param MSEobj An object of class MSE
#' @param ncomp Maximum number of observation variables to examine per MP
#' @param nbins Number of bins for sampled observation variables used for
#' calculating variability in utility across the sampled range of each
#' parameter
#' @param Ut A matrix of user-specified utility values of nsim rows and nMPs
#' columns
#' @param Utnam The name of the utility measure for plotting
#' @param lay Controls whether labels are in lay terms or not
#' @note VOI2 assumes that relative cost for each type of improvement in data
#' is linearly related to the number of samples (e.g. nCAAobs) or square
#' function of improved precision and bias e.g.: relative cost=
#' 1/(newCV/oldCV)^2
#' @author T. Carruthers
#' @export VOI2
VOI2 <- function(MSEobj, ncomp = 6, nbins = 4, Ut = NA, Utnam = "yield",
lay = F) {
op <- par(no.readonly=TRUE)
on.exit(par(op))
objnam <- deparse(substitute(MSEobj))
nsim <- MSEobj@nsim
if (is.na(Ut[1])) {
Ut <- array(NA, c(nsim, MSEobj@nMPs))
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
Ut[, mm] <- apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd *
100
# POF[,mm]<-apply(MSEobj@F_FMSY[,mm,]>1,1,sum)/MSEobj@proyears
# P10[,mm]<-apply(MSEobj@B_BMSY[,mm,]<0.1,1,sum)/MSEobj@proyears
}
}
MPs <- MSEobj@MPs
nMPs <- MSEobj@nMPs
# -- Observation model variables
slots <- c("Cat", "Cat", "AvC", "AvC", "CAA", "CAA", "CAL", "CAL",
"Ind", "Ind", "Dep", "Dep", "Dt", "Dt", "Mort", "FMSY_M", "SSBMSY_SSB0",
"L50", "L95", "LFC", "LFS", "Abun", "Abun", "vbK", "vbt0", "vbLinf",
"Steep", "Iref", "Cref", "Bref")
Obsnam <- c("Cbias", "Csd", "Cbias", "Csd", "CAA_nsamp", "CAA_ESS",
"CAL_nsamp", "CAL_ESS", "Isd", "betas", "Dbias", "Derr", "Dbias",
"Derr", "Mbias", "FMSY_Mbias", "BMSY_B0bias", "lenMbias", "lenMbias",
"LFCbias", "LFSbias", "Abias", "Aerr", "Kbias", "t0bias", "Linfbias",
"hbias", "Irefbias", "Crefbias", "Brefbias")
Obsnam2 <- c("Cbias", "Csd", "CAA_nsamp", "CAA_ESS", "CAL_nsamp", "CAL_ESS",
"Isd", "betas", "Dbias", "Derr", "Mbias", "FMSY_Mbias", "BMSY_B0bias",
"lenMbias", "LFCbias", "LFSbias", "Abias", "Aerr", "Kbias", "t0bias",
"Linfbias", "hbias", "Irefbias", "Crefbias", "Brefbias")
Obsnam3 <- c("Catch bias", "Catch error", "n CAA samples", "CAA ESS",
"n CAL samples", "CAL ESS", "Abun. Ind. error", "Hyperstability",
"Depln. bias", "Depln. error", "M bias", "FMSY/M bias", "BMSY/B0 bias",
"lenMbias", "Len 1st Cap bias", "Len full sel bias", "Cur Abund bias",
"Cur Abun err", "vB K bias", "vB t0 bias", "vB Linf bias", "Steepness bias",
"Ref index bias", "Ref catch bias", "Ref biomass bias")
# Types of observation error model 1:lognorm 2:percentile 3:replicates
# (higher is better) ##4:uniform on log 5:logit space
oem <- c(1, 2, 3, 3, 3, 3, 2, 4, 1, 2, 1, 1, 1, 1, 1, 1, 4, 2, 1, 1,
1, 2, 1, 1, 1)
# oem<-c( 2, 2, 3, 3, 3, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2,
# 2, 2, 1, 1, 1)
Obsd <- apply(MSEobj@Obs, 2, sd, na.rm = TRUE)
Obm <- apply(MSEobj@Obs, 2, mean, na.rm = TRUE)
Obmd <- apply(MSEobj@Obs, 2, quantile, p = 0.5, na.rm = TRUE)
maxcomp <- length(Obsnam2)
Obsv <- array(NA, c(nMPs, maxcomp, nbins))
Obsval <- array(NA, c(nMPs, maxcomp, nbins))
Obscost <- array(NA, c(nMPs, maxcomp, nbins))
Obsname <- list()
div <- seq(1, 2, length.out = nbins + 1)[2:(nbins + 1)] # for distributions
percs <- seq(0.5, 1, length.out = nbins + 1)[1:nbins] # for samples
percsCAA <- seq(0, 1, length.out = nbins + 2)[2:(nbins + 1)]
percUL <- seq(0, 0.25, length.out = nbins + 1)[2:(nbins + 1)]
percUU <- 1 - percUL
for (mm in 1:nMPs) {
Y1 <- Ut[, mm]
relobs <- Obsnam[slots %in% unlist(strsplit(Required(MPs[mm])[,
2], split = ", "))]
Obsname[[mm]] <- relobs
nr <- length(relobs)
if (length(relobs) > 0)
{
for (r in 1:nr) {
oemi <- match(relobs[r], Obsnam2)
obsi <- match(relobs[r], names(MSEobj@Obs))
for (cc in 1:nbins) {
if (oem[oemi] == 1) {
# Redundant SIR code for log-normal biases
T1 <- tdlnorm(MSEobj@Obs[, obsi], Obm[obsi], Obsd[obsi]/Obm[obsi])
# plot(MSEobj@Obs[,obsi],T1) # check
T2 <- tdlnorm(MSEobj@Obs[, obsi], Obm[obsi], Obsd[obsi]/(div[cc] *
Obm[obsi]))
W <- T2/T1
nrep2 <- nsim * 20
Y2 <- sample(Y1, nrep2 * 5, replace = T, prob = W)
Obsv[mm, r, cc] <- (mean(Y2) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- Obsd[obsi]/(div[cc] * Obm[obsi])
Obscost[mm, r, cc] <- div[cc]^2
} else if (oem[oemi] == 2) {
refval <- quantile(MSEobj@Obs[, obsi], percs[nbins:1][cc],
na.rm = TRUE)
ind <- MSEobj@Obs[, obsi] < refval
Obsv[mm, r, cc] <- (mean(Y1[ind]) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- mean(MSEobj@Obs[ind, obsi])
Obscost[mm, r, cc] <- 1/(Obsval[mm, r, cc]/mean(MSEobj@Obs[,
obsi]))^2
} else if (oem[oemi] == 3) {
refval <- quantile(MSEobj@Obs[, obsi], percsCAA[cc],
na.rm = TRUE)
ind <- MSEobj@Obs[, obsi] > refval
Obsv[mm, r, cc] <- (mean(Y1[ind]) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- mean(MSEobj@Obs[ind, obsi])
Obscost[mm, r, cc] <- Obsval[mm, r, cc]
} else if (oem[oemi] == 4) {
refval <- quantile(MSEobj@Obs[, obsi], percUL[cc],
na.rm = TRUE)
refval2 <- quantile(MSEobj@Obs[, obsi], percUU[cc],
na.rm = TRUE)
ind <- (MSEobj@Obs[, obsi] > refval) & (MSEobj@Obs[,
obsi] < refval2)
Obsv[mm, r, cc] <- (mean(Y1[ind]) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- sd(MSEobj@Obs[ind, obsi])
Obscost[mm, r, cc] <- 1/(Obsval[mm, r, cc]/sd(MSEobj@Obs[,
obsi]))^2
}
# observation model type
} # loop over bins
} # loop over r
} # observation variables?
} # loop over MPs
cb <- array(NA, c(MSEobj@nMPs, maxcomp))
for (mm in 1:MSEobj@nMPs) {
if (sum(!is.na(Obscost[mm, , ])) > 0) {
for (r in 1:length(Obsname[[mm]])) {
dat <- data.frame(x = Obscost[mm, r, ], y = Obsv[mm, r,
])
if (prod(apply(dat, 2, is.finite)) > 0) {
# plot(dat$x,dat$y)
cb[mm, r] <- lm(y ~ x - 1, data = dat)$coefficients[1]
}
}
}
}
ncols <- 100
# colsse<-makeTransparent(rainbow(ncols,start=0,end=0.36),95)[ncols:1]
colt <- rainbow(ncols, start = 0, end = 0.36)[1:ncols]
colsse <- makeTransparent(colt, 98)
cb[cb < 0 | is.na(cb)] <- 0
coly <- ceiling((cb/max(cb, na.rm = T))^0.5 * ncols)
coly[coly == 0] <- 1
ncol <- ceiling(MSEobj@nMPs^0.5)
nrow <- ceiling(MSEobj@nMPs/ncol)
op <- par(mfrow = c(nrow, ncol), mar = c(2.4, 2.4, 0.1, 0.1), omi = c(0.4,
0.35, 0.3, 0))
gcol1 <- "#99999960"
gcol2 <- "#99999940"
gcol3 <- "#99999920"
for (mm in 1:MSEobj@nMPs) {
if (sum(!is.na(Obscost[mm, , ])) > 0)
{
plot(c(1, 5), range(Obsv, na.rm = T), col = "white", main = "")
legend("topleft", legend = MSEobj@MPs[mm], bty = "n", text.font = 2,
cex = 1.4)
abline(h = (-20:50) * 4, col = gcol2, lwd = 1.5)
abline(h = (-20:50) * 4 + 2, col = gcol3, lwd = 1)
abline(v = 1:4, col = gcol2, lwd = 1.5)
abline(v = (1:4) + 0.5, col = gcol3, lwd = 1)
abline(h = 0, col = gcol1, lwd = 3)
no <- length(Obsname[[mm]])
ind <- order(cb[mm, 1:no], decreasing = T)[1:ncomp]
ind <- ind[!is.na(ind)]
ind2 <- order(Obsv[mm, 1:no, nbins])[1:ncomp]
ind2 <- ind2[!is.na(ind2)]
lpos <- Obsv[mm, ind2, nbins]
ppos <- seq(min(Obsv, na.rm = T), max(Obsv, na.rm = T),
length.out = length(ind2))
wt <- (max(Obsv[mm, 1:no, nbins], na.rm = T) - min(Obsv[mm,
1:no, nbins], na.rm = T))/(max(Obsv, na.rm = T) - min(Obsv,
na.rm = T))/(no/ncomp)
wt <- wt^0.66
nupos <- wt * lpos + (1 - wt) * ppos
for (r2 in 1:length(ind)) {
r <- ind2[r2]
lines(c(1, Obscost[mm, r, ]), c(0, Obsv[mm, r, ]), col = colsse[coly[mm,
r]], lwd = 3)
if (!lay) {
text(4.5, nupos[r2], Obsname[[mm]][r], col = colt[coly[mm,
r]], font = 2, cex = 1.2)
} else {
text(4.5, nupos[r2], Obsnam3[match(Obsname[[mm]][r],
Obsnam2)], col = colt[coly[mm, r]], font = 2, cex = 1.2)
}
} # observation quantities (lines)
# legend('topleft',legend=Obsname[[mm]][ind],text.col=colt[coly[mm,ind]],text.font=2,cex=1.2,bty='n')
} # if there is data to plot
} # MPs (plots)
mtext("Cost relative to today", 1, outer = T, cex = 0.9, line = 1,
font = 2)
# mtext(paste('Operating model parameters:
# ',objnam,'@OM',sep=''),3,outer=T,font=2,cex=0.9)
mtext(paste("% Change in ", Utnam, " relative to today", sep = ""),
2, outer = T, line = 0.6, font = 2, cex = 0.9)
par(op)
invisible(list(Obscost, Obsv, Obsval, cb, Obsname, MSEobj@MPs))
} # VOI2
#' Yet another Value of Information Plot
#'
#' A function that relates parameters of the observation model and the
#' operating model parameters to yield.
#'
#'
#' @param MSEobj An object of class MSE
#' @param MPs The MPs to plot. If NA it will plot the first nMP from MSEobj
#' @param nvars The number of observation or operating model parameters to plot
#' (number of columns)
#' @param nMP The maximum number of MPs to plot (number of rows)
#' @param Par Plot Operating Model (OM) or Observation (Obs) parameters?
#' @param YVar Variable for Y-Axis: Yield (Y) or Biomass (B) (relative to BMSY)
#' @param doPlot Output the plot?
#' @param incStat Include a print out of statistic describing the curviness of
#' the line?
#' @param availMP Optional character string of MPs that are available. These
#' names are colored black
#' @param acceptMP Optional character string of MPs that are acceptable. These
#' names are colored green if they are also in availMP
#' @param incNames Include the names?
#' @param labcex Character size of the label
#' @param quants Quantiles to calculate
#' @return A list of all the information included in the plot
#' @author A. Hordyk
#' @export VOIplot
VOIplot <- function(MSEobj, MPs = NA, nvars = 5, nMP = 4,
Par = c("Obs", "OM"), YVar = c("Y", "B"), doPlot = TRUE, incStat = FALSE,
availMP = NULL, acceptMP = NULL, incNames = TRUE, labcex = 0.8,
quants = c(0.05, 0.95)) {
YVar <- match.arg(YVar)
nvars <- max(nvars, 2) # maximum number of variables
Par <- match.arg(Par) # Operating Model or Observation
nMPs <- MSEobj@nMPs # Number of MPs
# Subset to specified MPs
if (any(is.na(MPs))) MPs <- MSEobj@MPs
if (methods::is(MPs, "numeric") | methods::is(MPs, "integer")) MPs <- MSEobj@MPs[MPs]
if (length(MPs) < 1) stop("No MPs found")
nMPss <- length(MPs)
if (nMP > nMPs) nMP <- nMPs
if (!all(MSEobj@MPs %in% MPs)) {
mse <- Sub(MSEobj, MPs = MPs)
nMPs <- mse@nMPs
} else {
mse <- MSEobj
}
# Calculate MSE sensitivities per MP
if (length(MPs) > 1) senseDat <- sapply(1:nMPs, calcMSESense, MSEobj = mse,
YVar = YVar, Par = Par,
simplify = FALSE, quants = quants)
if (length(MPs) == 1) senseDat <- calcMSESense(MP = MPs, MSEobj = mse,
YVar = YVar, Par = Par,
quants = quants)
# Operating Model or Observation Statistics
if (nMPs == 1) {
varNames <- senseDat$OMNames
} else varNames <- senseDat[[1]]$OMNames
used <- matrix(FALSE, nrow = length(varNames), ncol = nMPs)
if (Par == "OM") {
used <- matrix(TRUE, nrow = length(varNames), ncol = nMPs) # all OM parameters used
Obsnam <- varNames
LnName <- varNames
# LnName <- c("Reference yield", "Natural mortality", "Depletion", "Abundance",
# "BMSY/B0", "FMSY/M", "M gradient", "Inter-annual variability M",
# "Recruitment variability", "Inter-annual variability effort",
# "Final effort", "MSY", "Average change in catchability", "Inter-annual variabilility in catchability",
# "FMSY", "von Bert. Linf", "von Bert. K", "von Bert. t0", "Steepness",
# "Linf gradient", "K gradient", "Inter-annual variability in Linf",
# "Recruitment gradient", "Inter-annual variability in K", "Age at maturity",
# "Length at 5% selection", "Length at full selection", "Dome-shaped selectivity",
# "Length at first capture", "Auto-correlation recruitment",
# "Length 50% maturity", "Length 95% maturity", "B0", "N0", "SSB0", "BMSY_B0",
# "TACSD", "TACFrac", "TAESD", "TAEFrac", "SizeLimSD", "SizeLimFrac", "Blow", "BMSY",
# "SSBMSY", "Mexp", "Discard mortality", "LR5", "LFR", "DR", "Lm/SL")
# # cbind(Obsnam, LnName)
}
if (Par == "Obs") {
slots <- c("Cat", "Cat", "AvC", "AvC", "CAA", "CAA", "CAL", "CAL",
"Ind", "Ind", "Dep", "Dep", "Dt", "Dt", "Mort", "FMSY_M", "SSBMSY_SSB0",
"L50", "L95", "LFC", "LFS", "Abun", "Abun", "vbK", "vbt0",
"vbLinf", "Steep", "Iref", "Cref", "Bref", "ML", "ML")
Obsnam <- c("Cbias", "Csd", "Cbias", "Csd", "CAA_nsamp", "CAA_ESS",
"CAL_nsamp", "CAL_ESS", "Isd", "betas", "Dbias", "Derr", "Dbias",
"Derr", "Mbias", "FMSY_Mbias", "BMSY_B0Bias", "lenMbias", "lenMbias",
"LFCbias", "LFSbias", "Abias", "Aerr", "Kbias", "t0bias", "Linfbias",
"hbias", "Irefbias", "Crefbias", "Brefbias", "CAL_nsamp", "CAL_ESS")
LnName <- Obsnam
# LnName <- c("Catch bias", "Catch error", "Catch bias", "Catch error",
# "n CAA samples", "CAA effective sample size", "n CAL samples",
# "CAL effective sample size", "Index Abundance error", "Hyperstability/hyperdepletion",
# "Depletion bias", "Depletion error", "Depletion bias", "Depletion error",
# "M bias", "FMSY/M bias", "BMSY/B0 bias", "Length maturity bias",
# "Length maturity bias", "Length first capture bias", "Length full capture bias",
# "Current abundance bias", "Current abundance error", "vB K bias",
# "vB t0 bias", "vB Linf bias", "Steepness bias", "Reference index bias",
# "Reference catch bias", "Reference biomass bias", "Mean length",
# "Mean length")
# print(cbind(slots, Obsnam, LnName))
for (mm in 1:nMPs) {
ids <- Obsnam[slots %in% unlist(strsplit(Required(MPs[mm])[, 2], split = ", "))]
used[match(ids, varNames), mm] <- TRUE
}
}
colnames(used) <- MPs
rownames(used) <- varNames
# Find the highest Stat for each variable
if (nMPs > 1) {
stat <- lapply(senseDat, "[[", "OMStat")
Stat <- matrix(unlist(stat), ncol = nMPs) * used
if (max(Stat, na.rm = TRUE) > 100)
Stat <- Stat/100
rownames(Stat) <- varNames
statord <- apply(Stat, 2, order, decreasing = TRUE)
topStat <- statord[1:nvars, ] # highest nvars for each MP
} else {
stat <- senseDat$OMStat
Stat <- matrix(stat, ncol = nMPs)
rownames(Stat) <- varNames
colnames(Stat) <- MPs
statord <- order(Stat, decreasing = TRUE)
topStat <- statord[1:nvars]
}
if (doPlot) {
## Create Plotting Space ##
Ncol <- nvars
if (nMPs < 2)
Ncol <- min(sum(used[, MPs]), nvars)
if (nMPs > 1) {
temp <- apply(used[, MPs], 2, sum)
if (all(temp < nvars))
Ncol <- max(temp)
}
Nrow <- min(nMP, sum(apply(used, 2, sum) > 0))
if (sum(apply(used, 2, sum) > 0) == 0)
print(paste("No", Par, "used for these MPs"))
mat <- matrix(1:(Nrow * Ncol), nrow = Nrow, byrow = TRUE)
op <- par(mfrow = c(Nrow, Ncol), oma = c(3, 6, 2, 0), mar = c(3, 2, 2, 1))
if (Par == "OM") Title <- "Operating Model Parameters"
if (Par == "Obs") Title <- "Observation Parameters"
# Colors and Controls
ncols <- length(topStat) # nrow(Stat) * ncol(Stat)
Cols <- colorRampPalette(c("green", "red"))(ncols)
# rev(rainbow(ncols,start=0,end=0.36))
highest <- max(Stat, na.rm = TRUE)
pch <- 18
LWD <- 3
LCol <- "black"
count <- 1
mm <- 1
AxCex <- 1.15
doneMP <- 1
# Make MP colors for Available, Acceptable, and Not-Acceptable
availCol <- "green"
acceptCol <- "black"
nonAAcol <- "darkgray"
mpcol <- "black" # default
mpCols <- data.frame(MPs = MPs, col = rep(mpcol, nMPs), stringsAsFactors = FALSE)
if (is.null(acceptMP))
acceptMP <- MPs
if (!is.null(availMP) & (!is.null(acceptMP))) {
mpCols[, 2] <- nonAAcol
mpCols[MPs %in% acceptMP, 2] <- acceptCol
mpCols[MPs %in% acceptMP & MPs %in% availMP, 2] <- availCol
}
AllMPs <- 1:nMPs
AllMPs <- AllMPs[apply(used, 2, sum) > 0] # only include MPs which use the parameter
AllMPs <- AllMPs[1:Nrow] # first nMPs
# find max y
maxY <- 1
for (mm in AllMPs) {
for (vr in 1:Ncol) {
if (nMPs > 1) varind <- topStat[vr, mm] # Variable index
if (nMPs == 1) varind <- topStat[vr]
if (nMPs > 1) {
ys <- senseDat[[mm]]$OMPoints[[varind]][, 2]
} else {
ys <- senseDat$OMPoints[[varind]][, 2]
}
}
maxY <- max(maxY, max(ys))
}
YLim <- c(0, maxY)
for (mm in AllMPs) {
# Loop along MPs Loop along variables
for (vr in 1:Ncol) {
if (nMPs > 1) varind <- topStat[vr, mm] # Variable index
if (nMPs == 1) varind <- topStat[vr]
varSN <- varNames[varind]
varLN <- LnName[match(varSN, Obsnam)]
if (nMPs > 1) {
xs <- senseDat[[mm]]$OMPoints[[varind]][, 1]
ys <- senseDat[[mm]]$OMPoints[[varind]][, 2]
} else {
xs <- senseDat$OMPoints[[varind]][, 1]
ys <- senseDat$OMPoints[[varind]][, 2]
}
if (used[varSN, MPs[mm]]) {
# variable is used
Col <- makeTransparent(Cols[ceiling(Stat[varind, MPs[mm]]/highest * ncols)])
# ylim <- c(0, quantile(ys, 0.95, na.rm = TRUE))
plot(xs, ys, col = Col, pch = pch, bty = "n", axes = FALSE,
xlab = "", ylab = "", ylim = YLim)
if (vr == 1) {
MyCol <- mpCols[match(MPs[mm], mpCols[, 1]), 2]
axis(side = 2, las = 1, cex.axis = AxCex)
mtext(side = 2, MPs[mm], line = 2.75, cex = 1.2, col = MyCol)
}
if (vr != 1)
axis(side = 2, labels = FALSE)
axis(side = 1, cex.axis = AxCex)
if (incStat)
text(max(xs), 0.05 * max(ys), round(Stat[varind, MPs[mm]],
2), pos = 2)
# Smoother line
if (nMPs > 1) {
smX <- senseDat[[mm]]$OMSmooth[[varind]]$x
smY <- senseDat[[mm]]$OMSmooth[[varind]]$y
} else {
smX <- senseDat$OMSmooth[[varind]]$x
smY <- senseDat$OMSmooth[[varind]]$y
}
lines(smX, smY, lwd = LWD, col = LCol)
# Variable Name
if (!incNames)
mtext(side = 1, varSN, cex = 1, line = 2.5)
if (incNames)
mtext(side = 1, varLN, cex = labcex, line = 2.5)
} else {
plot(c(0, 1), axes = FALSE, type = "n", xlab = "", ylab = "")
}
}
}
mtext(side = 3, outer = TRUE, Title, cex = 1.5)
if (YVar == "Y")
mtext(side = 2, outer = TRUE, "Long-term yield relative to MSY (%)",
cex = 1.25, line = 3)
if (YVar == "B")
mtext(side = 2, outer = TRUE, "B/BMSY in last 5 years", cex = 1.25,
line = 3)
}
par(op)
# invisible(Out)
}
#' Biomass wormplot
#'
#' A worm plot for plotting the likelihood of meeting biomass targets in future
#' years.
#'
#' Returns a matrix of nMPs rows and proyears columns which is the fraction of
#' simulations for which biomass was above Bref.
#'
#' @param MSEobj Object of class MSE, output of the runMSE function
#' @param Bref The reference fraction of BMSY (to evaluate the probability of
#' exceeding this level)
#' @param LB The lower bound probability that separates red (bad) and yellow
#' (O.K.) colored segments
#' @param UB The upper bound probability that separates yellow (O.K.) and green
#' (good) colored segments
#' @author T. Carruthers
#' @export wormplot
wormplot <- function(MSEobj, Bref = 0.5, LB = 0.25, UB = 0.75) {
if (UB < LB)
stop("LB parameter must be lower than UB parameter")
if (LB < 0 | LB > 1)
stop("LB parameter must be in the range of 0 to 1")
if (UB < 0 | UB > 1)
stop("UB parameter must be in the range of 0 to 1")
ncol <- ceiling(MSEobj@nMPs^0.3)
nrow <- ceiling(MSEobj@nMPs/ncol)
op <- par(mfcol = c(nrow, ncol), mar = c(0.1, 0.1, 0.1, 0.1), omi = c(0.6,
0.25, 0.3, 0))
Bprob <- apply(MSEobj@SB_SBMSY > Bref, 2:3, sum)/MSEobj@nsim
ind <- order(apply(Bprob, 1, sum), decreasing = T)
BLB <- Bprob > LB
BUB <- Bprob > UB
col <- array("red", dim(Bprob))
col[BLB & !BUB] = "yellow"
col[BUB] = "green"
for (i in 1:(nrow * ncol)) {
if (i < (MSEobj@nMPs + 1)) {
MP <- ind[i]
plot(c(1, MSEobj@proyears + 2), c(-1, 1), col = "white", axes = F)
# abline(h=0)
for (ys in 1:MSEobj@proyears) {
x <- c(ys - 1, ys, ys, ys - 1)
y <- c(rep(Bprob[MP, ys], 2), rep(-Bprob[MP, ys], 2))
pol <- data.frame(x, y)
polygon(pol, col = col[MP, ys], border = NA)
}
legend("top", legend = MSEobj@MPs[MP], bty = "n")
if ((i/nrow) == round(i/nrow, 0))
axis(1, pretty(1:MSEobj@proyears), pretty(1:MSEobj@proyears))
} else {
plot.new()
}
if (i == (nrow * ncol)) {
legend("topright", fill = c("green", "red"), legend = c(paste(">",
round(UB * 100, 0), "% prob.", sep = ""), paste("<", round(LB *
100, 0), "% prob.", sep = "")), bty = "n")
}
}
mtext(paste("Probability of biomass above ", round(Bref * 100, 0),
"% BMSY for ", deparse(substitute(MSE)), sep = ""), 3, outer = T,
line = 0.5)
mtext("Projection year", 1, outer = T, line = 2.5)
mtext(paste("Fraction of simulations above ", round(Bref * 100, 0),
"% BMSY", sep = ""), 2, outer = T, line = 0.25)
par(op)
invisible(Bprob)
}
## old functions - may not be used anymore ####
# #' Calculate Statistics for MP Performance
# #'
# #' Function calculates probabilities and other statistics for a range of
# #' performance metrics
# #'
# #' @param MSEobj An object of class MSE
# #' @param PMRefs A list of reference points for the performance metrics (must
# #' be named)
# #' @param lastYrs The last number of years in the projection to calculate the
# #' statistics
# #' @param UseMean Logical. Calculate mean (TRUE) or median (FALSE)?
# #' @param msg Logical. Print out messages?
# #' @author A. Hordyk
# #' @export MPStats
# MPStats <- function(MSEobj, PMRefs = list(SB_SBMSY = 0.5, SSB_SSB0 = 0.2, F_FMSY = 1,
# AAVY = 30, AAVE = 30), lastYrs = 10, UseMean = TRUE, msg = TRUE) {
#
# if (msg)
# message("Calculating MP Performance for last ", lastYrs, " years")
# flush.console()
#
# sumFun <- ifelse(UseMean, mean, median)
#
# # Assign Variables
# Nyears <- MSEobj@nyears
# Pyears <- MSEobj@proyears
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
# nsim <- MSEobj@nsim
# RefYd <- MSEobj@OM$RefY
#
# trefs <- PMRefs
# if (length(names(trefs)) != 5) {
# PMnames <- c("SB_SBMSY", "SSB_SSB0", "F_FMSY", "AAVY", "AAVE")
# DF <- c(0.5, 0.2, 1, 30, 30)
# ind <- which(!PMnames %in% names(trefs))
# if (length(ind) > 0) {
# for (xx in ind) {
# trefs[[PMnames[xx]]] <- DF[xx]
# }
# }
# }
#
# # Error Checks
# if (!class(lastYrs) == "character" & lastYrs >= Pyears) {
# message("lastYrs set too high. Defaulting to last 10 years")
# lastYrs <- 10
# }
# if (lastYrs <= 0 | lastYrs == FALSE | lastYrs == "all" | lastYrs ==
# "All")
# lastYrs <- Pyears
#
# yrs <- (Pyears - lastYrs + 1):Pyears # Years to summarize performance
# yrs <- yrs[!yrs == 1]
# ystart <- 1:lastYrs # First 10 years
# y1 <- (yrs[1] - 1):(yrs[length(yrs)] - 1) # for calculating interannual variability
# y2 <- y1 + 1
#
# # Biomass/BMSY
# B_BMSYm <- apply(MSEobj@SB_SBMSY[, , yrs, drop = FALSE], 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# B_BMSYsd <- apply(MSEobj@SB_SBMSY[, , yrs, drop = FALSE], 2, sd, na.rm = TRUE) # sd in last yrs
# B_BMSYref <- MSEobj@SB_SBMSY[, , yrs, drop = FALSE] > trefs$B_BMSY # above reference?
# B_BMSYp <- round(apply(SB_SBMSYref, 2, sum, na.rm = TRUE)/(lastYrs *
# nsim), 2) # prob above ref
#
# # Biomass/B0
# temp <- as.matrix(expand.grid(1:nsim, 1:nMPs, 1:Pyears))
# Deplet <- array(NA, dim = dim(MSEobj@B_BMSY))
# Deplet[temp] <- (MSEobj@B_BMSY[temp] * MSEobj@OM$SSBMSY_SSB0[temp[, 1]])
#
# SSB_SSB0ref <- Deplet[, , yrs, drop = FALSE] > trefs$SSB_SSB0 # above reference?
# SSB_SSB0m <- apply(Deplet[, , yrs, drop = FALSE], 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# SSB_SSB0sd <- apply(Deplet[, , yrs, drop = FALSE], 2, sd, na.rm = TRUE) # sd in last yrs
# SSB_SSB0p <- round(apply(SSB_SSB0ref, 2, sum, na.rm = TRUE)/(lastYrs * nsim),
# 2) # prob above ref
#
# # F/FMSY
# F_FMSYm <- apply(MSEobj@F_FMSY[, , yrs, drop = FALSE], 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# F_FMSYsd <- apply(MSEobj@F_FMSY[, , yrs, drop = FALSE], 2, sd, na.rm = TRUE) # sd in last yrs
# F_FMSYref <- MSEobj@F_FMSY[, , yrs, drop = FALSE] < trefs$F_FMSY # below reference?
# F_FMSYp <- round(apply(F_FMSYref, 2, sum, na.rm = TRUE)/(lastYrs * nsim), 2) # prob below ref
#
# # AAVY - Interannual variability in yield
# maxVar <- ifelse(trefs$AAVY > 1, trefs$AAVY/100, trefs$AAVY)
# MSEobj@Catch[(!is.finite(MSEobj@Catch[, , , drop = FALSE]))] <- 0 # if catch is NAN or NA, make it 0
# aavy <- (((MSEobj@Catch[, , y1, drop = FALSE] - MSEobj@Catch[, , y2, drop = FALSE])/MSEobj@Catch[,
# , y2, drop = FALSE])^2)^0.5
#
# aavy[is.nan(aavy)] <- NA
# if (sum(aavy == Inf, na.rm=TRUE) > 0) aavy[aavy == Inf] <- NA
#
# # aavy[aavy > 10 * median(aavy, na.rm = TRUE)] <- NA # remove huge spikes from F=0
# AAVYm <- apply(aavy, 2, sumFun, na.rm = TRUE) # median/mean in last yrs
#
# AAVYsd <- apply(aavy, 2, sd, na.rm = TRUE) # sd in last yrs
# AAVYref <- aavy < maxVar
# AAVYp <- round(apply(AAVYref, 2, sum, na.rm = TRUE)/(lastYrs * nsim), 2)
#
# # AAVE - Interannual varialility in effort
# maxVar <- ifelse(trefs$AAVE > 1, trefs$AAVE/100, trefs$AAVE)
# eff <- MSEobj@Effort
# if (all(is.na(eff))) {
# message("Variability in effort unable to be calculated from this version of MSE object")
# aave <- NA
# AAVEm <- rep(NA, length(AAVYm))
# AAVEsd <- rep(NA, length(AAVYsd))
# AAVEref <- array(NA, dim = dim(AAVYref))
# AAVEp <- rep(NA, length(AAVYp))
# flush.console()
# } else {
# aave <- (((eff[, , y1 - 1, drop = FALSE] - eff[, , y2 - 1, drop = FALSE])/eff[,
# , y2 - 1, drop = FALSE])^2)^0.5
# aave[is.nan(aave)] <- NA
# aave[aave == Inf] <- NA
# # aave[aave > 10 * apply(aave, 2, median, na.rm = TRUE)] <- NA # remove huge spikes from F=0
# AAVEm <- apply(aave, 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# AAVEsd <- apply(aave, 2, sd, na.rm = TRUE) # sd in last yrs
# AAVEref <- aave < maxVar
# AAVEp <- round(apply(AAVEref, 2, sum, na.rm = TRUE)/(lastYrs *
# nsim), 2)
# }
#
# # Yield
# LTY <- round(apply(MSEobj@Catch[, , yrs, drop = FALSE]/RefYd, 2, sumFun,
# na.rm = TRUE) * 100, 1) # long-term yield
# STY <- round(apply(MSEobj@Catch[, , ystart, drop = FALSE]/RefYd, 2, sumFun,
# na.rm = TRUE) * 100, 1) # short-term yield
#
# MPtype <- sapply(1:nMPs, function(X) class(get(MPs[X])))
#
# DF <- data.frame(MP = MPs, B_BMSYm = B_BMSYm, B_BMSYsd = B_BMSYsd,
# B_BMSYp = B_BMSYp, SSB_SSB0m = SSB_SSB0m, SSB_SSB0sd = SSB_SSB0sd, SSB_SSB0p = SSB_SSB0p,
# F_FMSYm = F_FMSYm, F_FMSYsd = F_FMSYsd, F_FMSYp = F_FMSYp, AAVYm = AAVYm,
# AAVYsd = AAVYsd, AAVYp = AAVYp, AAVEm = AAVEm, AAVEsd = AAVEsd,
# AAVEp = AAVEp, LTY = LTY, STY = STY, lastYrs = lastYrs, B_BMSYRef = trefs$B_BMSY,
# SSB_SSB0Ref = trefs$SSB_SSB0, F_FMSYRef = trefs$F_FMSY, AAVYRef = trefs$AAVY,
# AAVERef = trefs$AAVE, MPtype = MPtype, stringsAsFactors = FALSE)
#
# Dist <- NULL # calculate distance from corner
# for (X in 1:nrow(DF)) Dist[X] <- euc.dist(c(DF[X, "B_BMSYp"] * 100,
# DF[X, "LTY"]), c(100, max(DF[, "LTY"], na.rm = TRUE)))
#
# DF$Dist <- Dist
#
# Probs <- list(F_FMSYref = F_FMSYref, SSB_SSB0ref = SSB_SSB0ref, B_BMSYref = B_BMSYref,
# AAVYref = AAVYref, AAVEref = AAVEref, Effort = eff)
#
# pastC <- apply(MSEobj@CB_hist[, , , , drop = FALSE], c(1, 3), sum,
# na.rm = TRUE)/RefYd
# temp <- replicate(nMPs, pastC)
# temp <- aperm(temp, c(1, 3, 2))
#
# lastYr <- temp[, , Nyears, drop = FALSE]
# lastYr2 <- replicate(Pyears, lastYr)
# Yield <- abind::abind(lastYr, MSEobj@Catch[, , , drop = FALSE]/RefYd, along = 3)
# # dim(Yield)
#
# # totC <- abind(temp, MSEobj@Catch[,,, drop=FALSE]/RefYd, along=3)
#
# bySim <- list(SSB_SSB0 = Deplet, B_BMSY = MSEobj@B_BMSY, F_FMSY = MSEobj@F_FMSY,
# AAVY = aavy, AAVE = aave, LTY = MSEobj@Catch[, , yrs, drop = FALSE]/RefYd,
# STY = MSEobj@Catch[, , ystart, drop = FALSE]/RefYd, Yield = Yield)
#
# Out <- list(Perf = DF, BySim = bySim, Probs = Probs)
# Out
# }
#
## Supporting functions - not exported ####
calcStat <- function(rr, evalbreaks) {
# supporting function
ind <- as.integer(evalbreaks/2)
ind2 <- as.integer(0.1 * evalbreaks)
ind3 <- as.integer(0.9 * evalbreaks)
if (all(is.na(rr))) return(0)
if (all(rr$x == 0)) return(0)
sum((rr$y - mean(rr$y, na.rm = TRUE))^2)
}
calcMSESense <- function(MP = 1, MSEobj, YVar = c("Y", "B"), Par = c("Obs","OM"),
evalbreaks = NULL, quants = c(0.05, 0.95)) {
# supporting function
YVar <- match.arg(YVar)
# Calculate for a single MP
if (length(MP) > 1) stop("Only one MP")
nMPs <- MSEobj@nMPs
MPs <- MSEobj@MPs
if (methods::is(MP,"character")) mm <- which(MPs %in% MP)
if (methods::is(MP, "numeric") | methods::is(MP,"integer")) {
mm <- MP
MP <- MPs[mm]
}
nsims <- MSEobj@nsim
RefYd <- MSEobj@OM$RefY
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
if (is.null(evalbreaks)) evalbreaks <- as.integer(nsims/4) # number of breaks for loess smoother
if (YVar == "Y") {
if (length(dim(MSEobj@Catch)) > 2) {
yout <- apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd * 100
} else {
yout <- apply(MSEobj@Catch[, yind], 1, mean, na.rm = T)/RefYd * 100
}
}
if (YVar == "B") {
if (length(dim(MSEobj@B_BMSY)) > 2) {
yout <- apply(MSEobj@B_BMSY[, mm, yind], 1, mean, na.rm = T)
} else {
yout <- apply(MSEobj@B_BMSY[, yind], 1, mean, na.rm = T)
}
}
# Operating Model names to include
MSEobj@OM$Lm_SL <- MSEobj@OM$L50/MSEobj@OM$LFS
varnames <- names(MSEobj@OM)
vars <- MSEobj@OM
vargood <- (apply(vars, 2, sd, na.rm = TRUE)/(apply(vars, 2, mean, na.rm = TRUE)^2)^0.5) > 0.005
# vargood[grep("qvar", varnames)] <- FALSE
vargood[is.na(vargood)] <- TRUE
varnames <- varnames[vargood]
omvals <- MSEobj@OM[, varnames]
# Ignore these parameters from VOI plot
omvals$RefY <- 0
omvals$A <- 0
omvals$Asp <- 0
omvals$OFLreal <- 0
omvals$FMSY <- 0
omvals$MSY <- 0
omvals$B0 <- 0
omvals$N0 <- 0
omvals$SSB0 <- 0
omvals$BMSY <- 0
omvals$SSBMSY <- 0
omvals$LFC <- 0
omvals$FMSY_M <- 0
omvals$BMSY_B0 <- 0
omvals$SSBMSY_SSB0 <- 0
omvals$MGT <- 0
omvals$Blow <- 0
omvals$FMSY_M <- 0
omvals$maxlen <- 0
omvals$SRrel <- 0
omvals$FinF <- 0
omvals[is.na(omvals)] <- 0
OMSmooth <- OMStat <- obvals <- OBSmooth <- OMPoints <- OMNames <- NULL
if (Par == "OM") {
# Apply loess smoother to Operating Model parameters
rr <- as.list(omvals)
OMNames <- names(rr)
OMSmooth <- list()
for (xx in 1:length(rr)) {
m <- matrix(c(rr[[xx]], yout), ncol = 2)
m <- m[order(m[, 1]), ]
qnts <- quantile(m[, 1], quants, na.rm = TRUE)
if (all(m[, 1] == 0)) {
OMSmooth[[xx]] <- NA
} else {
nas <- which(apply(!apply(m, 2, is.na), 1, prod) == 0)
if (length(nas) > 0) m <- m[-nas, ]
ind <- findInterval(qnts, m[, 1])
m <- m[ind[1]:ind[2], ]
rr[[xx]] <- m
OMSmooth[[xx]] <- suppressWarnings(loess.smooth(rr[[xx]][, 1], rr[[xx]][, 2]))
OMPoints[[xx]] <- m
}
}
# Calculate stat for OM curve
OMStat <- unlist(lapply(OMSmooth, calcStat, evalbreaks = evalbreaks))
}
if (Par == "Obs") {
# Observation Parameters
varnames <- names(MSEobj@Obs)
vars <- MSEobj@Obs
vargood <- (apply(vars, 2, sd, na.rm = TRUE)/(apply(vars, 2, mean, na.rm = TRUE)^2)^0.5) > 0.005
varnames <- varnames[vargood]
varnames <- varnames[!is.na(varnames)]
obvals <- MSEobj@Obs[, varnames]
rr <- as.list(obvals)
OMNames <- names(rr)
OMSmooth <- list()
for (xx in 1:length(rr)) {
m <- matrix(c(rr[[xx]], yout), ncol = 2)
m <- m[order(m[, 1]), ]
qnts <- quantile(m[, 1], quants, na.rm = TRUE)
if (all(m[, 1] == 0)) {
OMSmooth[[xx]] <- NULL
} else {
nas <- which(apply(!apply(m, 2, is.na), 1, prod) == 0)
if (length(nas) > 0)
m <- m[-nas, ]
ind <- findInterval(qnts, m[, 1])
m <- m[ind[1]:ind[2], ]
rr[[xx]] <- m
OMSmooth[[xx]] <- suppressWarnings(loess.smooth(rr[[xx]][, 1], rr[[xx]][, 2]))
OMPoints[[xx]] <- m
}
}
# Calculate stat for OM curve
OMStat <- unlist(lapply(OMSmooth, calcStat, evalbreaks = evalbreaks))
}
Out <- list(OMPoints = OMPoints, OMSmooth = OMSmooth, OMStat = OMStat, OMNames = OMNames)
Out
}
custombar<-function(dat,MPnams,tickwd1=0.1,tickwd2=0.05,lwd1=2,lwd2=2,xlab=T,bcol="azure2",barcol='dark grey'){
nMPer<-nrow(dat)
incr<-(max(dat)-min(dat))*0.05
plot(dat[,5],ylim=c(min(dat)-incr,max(dat)+incr),xlim=c(0.25,nMPer+0.25),col='white',axes=F,ylab="",xlab="")
if(xlab){
axis(1,-1:(nMPer+1),c("","",MPnams,""),las=2,font=2,cex.axis=1.2)
}else{
axis(1,-1:(nMPer+1),rep("",nMPer+3))
}
incr<-(max(dat)-min(dat))*0.2
yp<-pretty(seq(min(dat)-incr,max(dat)+incr,length.out=12))
axis(2,yp,yp,las=2)
big<-1E20
polygon(c(-big,big,big,-big),c(-big,-big,big,big),col=bcol)# grey92
abline(h=yp,col='white')
for(i in 1:nMPer){
lines(c(i,i),c(dat[i,1],dat[i,5]),lwd=lwd2) # 80%
lines(c(i,i),c(dat[i,2],dat[i,4]),lwd=lwd1) # 80%
polygon(c(i-tickwd1/2,i+tickwd1/2,i+tickwd1/2,i-tickwd1/2),c(dat[i,2],dat[i,2],dat[i,4],dat[i,4]),lwd=lwd1,col=barcol) # lower interquartile
#lines(c(i-tickwd1/2,i+tickwd1/2),c(dat[i,4],dat[i,4]),lwd=lwd1) # upper interquartile
lines(c(i-tickwd2/2,i+tickwd2/2),c(dat[i,1],dat[i,1]),lwd=lwd1) # lower 80%
lines(c(i-tickwd2/2,i+tickwd2/2),c(dat[i,5],dat[i,5]),lwd=lwd2) # upper 80%
}
points(dat[,3],pch=19,cex=1.1)
}
# A simulation by simulation approach to plotting results
comp <- function(MSEobj, MPs = NA) {
if (is.na(MPs))
MPs <- MSEobj@MPs
notm <- MPs[!(MPs %in% MSEobj@MPs)]
canm <- MPs[MPs %in% MSEobj@MPs]
if (length(notm) > 0)
print(paste("Methods", paste(notm, collapse = ", "), "were not carried out in MSE",
deparse(substitute(MSEobj)), sep = " "))
if (length(canm) == 0)
stop(paste("None of the methods you specified were carried out in the MSE",
deparse(substitute(MSEobj)), sep = ""))
if (length(canm) > 4) {
print(paste("A maximum of four methods can be compared at once. Plotting first four:",
paste(canm[1:4], collapse = ", "), sep = " "))
canm <- canm[1:4]
}
mind <- match(canm, MSEobj@MPs)
nm <- length(mind)
nsim <- MSEobj@nsim
proyears <- MSEobj@proyears
Yd <- array(NA, c(nm, nsim))
P10 <- array(NA, c(nm, nsim))
P50 <- array(NA, c(nm, nsim))
P100 <- array(NA, c(nm, nsim))
POF <- array(NA, c(nm, nsim))
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (m in 1:nm) {
mm <- mind[m]
Yd[m, ] <- round(apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd *
100, 1)
POF[m, ] <- round(apply(MSEobj@F_FMSY[, mm, ] > 1, 1, sum, na.rm = T)/proyears *
100, 1)
P10[m, ] <- round(apply(MSEobj@B_BMSY[, mm, ] < 0.1, 1, sum, na.rm = T)/proyears *
100, 1)
P50[m, ] <- round(apply(MSEobj@B_BMSY[, mm, ] < 0.5, 1, sum, na.rm = T)/proyears *
100, 1)
P100[m, ] <- round(apply(MSEobj@B_BMSY[, mm, ] < 1, 1, sum, na.rm = T)/proyears *
100, 1)
}
MSEcols <- c("red", "green", "blue", "orange")
# dev.new2(width=7,height=7)
op <- par(mfrow = c(2, 2), mai = c(0.85, 0.7, 0.1, 0.1), omi = rep(0.01, 4))
tradeoffplot2(POF, Yd, "Prob. of overfishing (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = NA)
tradeoffplot2(P100, Yd, "Prob. biomass < BMSY (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = canm)
tradeoffplot2(P50, Yd, "Prob. biomass < 0.5BMSY (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = NA)
tradeoffplot2(P10, Yd, "Prob. biomass < 0.1BMSY (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = NA)
par(op)
}
# Supporting Functions for Plots
tradeoffplot <- function(x, y, xlab, ylab, labs, cex, vl, hl) {
adjj <- c(0.7, 1.3)
XLim <- c(min(c(-10, min(x, na.rm = T) * adjj)), max(c(max(x, na.rm = T) *
adjj, 110)))
YLim <- c(min(c(-10, min(y, na.rm = T) * adjj)), max(c(max(y, na.rm = T) *
adjj, 110)))
coly <- rep(c("#0000ff95", "#ff000095", "#20ff1095"), 50)[1:length(labs)]
coly[labs %in% c("AvC", "curE", "FMSYref")] <- "black"
# plot(NA,xlim=range(x,na.rm=T)*adjj,ylim=range(y,na.rm=T)*adjj,xlab=xlab,ylab=ylab)
plot(NA, xlim = XLim, ylim = YLim, xlab = xlab, ylab = ylab)
abline(v = vl, col = "#99999940", lwd = 2)
abline(h = hl, col = "#99999940", lwd = 2)
text(x, y, labs, font = 2, col = coly, cex = 0.9)
}
tradeoffplot2 <- function(x, y, xlab, ylab, cex = 1, vl, hl, coly, leg) {
adjj <- c(0.7, 1.3)
plot(NA, xlim = range(x, na.rm = T) * adjj, ylim = range(y, na.rm = T) *
adjj, xlab = xlab, ylab = ylab)
abline(v = vl, col = "grey", lwd = 2)
abline(h = hl, col = "grey", lwd = 2)
for (m in 1:nrow(x)) points(x[m, ], y[m, ], col = makeTransparent(coly[m],
50), pch = 19, cex = cex)
if (!is.na(leg[1]))
legend("topright", legend = leg, text.col = coly, bty = "n")
}
tradeoffplot3 <- function(x, y, xlab, ylab, labs, cex, vl, hl, xlim, ylim) {
coly <- rep(c("#0000ff95", "#ff000095", "#20ff1095"), 10)
plot(NA, xlim = xlim, ylim = ylim, xlab = xlab, ylab = ylab)
abline(v = vl, col = "#99999940", lwd = 2)
abline(h = hl, col = "#99999940", lwd = 2)
text(x, y, labs, font = 2, col = coly, cex = 1)
}
tradeoffplot4 <- function(x, y, xlab, ylab, labs, cex, vl, hl, ShowLabs = FALSE,
ShowCols = FALSE, AvailMPs = NULL) {
adjj <- c(0.9, 1.1)
XLim <- c(min(c(-10, min(x, na.rm = T) * adjj)), max(c(max(x, na.rm = T) *
adjj, 110)))
YLim <- c(min(c(-10, min(y, na.rm = T) * adjj)), max(c(max(y, na.rm = T) *
adjj, 110)))
# Which MPs meet minimum PMs
ind <- which(x >= vl & y >= hl)
coly <- rep("darkgray", length(labs))
coly[ind] <- "black"
# coly[labs%in%c('AvC','curE','FMSYref')]<-'black'
Pch <- rep(21, length(labs))
# Pch[labs%in%c('AvC','curE','FMSYref')] <- 17
coly[grep("FMSY", labs)] <- "black"
Pch[grep("FMSY", labs)] <- 24
if (!is.null(AvailMPs))
Pch[labs %in% AvailMPs] <- 21
if (!is.null(AvailMPs))
coly[labs %in% AvailMPs & (x >= vl & y >= hl)] <- "green"
# coly<-rep(c('#0000ff95','#ff000095','#20ff1095'),50)[1:length(labs)]
plot(NA, xlim = XLim, ylim = YLim, xlab = xlab, ylab = ylab, bty = "l",
las = 1)
abline(v = vl, col = "#99999940", lwd = 2)
abline(h = hl, col = "#99999940", lwd = 2)
Alpha <- 30
# polygons
LeftCol <- rgb(red = 255, green = 0, blue = 0, alpha = Alpha, names = NULL,
maxColorValue = 255)
RightCol <- rgb(red = 0, green = 255, blue = 0, alpha = Alpha, names = NULL,
maxColorValue = 255)
if (ShowCols) {
polygon(x = c(0, vl, vl, 0), y = c(0, 0, hl, hl), col = LeftCol,
border = NA)
polygon(x = c(0, vl, vl, 0), y = c(0, 0, 100, 100), col = LeftCol,
border = NA)
polygon(x = c(vl, 100, 100, vl), y = c(0, 0, 100, 100), col = RightCol,
border = NA)
polygon(x = c(vl, 100, 100, vl), y = c(hl, hl, 100, 100), col = RightCol,
border = NA)
}
Cex <- 1.5
if (!ShowLabs)
points(x, y, bg = coly, pch = Pch, cex = Cex, col = "black")
if (ShowLabs)
text(x, y, labs, font = 2, col = coly, cex = 1)
# if(IdPoints) { message('Click points on plot to display MP name')
# message('Click Stop to finish') flush.console() identify(x,y,
# labels=labs) }
labs[ind]
}
#' Plot Spawning stock biomass and reference points for both historical and projected period
#'
#'
#' @param MSE An object of class 'MSE' produced by from runMSE()
#' @param simno Positive integer, the simulation number you wish to plot
#' @param ystart Positive integer, the calendar year corresponding with the first historical year
#' @param log Boolean, whether log SSB and reference points should be plotted
#' @param leg Boolean, should a legend be included in the plot?
#' @author T. Carruthers
#' @export SSBrefplot
SSBrefplot<-function(MSE,simno=1,ystart=1,log=F,leg=T){
ylaby<-ystart-1+(1:(MSE@nyears+MSE@proyears))
SSB <- c(MSE@SSB_hist[simno,],MSE@SSB[simno,1,])
# SSB <- c(apply(MSE@SSB_hist[simno,,,],2,sum),MSE@SSB[simno,1,])
dSSB0 <- MSE@RefPoint$Dynamic_Unfished$SSB0[simno,]
hSSB0 <- SSB[1]
aSSB0 <- MSE@RefPoint$ByYear$SSB0[simno,]
aSSBMSY <- MSE@RefPoint$ByYear$SSBMSY[simno,]
aBrat<-aSSBMSY/aSSB0
dSSBMSY <- aBrat*dSSB0
ylab="Spawning stock biomass"
ymin=0
if(log){
SSB<-log(SSB); dSSB0<-log(dSSB0); hSSB0<-log(hSSB0); aSSB0=log(aSSB0); dSSBMSY=log(dSSBMSY); aSSBMSY=log(aSSBMSY)
ylab = "log Spawning Stock Biomass"; ymin=min(SSB,dSSB0,hSSB0,aSSB0)
}
plot(ylaby,SSB,ylim=c(ymin,max(SSB,dSSB0,hSSB0,aSSB0)),yaxs='i',type='l',xlab="Year",ylab=ylab,col="white")
abline(h=hSSB0,lty=2,col="grey")
abline(v=ystart+MSE@nyears-0.5,col='grey')
lines(ylaby,dSSB0,col='#ff000090',lty=2,lwd=2)
lines(ylaby,dSSBMSY,col='#ff000090',lty=2)
lines(ylaby,aSSB0,col='#0000ff90',lty=2,lwd=2)
lines(ylaby,aSSBMSY,col='#0000ff90',lty=2)
lines(ylaby,SSB,lwd=2,col='#99999999')
if(leg)legend('topright',legend=c("Simulated","Asymp. SSB0","Asymp. SSBMSY","Dyn. SSB0","Dyn. SSBMSY","Historical"),
text.col=c('dark grey','blue','blue','red','red','dark grey'),
col=c('dark grey','blue','blue','red','red','dark grey'), lty=c(1,2,2,2,2,2),lwd=c(2,2,1,2,1,1),bty='n')
}
#' Make colors transparent
#'
#'
#' @param someColor Character string describing color
#' @param alpha transparency
#' @author T. Carruthers
#' @export makeTransparent
makeTransparent <- function(someColor, alpha = 100) {
newColor <- col2rgb(someColor)
apply(newColor, 2, function(curcoldata) {
rgb(red = curcoldata[1], green = curcoldata[2], blue = curcoldata[3],
alpha = alpha, maxColorValue = 255)
})
}
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2)^2))
GetStat <- function(PM, LTY, STY, PNOF, BMSYref, B0ref, VY) {
switch(PM, `Long-term Yield` = LTY, `Short-term Yield` = STY, Overfishing = PNOF,
`Biomass:BMSY` = BMSYref, `Biomass:B0` = B0ref, AnnualVar = VY)
}
StatLab <- function(PM, maxVar, BmsyRef, B0Ref) {
switch(PM, `Long-term Yield` = "Long-term Yield", `Short-term Yield` = "Short-term Yield",
Overfishing = "Prob. of Not Overfishing (%)", `Biomass:BMSY` = paste0("Prob. Biomass >",
BmsyRef, "BMSY (%)"), `Biomass:B0` = paste0("Prob. Biomass >",
B0Ref, "B0 (%)"), AnnualVar = paste0("Prob. AAVY <", maxVar,
"%"))
}
Blue-Matter/MSEtool documentation built on April 25, 2024, 12:30 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions Pplot2 Pplot NOAA_plot Kplot join_plots plot.MSE
Documented in join_plots Kplot NOAA_plot plot.MSE Pplot Pplot2
# ---- Plot MSE object ----
#' Plot MSE object
#' @param x object of class MSE
#' @param ... other parameters passed to plot (currently ignored)
#' @method plot MSE
#' @export
plot.MSE <- function(x, ...) {
Pplot(x, nam = deparse(substitute(x)))
Kplot(x)
Tplot(x, nam = deparse(substitute(x)))
}
#' Plot several plots with a shared legend
#'
#' @param plots list of plot objects of class `gg` or `ggplot`
#' @param ncol Optional number of columns
#' @param nrow Optional number of rows
#' @param position position of the legend ("bottom" or "right")
#' @param legend Logical. Use a legend?
#' @export
#'
#' @note modified from https://github.com/tidyverse/ggplot2/wiki/share-a-legend-between-two-ggplot2-graphs
join_plots <- function(plots, ncol = length(plots), nrow = 1, position = c("right", "bottom"),
legend=TRUE) {
if (!requireNamespace("gridExtra", quietly = TRUE)) {
stop("Package \"gridExtra\" needed for this function to work. Please install it.",
call. = FALSE)
}
position <- match.arg(position)
if (legend) {
g <- ggplot2::ggplotGrob(plots[[1]] + ggplot2::theme(legend.position = position))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
lwidth <- sum(legend$width)
gl <- lapply(plots, function(x) x + ggplot2::theme(legend.position="none"))
gl <- c(gl, ncol = ncol, nrow = nrow)
combined <- switch(position,
"bottom" = gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl),
legend,
ncol = 1,
heights = grid::unit.c(grid::unit(1, "npc") - lheight, lheight)),
"right" = gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl),
legend,
ncol = 2,
widths = grid::unit.c(grid::unit(1, "npc") - lwidth, lwidth)))
} else {
gl <- lapply(plots, function(x) x + ggplot2::theme(legend.position="none"))
gl <- c(gl, ncol = ncol, nrow = nrow)
combined <- gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl))
}
grid::grid.newpage()
grid::grid.draw(combined)
# return gtable invisibly
invisible(combined)
}
# #' Plot a barplot of MSE results
# #'
# #' @param height An object of class MSE. Generic function must have argument
# #' height. But note that this must be an MSE object.
# #' @param MSEobj Optional. An object of class MSE. Overides \code{height}
# #' @param PMs List of performance metrics. Options are \code{c('SSB_SSB0',
# #' 'SB_SBMSY', 'F_FMSY', 'AAVE', 'AAVY')}
# #' @param PLim Probability threshold
# #' @param lastYrs Last number of years in projection to calculate statistics
# #' @param maxMP Maximum number of MPs to include in each plot
# #' @param MPs Optional subset MSE object by MP
# #' @param Title Optional title for plot
# #' @param sims Optional subset MSE object by simulation
# #' @param msg Logical. Print out messages?
# #' @param incRef Logical. Include the reference methods?
# #' @param cex.names Size of names
# #' @param ... Optional additional arguments passed to \code{barplot}
# #' @author A. Hordyk
# #' @export
# barplot.MSE <- function(height, MSEobj = NULL, PMs = list(SB_SBMSY = 0.5,
# SSB_SSB0 = 0.2), PLim = 0.8, lastYrs = 10, maxMP = 14, MPs = NA, Title = NULL,
# sims = NULL, msg = TRUE, cex.names = 1.3, incRef = FALSE, ...) {
#
# MSEobj <- match.arg(MSEobj)
# if (is.null(MSEobj))
# MSEobj <- height
# if (!is.null(sims) & all(is.na(MPs)))
# MSEobj <- Sub(MSEobj, sims = sims)
# if (!is.null(sims) & all(!is.na(MPs)))
# MSEobj <- Sub(MSEobj, sims = sims, MPs = MPs)
# if (is.null(sims) & !all(is.na(MPs)))
# MSEobj <- Sub(MSEobj, MPs = MPs)
#
# if (!incRef) {
# mps <- MSEobj@MPs[!grepl("ref", MSEobj@MPs)]
# MSEobj <- Sub(MSEobj, MPs = mps)
# }
# DF <- list()
# if (length(lastYrs) > 1) {
# for (xx in seq_along(lastYrs)) DF[[xx]] <- MPStats(MSEobj, PMRefs = PMs,
# lastYrs = lastYrs[xx], UseMean = TRUE, msg = msg)$Perf
# } else {
# DF[[1]] <- MPStats(MSEobj, PMRefs = PMs, lastYrs = lastYrs, UseMean = TRUE,
# msg = msg)$Perf
# }
# lastYrs[lastYrs >= MSEobj@proyears] <- 10
# PosPMs <- c("SSB_SSB0", "SB_SBMSY", "F_FMSY", "AAVE", "AAVY")
# PMNames <- names(PMs)
# ind <- match(PMNames, PosPMs)
#
# pms <- paste0(names(PMs), "p")
#
# vars <- which(!is.na(match(names(DF[[1]]), pms)))
#
# temp <- lapply(DF, "[", vars)
# DF2 <- cbind(DF[[1]][, 1], do.call(cbind.data.frame, temp))
# names(DF2)[1] <- "MP"
#
# B0Ref <- unique(DF[[1]]$SSB_SSB0Ref)
# B_BMSYRef <- unique(DF[[1]]$SB_SBMSYRef)
# F_FMSYRef <- unique(DF[[1]]$F_FMSYRef)
# AAVERef <- unique(DF[[1]]$AAVERef)
# AAVYRef <- unique(DF[[1]]$AAVYRef)
#
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears)
#
# B0Leg <- bquote(italic(B) > ~.(B0Ref) ~ italic(B[0]))
# BMSYLeg <- bquote(italic(B) > ~.(B_BMSYRef) ~ italic(B[MSY]))
# FMSYLeg <- bquote(italic(F) < ~.(F_FMSYRef) ~ italic(F[MSY]))
# AAVELeg <- bquote(italic(AAVE) > ~.(AAVERef) ~ "%")
# AAVYRef <- bquote(italic(AAVY) > ~.(AAVYRef) ~ "%")
# LegList <- list(B0Leg, BMSYLeg, FMSYLeg, AAVELeg, AAVYRef)
# Legend <- NULL
# Legend <- append(Legend, as.expression(LegList[ind]))
#
# if (length(Years) > 1) {
# temp <- NULL
# count <- 1
# for (xx in seq_along(Years)) {
# for (yy in seq_along(LegList[ind])) {
# temp[[count]] <- bquote(.(LegList[ind][[yy]]) ~ (Years ~
# .((MSEobj@proyears - lastYrs[xx]) + 1) ~ "-" ~ .(MSEobj@proyears)))
# count <- count + 1
# }
# }
# Legend <- append(NULL, as.expression(temp))
# }
#
# MPs <- as.character(DF2$MP)
# nMPs <- length(MPs)
# if (is.null(MPs))
# stop("Dataframe must a column named `MP`")
# ndat <- ncol(DF2) - 1
# # if (length(ProbLims) != ndat) stop('Must be a probablility limit for
# # each variable')
# Probs <- t(DF2[, 2:(ndat + 1)])
# colnames(Probs) <- MPs
# if (max(Probs) <= 1)
# Probs <- Probs * 100
# ProbLims <- VLine <- PLim
# if (max(ProbLims) <= 1)
# ProbLims <- ProbLims * 100
# if (nrow(Probs) > 1) {
# Ord <- order(apply(Probs, 2, mean))
# Probs <- Probs[, Ord]
# MPnames <- colnames(Probs)
# Pass <- as.logical(apply(Probs >= ProbLims, 2, prod))
# } else {
# MPnames <- colnames(Probs)
# Ord <- order(Probs)
# Probs <- t(as.matrix(Probs[Ord, drop = FALSE]))
# MPnames <- MPnames[Ord]
# colnames(Probs) <- MPnames
# Pass <- as.logical(Probs >= ProbLims)
# }
#
# # if (length(ProbLims) == 1) Pass <- Probs > ProbLims
#
# # bcols <- brewer.pal(8, 'Dark2')[1:length(vars)]
# cols <- c("#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02",
# "#A6761D", "#666666")
# bcols <- cols[1:ndat]
#
# nplots <- ceiling(nMPs/maxMP)
# Ncol <- ceiling(sqrt(nplots))
# Nrow <- ceiling(nplots/Ncol)
# if (!(Ncol * Nrow >= nplots))
# stop("Error in number of plots (you've found a bug in the function!)")
#
# tempmat <- matrix(1:(Ncol * Nrow), nrow = Nrow, byrow = TRUE)
# bspace <- max(nchar(MPs)) - 1
#
# op <- par(mfrow = c(Nrow, Ncol), oma = c(2.5, 1, 5, 2), mar = c(3, bspace,
# 0, 0))
# if (nplots == 1) {
# barplot(Probs, beside = TRUE, horiz = TRUE, names = MPnames, las = 1,
# xlim = c(0, 100), cex.axis = 1.5, cex.lab = 2, col = bcols,
# xlab = "Probability", xpd = NA, cex.names = cex.names, ...)
# if (!is.null(VLine)) {
# if (VLine < 1)
# VLine <- VLine * 100
# if (VLine > 0)
# abline(v = VLine, lty = 2, lwd = 2, col = "gray")
# }
# rng <- par("usr")
# lg <- legend(rng[1], rng[2], legend = Legend, bty = "n", cex = 1.25,
# horiz = TRUE, xpd = NA, title = "", plot = FALSE)
# legend(rng[1], rng[4] + lg$rect$h, legend = Legend, bty = "n",
# cex = 1.25, horiz = TRUE, xpd = NA, fill = bcols, title = "")
# }
# # Split the MPs over multiple plots
# if (length(vars) == 1)
# colnames(Probs) <- MPnames
# if (nplots > 1) {
# npplot <- min(ceiling(nMPs/nplots), maxMP) # number per plot
# xx <- 1
# xx2 <- npplot
# for (X in 1:nplots) {
# # if (length(Legend) > 1) splitdat <- Probs[,xx:xx2] if (length(Legend)
# # == 1) splitdat <- Probs[,xx:xx2]
# splitdat <- Probs[, xx:xx2]
# if (X == 1) {
# tt <- barplot(splitdat, beside = TRUE, ylab = "", xlim = c(0,
# 100), col = bcols, names.arg = NULL, cex.axis = 1.5,
# cex.lab = 2, las = 1, xpd = NA, horiz = TRUE, cex.names = cex.names,
# ...)
# if (!is.null(VLine)) {
# if (VLine < 1)
# VLine <- VLine * 100
# if (VLine > 0)
# abline(v = VLine, lty = 2, lwd = 2, col = "gray")
# }
# rng <- par("usr")
# lg <- legend(rng[1], rng[2], legend = Legend, bty = "n",
# cex = 1.25, horiz = TRUE, xpd = NA, fill = bcols, title = "",
# plot = FALSE)
# legend(rng[1], rng[4] + lg$rect$h, legend = Legend, bty = "n",
# cex = 1.5, horiz = TRUE, xpd = NA, fill = bcols, title = "",
# ...)
# } else {
# barplot(splitdat, beside = TRUE, ylab = "", xlim = c(0,
# 100), names.arg = NULL, cex.axis = 1.5, cex.lab = 2,
# las = 1, axes = TRUE, col = bcols, horiz = TRUE, cex.names = cex.names,
# ...)
# # abline(v=PLim, lty=2, col='gray', xpd=FALSE)
# }
# xx <- xx2 + 1
# xx2 <- min(xx + npplot - 1, nMPs)
# mtext(side = 1, outer = TRUE, "Probability", line = 1, cex = 1.4)
# if (!is.null(VLine)) {
# if (VLine < 1)
# VLine <- VLine * 100
# if (VLine > 0)
# abline(v = VLine, lty = 2, lwd = 2, col = "gray")
# }
# }
# }
# if (!is.null(Title))
# mtext(side = 3, outer = TRUE, title, line = 3.5, cex = 1.25)
#
# if (length(Years) == 1)
# mtext(side = 3, outer = TRUE, Years, line = 1.5)
# Pout <- t(Probs)
# if (is.null(rownames(Pout))) {
# MP <- colnames(Pout)
# colnames(Pout) <- NULL
# } else {
# MP <- rownames(Pout)
# rownames(Pout) <- NULL
# }
# MPclass <- MPtype(MPs)[,2]
# par(op)
# invisible(data.frame(MP = MP, Pout, Pass = Pass, MPClass = MPclass,
# stringsAsFactors = FALSE))
# }
#
# #' Boxplot of MP performance from MSE object
# #'
# #' @param x An object of class MSE
# #' @param MPs Optional subset MSE object by MP
# #' @param maxMP Maximum number of MPs to plot
# #' @param PMRefs List containing the Performance Metrics reference points.
# #' Options are \code{'SSB_SSB0', 'B_BMSY', 'F_FMSY', 'AAVE', 'AAVY'}
# #' @param lastYrs Last number of years in projection to calculate statistics
# #' @param cex.lab Size of axis label text
# #' @param cex.PM Size of performance metric text
# #' @param canMPs Optional character vector of MPs that can be applied (plotted
# #' in different colour)
# #' @param cols Optional vector of colours
# #' @param outline Logical. Include outliers in boxplot?
# #' @param CexName Size of the names
# #' @param incLine Logical. Include vertical line?
# #' @param incref Logical. Include reference methods?
# #' @param Names Logical. Include MP names in plot?
# #' @param ... Additional arguments to be passed to plotting functions
# #' @author A. Hordyk
# #' @export
# boxplot.MSE <- function(x, MPs = NA, maxMP = 8,
# PMRefs = list(B_BMSY = 1,
# SSB_SSB0 = 0.2, F_FMSY = 1, AAVY = 30, AAVE = 30),
# lastYrs = 10, cex.lab = 1.2,
# cex.PM = 0.75, canMPs = NULL, cols = TRUE, outline = FALSE, CexName = 1.25,
# incLine = TRUE, incref = FALSE, Names = TRUE, ...) {
#
# MSEobj <- x
# if (!all(is.na(MPs))) {
# if (!incref) {
# ind <- grep("FMSYref", MPs) # FMSYref methods
# ind <- c(ind, grep("NFref", MPs)) # No fishing reference
# if (length(ind) > 0)
# MPs <- MPs[-ind]
# }
# MSEobj <- Sub(MSEobj, MPs = MPs)
# }
# nmp <- MSEobj@nMPs
# Nyears <- MSEobj@nyears
# Pyears <- MSEobj@proyears
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
# nsim <- MSEobj@nsim
# RefYd <- MSEobj@OM$RefY
# nyrs <- MSEobj@proyears
#
# perf <- MPStats(MSEobj, lastYrs = lastYrs)
# if (lastYrs >= MSEobj@proyears)
# lastYrs <- 10
# yrs <- (nyrs - lastYrs + 1):nyrs
# perfdat <- perf[[1]]
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears,
# "(last", lastYrs, "years)")
#
# # MPtype <- perfdat$MPtype
# MPtypes <- MPtype(MPs)[,2]
# outmps <- perfdat[MPtypes == "Output", ]$MP
# inmps <- perfdat[MPtypes == "Input", ]$MP
# nOut <- ceiling(nmp/2)
# nIn <- floor(nmp - nOut)
# if (nMPs > nmp) {
# outDist <- perfdat[MPtypes == "Output", ]$Dist
# OutMPs <- perfdat[MPtypes == "Output", ]$MP[order(outDist)[1:nOut]]
# inDist <- perfdat[MPtypes == "Input", ]$Dist
# InMPs <- perfdat[MPtypes == "Input", ]$MP[order(inDist)[1:nIn]]
# } else {
# OutMPs <- perfdat[MPtypes == "Output", ]$MP
# InMPs <- perfdat[MPtypes == "Input", ]$MP
# }
#
# mseobj <- Sub(MSEobj, MPs = c(OutMPs, InMPs))
# nMPs <- mseobj@nMPs
# perf <- MPStats(mseobj, msg = FALSE)
# perfdat <- perf$Perf
# MPtypes <- MPtype(mseobj@MPs)[,2]
# # MPtype <- perfdat$MPtype
# rawVals <- perf$BySim
# MPs <- perfdat$MP
#
# # Reporting Distribution in last years
# BBMSY <- rawVals$B_BMSY[, , yrs]
# BBMSY <- aperm(BBMSY, c(1, 3, 2))
# dim(BBMSY) <- c(nsim * lastYrs, nMPs)
#
# BB0 <- rawVals$SSB_SSB0[, , yrs]
# BB0 <- aperm(BB0, c(1, 3, 2))
# dim(BB0) <- c(nsim * lastYrs, nMPs)
#
# FFMSY <- rawVals$F_FMSY[, , yrs]
# FFMSY <- aperm(FFMSY, c(1, 3, 2))
# dim(FFMSY) <- c(nsim * lastYrs, nMPs)
#
# LTY <- rawVals$LTY
# LTY <- aperm(LTY, c(1, 3, 2))
# dim(LTY) <- c(nsim * 10, nMPs)
#
# AAVY <- rawVals$AAVY
# AAVY <- aperm(AAVY, c(1, 3, 2))
# dim(AAVY) <- c(nsim * 10, nMPs)
#
# AAVE <- rawVals$AAVE
# # apply(AAVE[,,10], 2, mean, na.rm=TRUE)
# AAVE <- aperm(AAVE, c(1, 3, 2))
# dim(AAVE) <- c(nsim * 10, nMPs)
# # apply(AAVE, 2, mean, na.rm=TRUE)
#
# # Colors - to make the plot a bit more cheerful
# inputs <- which(MPtypes == "Input")
# outputs <- which(MPtypes == "Output")
# fonts <- rep(2, nmp)
# fonts[MPtypes == "Input"] <- 4
# NameCol <- rep("black", nmp)
# NameCol[MPs %in% canMPs] <- "green"
# index <- grep("ref", MPs)
# NameCol[index] <- "darkgray"
#
# if (class(cols) == "character")
# Col <- cols
# if (class(cols) == "logical" && !(cols))
# Col <- "lightblue"
#
# if (class(cols) == "logical" && cols) {
# cols1 <- rep(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",
# "#FFFF33", "#A65628", "#F781BF"), 4) # brewer.pal(8, 'Set1')
# cols2 <- rep(c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854",
# "#FFD92F", "#E5C494", "#B3B3B3"), 4) # brewer.pal(8, 'Set2')
# Col <- rep(cols1, length.out = nMPs)[1:nMPs]
# if (length(inputs) > 0)
# Col[inputs] <- cols2[1:length(inputs)]
# }
#
# Line <- 3.5
# CexName <- 1.25
# lncol <- "slategray"
# topcex <- cex.PM
# ncharmp <- max(nchar(MPs)) - 1.5
# multi <- 1.15
# op <- par(mfrow = c(2, 3), bty = "n", oma = c(4, ncharmp, 2, 0), mar = c(3,
# 2, 4, 3))
# # if (class(Title) == 'character') par(mfrow=c(2,3), bty='n',
# # oma=c(4,ncharmp,2,0), mar=c(3,2,4,3)) if (class(Title) !=
# # 'character') par(mfrow=c(2,3), bty='n', oma=c(4,ncharmp,0,0),
# # mar=c(3,2,4,3)) B/B0
# YLim <- c(0, 1)
# tt <- boxplot(BB0, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, cex.names = CexName,
# axes = FALSE, ylim = YLim)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE, las = 2)
# axis(side = 2, at = inputs, labels = FALSE, las = 2)
# if (Names)
# text(x = rep(-0.07, nmp), y = 1:nmp, labels = MPs, col = NameCol,
# cex = CexName, font = fonts, pos = 2, xpd = NA, srt = 0)
# mtext(side = 1, expression(italic(B/B[0])), cex = cex.lab, line = Line)
# mtext(side = 3, outer = TRUE, Years, line = -0.5)
# PosPMs <- c("SSB_SSB0", "B_BMSY", "F_FMSY", "AAVE", "AAVY")
# ind <- match(names(PMRefs), PosPMs)
# refs <- unlist(PMRefs[ind])
#
# if (incLine && refs[1] > 0) {
# ps <- round(apply(BB0 > refs[1], 2, sum, na.rm = TRUE)/(nrow(BB0)),
# 2)
# font <- rep(1, length(ps))
# ps <- sprintf("%3.2f", ps)
# if (refs[1] != 1)
# txt <- bquote(Prob. ~ italic(B) > ~.(refs[1]) ~ italic(B[0]))
# if (refs[1] == 1)
# txt <- bquote(Prob. ~ italic(B) > ~italic(B[0]))
#
# text(x = YLim[2] * 1.1, y = 1:nMPs, labels = ps, xpd = NA, font = font)
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# abline(v = refs[1], lty = 2, lwd = 2, col = lncol)
# }
#
# text(-0.2, nmp * 1.25, "Input Control", font = 4, xpd = NA, cex = 1.2,
# pos = 4)
# text(-0.2, nmp * 1.35, "Output Control", font = 2, xpd = NA, cex = 1.2,
# pos = 4)
# if (!is.null(canMPs)) {
# text(1, nmp * 1.25, "Available", font = 2, xpd = NA, cex = 1.2,
# col = "green", pos = 2)
# text(1, nmp * 1.35, "Not Available", font = 2, xpd = NA, cex = 1.2,
# pos = 2)
# }
#
# # B/BMSY
# YLim <- c(0, 3)
# tt <- boxplot(BBMSY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = YLim)
#
# mtext(side = 1, expression(italic(B/B[MSY])), cex = cex.lab, line = Line)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[2] > 0) {
# ps <- round(apply(BBMSY > refs[2], 2, sum, na.rm = TRUE)/(nrow(BBMSY)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# if (refs[2] != 1)
# txt <- bquote(Prob. ~ italic(B) > ~.(refs[2]) ~ italic(B[MSY]))
# if (refs[2] == 1)
# txt <- bquote(Prob. ~ italic(B) > ~italic(B[MSY]))
# # text(x=YLim[2]*1.1, y=1:nMPs, labels=ps, xpd=NA, font=font)
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# abline(v = refs[2], lty = 2, lwd = 2, col = lncol)
# }
#
#
# # F/FMSY
# YLim <- c(0, 2)
# tt <- boxplot(FFMSY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = YLim)
#
# mtext(side = 1, expression(italic(F/F[MSY])), cex = cex.lab, line = Line)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[3] > 0) {
# ps <- round(apply(FFMSY < refs[3], 2, sum, na.rm = TRUE)/(nrow(FFMSY)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# abline(v = refs[3], lty = 2, lwd = 2, col = lncol)
# if (refs[3] != 1)
# txt <- bquote(Prob. ~ italic(F) < ~.(refs[3]) ~ italic(F[MSY]))
# if (refs[3] == 1)
# txt <- bquote(Prob. ~ italic(F) < ~italic(F[MSY]))
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# }
#
#
# # AAVY
# if (refs[4] < 1)
# refs[4] <- refs[4] * 100
# AAVY <- AAVY * 100
# YLim <- c(0, 60)
# tt <- boxplot(AAVY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = YLim)
# mtext(side = 1, "Average Annual\n Variation Yield (%)", cex = cex.lab,
# line = Line + 1)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE, las = 2)
# axis(side = 2, at = inputs, labels = FALSE, las = 2)
# text(x = rep(-5, nmp), y = 1:nmp, labels = MPs, col = NameCol, cex = CexName,
# font = fonts, pos = 2, xpd = NA, srt = 0)
# if (incLine && refs[4] > 0) {
# ps <- round(apply(AAVY < refs[4], 2, sum, na.rm = TRUE)/(nrow(AAVY)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
# if (refs[4] != 1)
# txt <- bquote(Prob. ~ italic(AAVY) < ~.(refs[4]) ~ "%")
# if (refs[4] == 1)
# txt <- bquote(Prob. ~ italic(AAVY) < ~"%")
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
# }
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
#
# # AAVE
# if (refs[5] < 1)
# refs[5] <- refs[5] * 100
# AAVE <- AAVE * 100
# tt <- boxplot(AAVE, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE, ylim = c(0,
# 60))
# mtext(side = 1, "Average Annual\n Variation Effort (%)", cex = cex.lab,
# line = Line + 1)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[4] > 0) {
# ps <- round(apply(AAVE < refs[4], 2, sum, na.rm = TRUE)/(nrow(AAVE)),
# 2)
# ps <- sprintf("%3.2f", ps)
# axis(side = 4, at = 1:nMPs, labels = ps, las = 1, tick = FALSE,
# line = -1, xpd = NA)
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
# if (refs[5] != 1)
# txt <- bquote(Prob. ~ italic(AAVE) < ~.(refs[5]) ~ "%")
# if (refs[5] == 1)
# txt <- bquote(Prob. ~ italic(AAVE) < ~"%")
# text(x = YLim[2] * 1.2, y = nMPs * multi, txt, xpd = NA, pos = 2)
#
# }
# abline(v = refs[4], lty = 2, lwd = 2, col = lncol)
#
#
# # LTY
# tt <- boxplot(LTY, names = FALSE, las = 1, outline = outline, horizontal = TRUE,
# xlab = "", bty = "n", col = Col, xpd = NA, axes = FALSE)
# mtext(side = 1, "Relative Long-Term\n Yield", cex = cex.lab, line = Line +
# 1)
# axis(side = 1, cex.axis = CexName)
# axis(side = 2, at = outputs, labels = FALSE)
# axis(side = 2, at = inputs, labels = FALSE)
# if (incLine && refs[5] > 0)
# abline(v = refs[4], lty = 2, lwd = 2, col = "darkgray")
# par(op)
#
# }
#
#
# #' Plot the median biomass and yield relative to last historical year
# #'
# #' Compare median biomass and yield in first year and last 5 years of
# #' projection
# #'
# #' @param MSEobj An object of class MSE
# #' @param MPs Optional subset by MP
# #' @param lastYrs Last number of years of projection to calculate median
# #' @param XMin Optional minimum for the x-axis
# #' @param YMin Optional minimum for the y-axis
# #' @param ShowLabs Logical. Show the MP labels? Otherwise only plot points
# #' @return Invisibly returns a data frame containing information shown in the
# #' plot
# #' @author A. Hordyk
# #' @export Cplot
# Cplot <- function(MSEobj, MPs = NA, lastYrs = 5, XMin = NULL, YMin = NULL,
# ShowLabs = FALSE) {
# if (!all(is.na(MPs)))
# MSEobj <- Sub(MSEobj, MPs = MPs)
# nsim <- MSEobj@nsim
# Alpha <- 60
# if (nsim < 10)
# Alpha <- 180
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
# nyears <- MSEobj@nyears
# proyears <- MSEobj@proyears
#
# Stat <- MPStats(MSEobj, lastYrs = lastYrs)$BySim
# ny <- dim(Stat$Yield)[3]
# Stat$Yield <- Stat$Yield[, , , drop = FALSE]/Stat$Yield[, , rep(1,
# ny), drop = FALSE]
#
# RelYield <- apply(Stat$Yield, 2, median, na.rm = TRUE)
#
# Bcurr <- Stat$B_BMSY[, , 1] # Biomass at start of projections
# Bend <- apply((Stat$B_BMSY[, , (proyears - lastYrs + 1):proyears]),
# c(1, 2), median, na.rm = TRUE) # median biomass in last years
# RelBio <- apply(Bend/Bcurr, 2, median, na.rm = TRUE)
#
# XMin <- ifelse(is.null(XMin), 0, XMin)
# YMin <- ifelse(is.null(YMin), 0, YMin)
# XLim <- c(YMin, ceiling(max(RelBio)/0.5) * 0.5) * c(0.95, 1.05)
# YLim <- c(XMin, ceiling(max(RelYield)/0.5) * 0.5) * c(0.95, 1.05)
# op <- par(mfrow = c(1, 1), oma = c(3, 5, 1, 1), mar = c(2, 2, 0, 0))
# plot(RelBio, RelYield, xlim = XLim, ylim = YLim, type = "n", bty = "l",
# xlab = "", ylab = "", xaxs = "i", yaxs = "i", las = 1)
# if (ShowLabs)
# text(RelBio, RelYield, MSEobj@MPs)
# if (!ShowLabs)
# points(RelBio, RelYield, pch = 21, cex = 2, bg = "lightgray")
# abline(h = 1, lty = 3, col = "lightgray")
# abline(v = 1, lty = 3, col = "lightgray")
# mtext(side = 1, line = 3.5, paste("Median Biomass (last", lastYrs,
# "years)\n relative to current"), cex = 1.25)
# mtext(side = 2, line = 3, paste("Median Yield (last", lastYrs, "years)\n relative to current"),
# cex = 1.25)
# par(op)
# DF <- data.frame(MP = MSEobj@MPs, Biomass = RelBio, Catch = RelYield,
# stringsAsFactors = FALSE)
# invisible(DF)
#
# }
#
#
# #' Joint probability plot
# #'
# #' Calculates and plots the joint probability of meeting all performance
# #' metrics simultaneously
# #'
# #'
# #' @param MSEobj An object of class MSE
# #' @param PLim Probability limit (acceptable risk threshold; e.g., 0.8 for 80
# #' percent)
# #' @param YVar What to plot of the y-axis: choose from \code{c('LTY', 'STY',
# #' 'avgSSB_SSB0', 'avgB_BMSY')}
# #' @param PMRefs List containing the reference limits for each metric
# #' @param UseMean Logical. Calculate mean (TRUE) or median (FALSE)
# #' @param lastYrs Last number of years in projection period to calculate
# #' summary statistics
# #' @param AvailMPs Optional character vector of available MPs (plotted in a
# #' different colour)
# #' @param XLim Optional limits for the x-axis
# #' @param ShowCols Logical. Show the background colours?
# #' @param ShowLabs Logical. Show the MP labels?
# #' @param All Logical. Plot all MPs (TRUE) or only those above the probability
# #' limit (\code{PLim}))?
# #' @return Invisibly returns data frame containing statistics shown in the plot
# #' @author A. Hordyk
# #' @export Jplot
# Jplot <- function(MSEobj, PLim = 0.8, YVar = c("LTY", "STY", "avgSSB_SSB0",
# "avgB_BMSY"), PMRefs = list(B_BMSY = 0.5, SSB_SSB0 = 0.2), UseMean = TRUE,
# lastYrs = 10, AvailMPs = NULL, XLim = NULL, ShowCols = TRUE, ShowLabs = FALSE,
# All = TRUE) {
#
# nsim <- MSEobj@nsim
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
#
# PMs <- names(PMRefs)
# YVar <- match.arg(YVar, several.ok = FALSE)
# mYVar <- YVar
# mYVar[mYVar == "avgSSB_SSB0"] <- "SSB_SSB0m"
# mYVar[mYVar == "avgB_BMSY"] <- "B_BMSYm"
#
# perf <- MPStats(MSEobj, PMRefs = PMRefs, lastYrs = lastYrs, UseMean = UseMean)
# Probs <- perf$Probs
#
# if (lastYrs >= MSEobj@proyears)
# lastYrs <- 10
# index <- paste0(PMs, "ref")
# # Joint Prob above B refs and below F
# jointP <- Probs[[index[1]]]
# if (length(PMs) > 1) {
# for (X in 2:length(PMs)) {
# jointP <- jointP * Probs[[index[X]]]
# }
# }
# JP <- apply(jointP, 2, sum, na.rm = TRUE)/(lastYrs * nsim)
#
# # Probability Thresholds
# vl <- PLim
# hl <- 0
# if (vl < 1)
# vl <- vl * 100
#
# x <- JP
# if (max(x) <= 1)
# x <- x * 100
# y <- perf[[1]][, mYVar]
# if (max(y) <= 10)
# y <- y * 100
# adjj <- c(0.9, 1.1)
# if (is.null(XLim))
# XLim <- c(min(c(-10, min(x, na.rm = T) * adjj)), max(c(max(x, na.rm = T) *
# adjj, 110)))
# if (!All)
# XLim <- c(vl * 0.95, 105)
# YLim <- c(min(c(-10, min(y, na.rm = T) * adjj)), max(c(max(y, na.rm = T) *
# adjj, 110)))
#
# ## Legend
# BmsyRef <- unique(perf[[1]]$B_BMSYRef)
# B0Ref <- unique(perf[[1]]$SSB_SSB0Ref)
# FRef <- unique(perf[[1]]$F_FMSYRef)
# # legend text
# if (FRef == 1)
# leg1 <- bquote(italic(F) < ~italic(F[MSY]))
# if (FRef != 1)
# leg1 <- bquote(italic(F) < ~.(FRef) ~ italic(F[MSY]))
# if (B0Ref == 1)
# leg2 <- bquote(italic(B) > ~italic(B[0]))
# if (B0Ref != 1)
# leg2 <- bquote(italic(B) > ~.(B0Ref) ~ italic(B[0]))
# if (BmsyRef == 1)
# leg3 <- bquote(italic(B) > ~~italic(B[MSY]))
# if (BmsyRef != 1)
# leg3 <- bquote(italic(B) > ~.(BmsyRef) ~ italic(B[MSY]))
#
# legtex <- list(F_FMSY = leg1, SSB_SSB0 = leg2, B_BMSY = leg3)
# ind <- match(PMs, names(legtex))
# Legend <- NULL
# Legend <- append(Legend, as.expression(legtex[ind]))
#
# # Plot
# xlab <- "Probability > All Performance Limits"
# if (UseMean)
# ylab <- switch(YVar, LTY = paste0("Long-Term Yield (last ", lastYrs,
# " years)"), STY = paste0("Short-Term Yield (first ", lastYrs,
# " years)"), avgB_BMSY = paste0("Mean B/BMSY (%) (last ", lastYrs,
# " years)"), avgSSB_SSB0 = paste0("Mean B/B0 (%) (last ", lastYrs,
# " years)"))
# if (!UseMean)
# ylab <- switch(YVar, LTY = paste0("Long-Term Yield (last ", lastYrs,
# " years)"), STY = paste0("Short-Term Yield (first ", lastYrs,
# " years)"), avgB_BMSY = paste0("Median B/BMSY (%) (last ",
# lastYrs, " years)"), avgSSB_SSB0 = paste0("Median B/B0 (%) (last ",
# lastYrs, " years)"))
# op <- par(mfrow = c(1, 1), mar = c(4, 4, 1, 1), oma = c(1, 1, 0, 6))
# plot(NA, xlim = XLim, ylim = YLim, xlab = xlab, ylab = ylab, bty = "l",
# las = 1, cex.lab = 1.25)
#
# abline(v = vl, col = "#99999940", lwd = 2)
# Alpha <- 15
# # polygons
# LeftCol <- rgb(red = 255, green = 0, blue = 0, alpha = Alpha, names = NULL,
# maxColorValue = 255)
# RightCol <- rgb(red = 0, green = 255, blue = 0, alpha = Alpha, names = NULL,
# maxColorValue = 255)
# if (ShowCols) {
# polygon(x = c(0, vl, vl, 0), y = c(0, 0, hl, hl), col = LeftCol,
# border = NA)
# polygon(x = c(0, vl, vl, 0), y = c(0, 0, max(YLim), max(YLim)),
# col = LeftCol, border = NA)
# polygon(x = c(vl, max(XLim), max(XLim), vl), y = c(0, 0, max(YLim),
# max(YLim)), col = RightCol, border = NA)
# polygon(x = c(vl, max(XLim), max(XLim), vl), y = c(hl, hl, max(YLim),
# max(YLim)), col = RightCol, border = NA)
# }
#
# MPs <- MSEobj@MPs
# Pch <- rep(21, length(MPs))
# Pch[grep("FMSY", MPs)] <- 24
# Pch[grep("NFref", MPs)] <- 24
#
# # Which MPs meet minimum PMs
# ind <- which(x >= vl & y >= hl)
# coly <- rep("darkgray", length(MPs))
# coly[ind] <- "black"
# if (!is.null(AvailMPs))
# coly[MPs %in% AvailMPs & !(x >= vl & y >= hl)] <- "Medium Sea Green"
# if (!is.null(AvailMPs))
# coly[MPs %in% AvailMPs & (x >= vl & y >= hl)] <- "green"
# coly[grep("FMSY", MPs)] <- "lightgray"
# coly[grep("NFref", MPs)] <- "lightgray"
#
# MPtype <- sapply(1:nMPs, function(X) class(get(MPs[X])))
# fonts <- rep(2, nMPs)
# fonts[MPtype == "Input"] <- 4
# Cex <- 1.5
# if (!ShowLabs)
# points(x, y, bg = coly, pch = Pch, cex = Cex, col = "black")
# if (ShowLabs)
# text(x, y, MPs, font = fonts, col = coly, cex = 1)
#
# # Add Legend
# text(min(XLim), max(YLim), "Performance Limits", cex = 1.25, pos = 4)
# temp <- 0.95
# for (XX in 1:length(Legend)) {
# text(min(XLim), max(YLim) * temp, Legend[XX], pos = 4)
# temp <- temp - 0.05
# }
# Cvec <- c("darkgray", "black", "Medium Sea Green", "green", "lightgray")
# if (!ShowLabs)
# legend(100, max(YLim), pch = c(21, 21, 21, 21, 24), legend = c("Not Acceptable",
# "Acceptable", "Available", "Acceptable & Available", "Reference"),
# bty = "n", pt.bg = Cvec, pt.cex = Cex, xpd = NA)
#
# if (ShowLabs)
# legend(100, max(YLim), text.font = c(2, 4), legend = c("Output Control",
# "Input Control", "Not Acceptable", "Acceptable", "Available",
# "Acceptable & Available", "Reference"), bty = "n", text.col = c("black",
# "black", Cvec), pt.cex = Cex, xpd = NA)
#
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears,
# "(last", lastYrs, "years)")
# mtext(side = 3, Years, cex = 1.25)
#
# if (YVar == "B_BMSYm")
# abline(h = 100, col = "#99999940", lwd = 2)
# if (YVar == "SSB_SSB0m")
# abline(h = 50, col = "#99999940", lwd = 2)
# par(op)
# DF <- data.frame(MPs = MPs, Yield = y, Prob = x, Pass = x >= vl, stringsAsFactors = FALSE)
# invisible(DF[order(DF$Prob, decreasing = TRUE), ])
# }
#
#' KOBE plot: a projection by projection plot of F/FMSY and B/BMSY
#'
#' A standard KOBE plot by each method that also shows the percentage of
#' methods that ended up in each quadrant.
#'
#'
#' @param MSEobj An object of class MSE
#' @param maxsim Maximum number of simulations (lines) to plot on each panel.
#' @param MPs Optional subset MSE object by MP
#' @param sims Optional subset MSE object by simulation
#' @param maxMP Maximum number of MPs to include in plot
#' @param nam The name of the plot
#' @param cex.leg Size of legend
#' @note Apologies for the nauseating shading.
#' @author T. Carruthers with some additions from A. Hordyk
#' @export Kplot
Kplot <- function(MSEobj, maxsim = 60, MPs = NA, sims = NULL, maxMP = 9,
nam = NA, cex.leg = 1.5) {
# png('Kplot.png')
if (!is.null(sims) & all(is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims)
if (!is.null(sims) & all(!is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims, MPs = MPs)
if (is.null(sims) & !all(is.na(MPs)))
MSEobj <- Sub(MSEobj, MPs = MPs)
nMPs <- MSEobj@nMPs
nsim <- MSEobj@nsim
if (is.null(sims) & nsim > maxsim)
MSEobj <- Sub(MSEobj, sims = 1:maxsim)
if (nMPs > maxMP) {
message("MSE object has more than ", maxMP, " MPs. Plotting the first ",
maxMP)
MSEobj <- Sub(MSEobj, MPs = 1:maxMP)
nMPs <- MSEobj@nMPs
}
nr <- floor((MSEobj@nMPs)^0.5)
nc <- ceiling((MSEobj@nMPs)/nr)
Cex <- 1.5
TitleCex <- 1.5
FMSYr <- quantile(MSEobj@F_FMSY, c(0.001, 0.9), na.rm = T)
BMSYr <- quantile(MSEobj@SB_SBMSY, c(0.001, 0.975), na.rm = T)
# dev.new2(width=nc*3,height=nr*3.6)
# par(mfrow=c(nr,nc),mai=c(0.45,0.45,0.45,0.01),omi=c(0.45,0.3,0.35,0.01))
# par(mfcol=c(nr,nc),mai=c(0.2,0.35,0.3,0.01),omi=c(0.5,0.4,0.4,0.05))
if (is.na(nam))
op <- par(mfrow = c(nr, nc), mar = c(2, 2, 3, 1), oma = c(3, 3.5, 1.2,
0))
if (!is.na(nam))
op <- par(mfrow = c(nr, nc), mar = c(2, 2, 3, 1), oma = c(3, 3.5, 3, 0))
colsse <- rainbow(MSEobj@proyears, start = 0.63, end = 0.95)[1:MSEobj@proyears]
colsse <- makeTransparent(colsse, 95)
XLim <- c(0, 3)
YLim <- c(0, 2.5)
pmat <- matrix(NA, nrow = nc, ncol = nr, byrow = FALSE)
pmat[1:nMPs] <- 1:nMPs
pmat <- t(pmat)
for (mm in 1:MSEobj@nMPs) {
plot(MSEobj@SB_SBMSY[1, mm, 1], MSEobj@F_FMSY[1,
mm, 1], xlim = XLim, ylim = YLim,
col = colsse[1], bty = "n", axes = FALSE)
if (nrow(pmat) > 1) {
if (mm %in% pmat[, 1]) {
axis(side = 2, labels = TRUE, las = 1)
} else axis(side = 2, labels = FALSE)
if (mm %in% pmat[nr, ]) {
axis(side = 1, labels = TRUE)
} else axis(side = 1, labels = FALSE)
nas <- apply(pmat, 1, sum)
rr <- which.max(nas)
nas2 <- apply(pmat, 2, sum)
cc <- which(is.na(nas2))
if (mm %in% pmat[rr, cc])
axis(side = 1, labels = TRUE)
} else {
if (mm == 1)
axis(side = 2, labels = TRUE, las = 1)
if (mm != 1)
axis(side = 2, labels = FALSE)
axis(side = 1, labels = TRUE)
}
OO <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] < 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] > 1, na.rm = T)/MSEobj@nsim * 100, 1)
OU <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] > 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] > 1, na.rm = T)/MSEobj@nsim * 100, 1)
UO <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] < 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] < 1, na.rm = T)/MSEobj@nsim * 100, 1)
UU <- round(sum(MSEobj@SB_SBMSY[, mm, MSEobj@proyears] > 1 & MSEobj@F_FMSY[,
mm, MSEobj@proyears] < 1, na.rm = T)/MSEobj@nsim * 100, 1)
# alp<-80
# polygon(c(1,-1000,-1000,1),c(1,1,1000,1000),col=makeTransparent('orange',alp),border=makeTransparent('orange',alp))
# polygon(c(1,1000,1000,1),c(1,1,1000,1000),col=makeTransparent('yellow',alp),border=makeTransparent('yellow',alp))
# polygon(c(1,-1000,-1000,1),c(1,1,-1000,-1000),col=makeTransparent('yellow',alp),border=makeTransparent('yellow',alp))
# polygon(c(1,1000,1000,1),c(1,1,-1000,-1000),col=makeTransparent('green',alp),border=makeTransparent('yellow',alp))
abline(h = 1, col = "grey", lwd = 3)
abline(v = 1, col = "grey", lwd = 3)
# abline(v=c(0.1,0.5),col='grey',lwd=2)
y <- 1:(MSEobj@proyears - 1)
rng <- 1:min(maxsim, MSEobj@nsim)
points(MSEobj@SB_SBMSY[rng, mm, 1], MSEobj@F_FMSY[rng, mm, 1], pch = 19,
cex = 0.8, col = colsse[1])
points(MSEobj@SB_SBMSY[rng, mm, MSEobj@proyears], MSEobj@F_FMSY[rng,
mm, MSEobj@proyears], pch = 19, cex = 0.8, col = colsse[MSEobj@proyears])
if (mm == 1)
legend("right", c("Start", "End"), bty = "n", text.col = c(colsse[1],
colsse[MSEobj@proyears]), pch = 19, col = c(colsse[1],
colsse[MSEobj@proyears]))
legend("topleft", paste(OO, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
legend("topright", paste(OU, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
legend("bottomleft", paste(UO, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
legend("bottomright", paste(UU, "%", sep = ""), bty = "n", text.font = 2,
cex = cex.leg)
mtext(MSEobj@MPs[mm], 3, line = 0.6, cex = TitleCex)
}
mtext(expression(B/B[MSY]), 1, outer = T, line = 2, cex = Cex)
mtext(expression(F/F[MSY]), 2, outer = T, line = 1.2, cex = Cex)
# if(is.na(nam))mtext(deparse(substitute(MSEobj)),3,outer=T,line=0.25,font=2,
# cex=TitleCex)
# if(!is.na(nam))mtext(MSEobj@Name,3,outer=T,line=0.25,font=2,
# cex=TitleCex)
if (!is.na(nam))
mtext(nam, 3, outer = TRUE, line = 0.25, font = 2, cex = TitleCex)
# dev.off()
par(op)
}
#' National Oceanographic and Atmospheric Administration default plot 1
#'
#' A preliminary plot for returning trade-offs plots and performance table for
#' total yield, variability in yield, probability of overfishing and likelihood
#' of biomass dropping below 50 per cent BMSY
#'
#'
#' @param MSEobj An object of class MSE
#' @param nam Title of plot
#' @param type Plots full range of data if NA. Plots a subset that meet
#' thresholds if not NA.
#' @param panel Should a two panel plot be made or should plots be made in
#' sequence.
#' @return A table of performance metrics.
#' @author T. Carruthers
#' @export NOAA_plot
NOAA_plot <- function(MSEobj, nam = NA, type = NA, panel = T) {
Yd <- rep(NA, MSEobj@nMPs)
B50 <- rep(NA, MSEobj@nMPs)
PNOF <- rep(NA, MSEobj@nMPs)
LTY <- rep(NA, MSEobj@nMPs)
STY <- rep(NA, MSEobj@nMPs)
VY <- rep(NA, MSEobj@nMPs)
y1 <- 1:(MSEobj@proyears - 1)
y2 <- 2:MSEobj@proyears
yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
PNOF[mm] <- round(mean(MSEobj@F_FMSY[, mm, ] <= 1, na.rm = T) * 100, 1)
B50[mm] <- round(mean(MSEobj@SB_SBMSY[, mm, ] >= 0.5, na.rm = T) * 100, 1)
LTY[mm] <- round(mean(MSEobj@Catch[, mm, yend]/RefYd >= 0.5, na.rm = T), 3) * 100
AAVY <- apply((((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])/MSEobj@Catch[, mm, y2])^2)^0.5, 1, mean, na.rm = T)
VY[mm] <- round(mean(AAVY <= 0.15, na.rm = T), 3) * 100
}
# dev.new2(width=7,height=7)
if (panel)
op <- par(mfrow = c(1, 2), mai = c(1.5, 1.5, 0.1, 0.1), omi = c(0.1, 0.1, 0.4, 0))
if (is.na(type)) {
tradeoffplot(PNOF, LTY, "Prob. of not overfishing (%)", "Long-term yield ",
MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
tradeoffplot(B50, VY, "Prob. biomass above half BMSY (%)", "Prob. AAVY less than 15%",
MSEobj@MPs[1:MSEobj@nMPs], vl = 80, hl = 50)
} else {
tradeoffplot3(PNOF, LTY, "Prob. of not overfishing (%)", "Long-term yield",
MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100, xlim = c(45,
105), ylim = c(0, 105))
tradeoffplot3(B50, VY, "Prob. biomass above half BMSY (%)", "Prob. AAVY less than 15%",
MSEobj@MPs[1:MSEobj@nMPs], vl = 80, hl = 50, xlim = c(75, 105),
ylim = c(45, 105))
}
# if(is.na(nam))mtext(deparse(substitute(MSEobj)),3,outer=T,line=0.3,font=2)
# if(!is.na(nam) &
# !is.character(nam))mtext(MSEobj@Name,3,outer=T,line=0.3,font=2)
# if(!is.na(nam) &
# is.character(nam))mtext(nam,3,outer=T,line=0.3,font=2)
if (panel) par(op)
temp <- data.frame(PNOF, B50, LTY, VY)
row.names(temp) <- MSEobj@MPs[1:MSEobj@nMPs]
temp
}
#' A projection by projection plot of F/FMSY and B/BMSY
#'
#' A shorter version of the plot method for MSEs that just shows the projected
#' trends in stock status and over exploitation
#'
#'
#' @param MSEobj An object of class MSE
#' @param nam Title of plot
#' @param maxMP The maximum number of MPs to plot (defaults to the first 10)
#' @param MPs A character vector of MPs to plot
#' @param maxsims Integer, the maximum number of simulations to plot
#' @author T. Carruthers
#' @export Pplot
Pplot <- function(MSEobj, nam = NA, maxMP = 10,MPs=NA,maxsims=20) {
op <- par(no.readonly = TRUE)
on.exit(par(op))
if(!is.na(MPs)){
maxMP<-length(MPs)
MSEobj<-Sub(MSEobj,MPs=MPs)
} else{
if(MSEobj@nMPs>maxMP)MSEobj<-Sub(MSEobj,MPs=MSEobj@MPs[1:maxMP])
}
maxsims <- min(maxsims, MSEobj@nsim)
MSEobj<-Sub(MSEobj,sims=1:maxsims)
FMSYr <- quantile(MSEobj@F_FMSY, c(0.001, 0.9), na.rm = T)
BMSYr <- quantile(MSEobj@SB_SBMSY, c(0.001, 0.975), na.rm = T)
colsse <- rainbow(100, start = 0, end = 0.36)[1:100]
colB <- rep(colsse[100], ceiling(BMSYr[2] * 100))
colB[1:100] <- colsse
colB <- makeTransparent(colB, 60)
colsse <- rainbow(200, start = 0, end = 0.36)[200:1]
colF <- rep(colsse[200], ceiling(FMSYr[2] * 100))
colF[1:200] <- colsse
colF <- makeTransparent(colF, 60)
Yd <- rep(NA, MSEobj@nMPs)
P10 <- rep(NA, MSEobj@nMPs)
P50 <- rep(NA, MSEobj@nMPs)
P100 <- rep(NA, MSEobj@nMPs)
POF <- rep(NA, MSEobj@nMPs)
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
Yd[mm] <- round(mean(apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd,
na.rm = T) * 100, 1)
# cbind(MSEobj@Catch[,mm,yind],unlist(MSEobj@OM$MSY))
POF[mm] <- round(sum(MSEobj@F_FMSY[, mm, ] > 1, na.rm = T)/prod(dim(MSEobj@F_FMSY[,
mm, ]), na.rm = T) * 100, 1)
P10[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
mm, ])) * 100, 1)
P50[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.5, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
mm, ])) * 100, 1)
P100[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
mm, ])) * 100, 1)
}
nr <- ceiling(MSEobj@nMPs/8)
nc <- ceiling(MSEobj@nMPs/nr)
nr <- nr * 2
MSEcols <- c("red", "green", "blue", "orange", "brown", "purple", "dark grey",
"violet", "dark red", "pink", "dark blue", "grey")
temp <- array(0, c(nr * 2 + (nr/2 - 1), nc * 2))
i <- 0
for (c in 1:nc) {
for (r in 1:nr) {
i <- i + 1
temp[(ceiling(r/2) - 1) + (1:2) + (r - 1) * 2, (1:2) + (c - 1) * 2] <- ((c - 1) * nr) + r
}
}
par(mfcol = c(nr, nc), mar = c(2, 2, 2, 1), oma = c(3, 2, 2, 0))
layout(temp)
# dev.new2(width=nc*3,height=nr*3)
lwdy <- 2.5
for (mm in 1:MSEobj@nMPs) {
plot(MSEobj@F_FMSY[1, mm, ], ylim = FMSYr, col = colF[ceiling(mean(MSEobj@F_FMSY[1,
mm, ], na.rm = T) * 100)], type = "l", lwd = lwdy)
for (i in 1:MSEobj@nsim) lines(MSEobj@F_FMSY[i, mm, ], col = colF[ceiling(mean(MSEobj@F_FMSY[i,
mm, ], na.rm = T) * 100)], lwd = lwdy)
abline(h = 100, col = "grey", lwd = 3)
mtext(MSEobj@MPs[mm], 3, outer = F, line = 0.6)
legend("topright", c(paste(POF[mm], "% POF", sep = ""), paste(Yd[mm],
"% FMSY yield", sep = "")), bty = "n", cex = 0.8)
if (mm %in% (1:(nr/2)))
mtext("F/FMSY", 2, line = 2.5, outer = F)
abline(h = 1, col = makeTransparent("grey", 30), lwd = 2.5)
plot(MSEobj@SB_SBMSY[1, mm, ], ylim = BMSYr, col = colB[ceiling(MSEobj@SB_SBMSY[1,
mm, MSEobj@proyears] * 100)], type = "l", lwd = lwdy)
for (i in 1:MSEobj@nsim) lines(MSEobj@SB_SBMSY[i, mm, ], col = colB[ceiling(MSEobj@SB_SBMSY[i,
mm, MSEobj@proyears] * 100)], lwd = lwdy)
abline(h = 100, col = "grey", lwd = 3)
legend("topright", c(paste(P100[mm], "% < BMSY", sep = ""), paste(P50[mm],
"% < 0.5BMSY", sep = ""), paste(P10[mm], "% < 0.1BMSY", sep = "")),
bty = "n", cex = 0.8)
if (mm %in% (1:(nr/2)))
mtext("B/BMSY", 2, line = 2.5, outer = F)
abline(h = 1, col = makeTransparent("grey", 30), lwd = 2.5)
}
mtext("Projection year", 1, outer = T, line = 1.2)
if (is.na(nam))
mtext(deparse(quote(MSEobj)), 3, outer = T, line = 0.3, font = 2)
if (!is.na(nam) & !is.character(nam))
mtext(MSEobj@Name, 3, outer = T, line = 0.3, font = 2)
if (!is.na(nam) & is.character(nam))
mtext(nam, 3, outer = T, line = 0.3, font = 2)
return(invisible())
}
#' A projection by projection plot of F/FMSY, B/BMSY, B/B0, and yield
#'
#'
#' @param MSEobj An object of class MSE
#' @param YVar What to plot on the y-axis? Options are: \code{c('SSB_SSB0',
#' 'SSB_SSBMSY', 'F_FMSY', 'Yield')}
#' @param MPs Optional subset by MP
#' @param sims Optional subset by simulation
#' @param traj Plot all projections (\code{all}), only quantiles
#' (\code{quant}), or both projections and median (\code{both})
#' @param quants Numeric vector of length 2 specifying the quantiles (e.g.,
#' 10th and 90th. Median is always included)
#' @param incquant Logical. Include the quantiles or only plot median?
#' @param quantcol Colour of the quantile polygon
#' @param ref.lines Numeric vector of y-values for horizontal reference lines. Set to NULL to remove lines.
#' @param RefYield Should yield be relative to long-term optimum (\code{lto})
#' or last historical year (\code{curr})
#' @param LastYr Logical. Include the last historical year in the yield
#' projections?
#' @param maxMP Maximum number of MPs to plot
#' @param alpha Alpha for transparency of lines
#' @param cex.axis Size of axis text
#' @param cex.lab Size of axis label
#' @param YLab Optional label for y-axis
#' @param incMP Logical. Include name of MP?
#' @param MPcex Size of MP label
#' @param MPcol Optional character vector of colors for MP labels
#' @param incLeg Logical. Include a legend?
#' @param cex.leg Size of legend text
#' @param legPos Legend position
#' @param yline Optional horizontal lines
#' @param xline Optional vertical lines
#' @param parOR Logical to over-ride the par parameters
#' @param xaxis Logical. Should x-axis labels be displayed?
#' @param yaxis Logical. Should y-axis labels be displayed?
#' @param oneIt Logical. Should one iteration be plotted on the quantile plot?
#' @param ... Additional arguments to be passed to plotting functions
#' @author T. Carruthers & A.Hordyk
#' @export Pplot2
Pplot2 <- function(MSEobj, YVar = c("F_FMSY", "SSB_SSBMSY"), MPs = NA, sims = NULL,
traj = c("all", "quant", "both"), quants = c(0.1, 0.9), incquant = TRUE,
quantcol = "lightgray",
RefYield = c("lto", "curr"), LastYr = TRUE,
ref.lines=c(0.5, 1, 1.5), maxMP = 6, alpha = 60,
cex.axis = 1, cex.lab = 1, YLab = NULL, incMP = TRUE, MPcex = 1,
MPcol='black',
incLeg = TRUE, cex.leg = 1.5, legPos = "topleft", yline = NULL, xline=NULL, parOR = FALSE,
xaxis = TRUE, yaxis = TRUE, oneIt=TRUE, ...) {
old_par <- par(no.readonly = TRUE)
on.exit(par(old_par))
YVars <- c("SSB_SSB0", "SSB_SSBMSY", "F_FMSY", "Yield")
YVar <- match.arg(YVar, choices = YVars, several.ok = TRUE)
op <- par(no.readonly=TRUE)
if (!is.null(YLab) & length(YLab) != length(YVar))
stop("Length of YLab must equal length of YVar")
if (!is.null(sims) & all(is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims)
if (!is.null(sims) & all(!is.na(MPs)))
MSEobj <- Sub(MSEobj, sims = sims, MPs = MPs)
if (is.null(sims) & !all(is.na(MPs)))
MSEobj <- Sub(MSEobj, MPs = MPs)
nsim <- MSEobj@nsim
if (nsim < 10)
alpha <- 180
nMPs <- MSEobj@nMPs
if (nMPs > maxMP) {
message("MSE object has more than ", maxMP, " MPs. Plotting the first ",
maxMP)
MSEobj <- Sub(MSEobj, MPs = 1:maxMP)
nMPs <- MSEobj@nMPs
}
MPs <- MSEobj@MPs
proyears <- MSEobj@proyears
RefYd <- MSEobj@OM$RefY
RefYield <- match.arg(RefYield)
traj <- match.arg(traj)
# Calculate Statistics Biomass/B0
temp <- as.matrix(expand.grid(1:nsim, 1:nMPs, 1:proyears))
Deplet <- array(NA, dim = dim(MSEobj@SB_SBMSY))
Deplet[temp] <- (MSEobj@SB_SBMSY[temp] * MSEobj@OM$SSBMSY_SSB0[temp[, 1]])
# Yield - including last historical year (current year)
# pastC <- apply(MSEobj@CB_hist[, , , , drop = FALSE], c(1, 3), sum,
# na.rm = TRUE)/RefYd
pastC <- MSEobj@CB_hist/RefYd
temp <- aperm(replicate(nMPs, pastC), c(1, 3, 2))
lastYr <- temp[, , MSEobj@nyears, drop = FALSE]
Yield <- abind::abind(lastYr, MSEobj@Catch[, , , drop = FALSE]/RefYd, along = 3)
Dat <- list(SSB_SSB0 = Deplet, SSB_SSBMSY = MSEobj@SB_SBMSY, F_FMSY = MSEobj@F_FMSY,
Yield = Yield)
Dat <- Dat[YVar]
if ("Yield" %in% YVar & RefYield == "curr") {
ny <- dim(Dat$Yield)[3]
Dat$Yield <- Dat$Yield[, , , drop = FALSE]/Dat$Yield[, , rep(1,
ny), drop = FALSE]
}
if ("Yield" %in% YVar & !LastYr) {
Dat$Yield <- Dat$Yield[, , 2:proyears, drop = FALSE]
}
nr <- length(Dat)
nc <- nMPs
dots <- list(...)
ylims <- cbind(0, unlist(lapply(Dat, quantile, 0.9, na.rm = TRUE)))
if ("SSB_SSB0" %in% YVar) {
index <- which(YVar == "SSB_SSB0")
ylims[index, ] <- c(0, max(1, max(ylims[index, ])))
}
if (length(dots$ylim) != 0) ylims <- matrix(rep((dots$ylim), length(Dat)), nrow = nr, byrow = TRUE)
colrange <- matrix(unlist(lapply(Dat, quantile, c(0.001, 0.975), na.rm = TRUE)),
nrow = nr, byrow = TRUE)
colsse <- rainbow(100, start = 0, end = 0.36)[1:100]
# Col<-rep(colsse[100],ceiling(colrange[2]*100)) Col[1:100]<-colsse
Col <- makeTransparent(colsse, alpha)
if (length(dots$lwd) == 0) lwd <- 3
if (length(dots$lwd) != 0) lwd <- dots$lwd
YLabs <- list(expression(SSB/SSB[0]), expression(SSB/SSB[MSY]), expression(F/F[MSY]),
"Yield relative\n to Long-Term\n Optimum")
if ("Yield" %in% YVar & RefYield == "curr")
YLabs[[4]] <- expression(Yield/Yield[current])
YLabs <- YLabs[match(YVar, YVars)]
if (!is.null(YLab))
YLabs <- YLab
if (!parOR) {
if ("Yield" %in% YVar & RefYield != "curr") {
op <- par(mfrow = c(nr, nc), bty = "n", mar = c(2, 2, 0, 0), oma = c(4, 8, 2, 1))
} else op <- par(mfrow = c(nr, nc), bty = "n", mar = c(2, 2, 0, 0), oma = c(4, 4, 2, 1))
}
if (parOR) {
nr <- par()$mfrow[1]
nc <- par()$mfrow[2]
}
for (X in 1:length(Dat)) {
Col2 <- Col
dat <- Dat[[X]]
ylim <- ylims[X, ]
ylab <- YLabs[[X]]
if (grepl("F_FMSY", YVar[X])) Col2 <- rev(Col)
for (mm in 1:nMPs) {
plot(1:length(dat[1, mm, ]), dat[1, mm, ], ylim = ylim, type = "n",
axes = FALSE, xlab = "", ylab = "")
# add reference lines
if (!is.null(ref.lines)) {
for (h in ref.lines) abline(h=h, lty=3, lwd=1, col="darkgray")
}
if (traj == "all" | traj=="both")
for (i in 1:MSEobj@nsim) lines(dat[i, mm, ],
col = Col2[min(100, ceiling(dat[i, mm, length(dat[i, mm, ])] * 100))], lwd = lwd)
if (traj == "quant" | traj=="both") {
stats <- apply(dat[, mm, , drop = FALSE], 3, quantile,
c(quants[1], 0.5, quants[2]), na.rm = TRUE)
if (length(quants) == 4)
stats2 <- apply(dat[, mm, , drop = FALSE], 3, quantile,
c(quants[3], quants[4]), na.rm = TRUE)
if (length(quants) != 4) stats2 <- NULL
if (traj=="both") {
stats2 <- NULL
incquant <- FALSE
}
if (!incquant) lines(1:length(stats[2, ]), stats[2, ], lwd = 3)
if (incquant) {
if (!is.null(stats2)) {
if (length(quantcol) < 2)
quantcol <- c(quantcol, "darkgray")
polygon(x = c(1:length(stats2[2, ]), length(stats2[2, ]):1),
y = c(stats2[1, ], rev(stats2[2, ])), col = quantcol[2],
border = FALSE)
}
polygon(x = c(1:length(stats[2, ]), length(stats[2, ]):1),
y = c(stats[1, ], rev(stats[3, ])), col = quantcol[1],
border = FALSE)
lines(1:length(stats[2, ]), stats[2, ], lwd = 3)
if (oneIt) lines(dat[2, mm, , drop = FALSE] , lwd=1)
}
}
if (X == nr & xaxis)
axis(side = 1, labels = TRUE, cex.axis = cex.axis)
if (X != nr | !xaxis)
axis(side = 1, labels = FALSE)
if (mm == 1 & yaxis) {
axis(side = 2, labels = TRUE, cex.axis = cex.axis, las = 1)
mtext(side = 2, ylab, cex = cex.lab, line = 3)
}
if (parOR) {
if (xaxis)
axis(side = 1, labels = TRUE, cex.axis = cex.axis, las = 1)
if (yaxis & mm == 1)
axis(side = 2, labels = TRUE, cex.axis = cex.axis, las = 1)
}
axis(side = 2, labels = FALSE)
MPcol <- rep(MPcol, MSEobj@nMPs)[1:MSEobj@nMPs]
if (incMP & X == 1 & !parOR)
mtext(side = 3, MSEobj@MPs[mm], cex = MPcex, col=MPcol[mm])
if (incMP & parOR)
mtext(side = 3, MSEobj@MPs[mm], cex = MPcex, col=MPcol[mm])
# Legend #
if (mm == 1 & incLeg & (traj == "quant"||traj=="both") & X == 1) {
if (incquant) {
if (is.null(stats2)) {
pquants <- quants
if (max(quants) < 1) pquants <- quants * 100
legend(legPos, legend = c(expression("50"^"th" * " (median)"),
bquote(.(pquants[1])^th ~ and ~ .(pquants[2])^th)),
pch = 22, title = "Percentile", pt.bg = c("black", quantcol[1], quantcol[2]),
bty = "n", cex = cex.leg, xpd = NA, col = "black")
}
if (!is.null(stats2)) {
pquants <- quants
if (max(quants) < 1)
pquants <- quants * 100
legend(legPos, legend = c(expression("50"^"th" * " (median)"),
bquote(.(pquants[1])^th ~ and ~ .(pquants[2])^th),
bquote(.(pquants[3])^th ~ and ~ .(pquants[4])^th)),
pch = 22, title = "Percentile", pt.bg = c("black", quantcol[1], quantcol[2]),
bty = "n", cex = cex.leg, xpd = NA, col = "black")
}
} else {
legend(legPos, legend = "Median", pch = 15, col = "black", bty = "n", cex = 1.25)
}
}
if (!is.null(yline))
abline(h = yline[X], lwd = 2, col = "darkgray", lty = 2)
if (!is.null(xline)) {
for (xx in 1:length(xline)) {
abline(v = xline[xx], lwd = 2, col = "darkgray", lty = xx+1)
}
}
}
}
mtext(side = 1, "Projection Years", line = 2, cex = cex.lab, outer = TRUE)
if (!parOR) par(op)
invisible(Dat)
}
#' Performance Whisker Plot
#'
#' A NAFO / ICCAT / SSB style MSE performance whisker plot
#'
#'
#' @param MSEobj An object of class MSE
#' @return A box plot of performance
#' @author T. Carruthers
#' @export PWhisker
PWhisker<-function(MSEobj){#},Pnames=c("POF","C30","D30","LD","DNC","LDNC","PGK","AAVC")){
P10 <- P50 <- PNOF <- LTY <- STY <- AAVY <- VY <- array(NA,c(MSEobj@nsim, MSEobj@nMPs))
Pnames<-c("B10","B50","PNOF","LTY","AAVY")
nsim<-MSEobj@nsim
nMPs<-MSEobj@nMPs
nperf<-length(Pnames)
store<-array(NA,c(MSEobj@nMPs,nperf,5))
y1 <- 1:(MSEobj@proyears - 1)
y2 <- 2:MSEobj@proyears
yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
P10[,mm] <- round(apply(MSEobj@SB_SBMSY[, mm, ] <= 0.1,1,mean, na.rm = T) * 100, 1)
P50[,mm] <- round(apply(MSEobj@SB_SBMSY[, mm, ] <= 0.5,1,mean, na.rm = T) * 100, 1)
PNOF[,mm] <- round(apply(MSEobj@F_FMSY[, mm, ] <= 1,1,mean, na.rm = T) * 100, 1)
LTY[,mm] <- round(apply(MSEobj@Catch[, mm, yend]/RefYd >= 0.5,1,mean, na.rm = T), 3) * 100
AAVY[,mm] <- apply((((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])/MSEobj@Catch[, mm, y2])^2)^0.5, 1, mean, na.rm = T)
#VY[,mm] <- round(apply(AAVY <= 0.15, na.rm = T), 3) * 100
store[mm,1,]<-quantile(P10[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,2,]<-quantile(P50[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,3,]<-quantile(PNOF[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,4,]<-quantile(LTY[,mm],c(0.05,0.25,0.5,0.72,0.95))
store[mm,5,]<-quantile(AAVY[,mm],c(0.05,0.25,0.5,0.72,0.95))
#store[mm,6,]<-quantile(VY[,mm],c(0.05,0.25,0.5,0.72,0.95))
}
op <- par(mfrow=c(nperf,1),mai=c(0.1,0.3,0.01,0.05),omi=c(1.2,0.4,0.05,0.01))
for(i in 1:nperf){
xlab=F
if(i==nperf)xlab=T
custombar(dat=store[,i,],MPnams=MSEobj@MPs,xlab=xlab)
mtext(Pnames[i],2,line=3,font=2)
}
mtext("Management Procedure",1,line=6.8,font=2,outer=T)
on.exit(par(op))
}
# #' Scatter plot of B/BMSY or B/B0 and F/FMSY for lastYrs
# #'
# #' Scatter plot of B/BMSY or B/B0 and F/FMSY for lastYrs
# #'
# #'
# #' @param MSEobj An object of class MSE
# #' @param MPs Optional subset by MP
# #' @param All Logical. Plot all points or just the mean?
# #' @param Var What to plot on the y-axis: \code{B_BMSY} or \code{SSB_SSB0}
# #' @param lastYrs Last number of years in projection to calculate statistics
# #' @param Fref Location of F statistic reference line
# #' @param BMSYref Location of B_MSY statistic reference line
# #' @param B0ref Location of B_0 statistic reference line
# #' @param cex.MP size of MP label
# #' @param Fbg Logical. Include background colours for F-statistic?
# #' @param Bbg Logical. Include background colours for B-statistic?
# #' @param Props Logical. Display the proportion of points in each quadrant?
# #' @param TP Logical. Use transparent colours?
# #' @author A. Hordyk
# #' @export Splot
# Splot <- function(MSEobj = NULL, MPs = NA, All = TRUE, Var = c("B_BMSY",
# "SSB_SSB0"), lastYrs = 10, Fref = 1, BMSYref = 1, B0ref = 0.4, cex.MP = 1,
# Fbg = FALSE, Bbg = FALSE, Props = FALSE, TP = FALSE) {
#
# Var <- match.arg(Var)
# if (!any(is.na(MPs)))
# MSEobj <- Sub(MSEobj, MPs = MPs)
#
# nMPs <- MSEobj@nMPs
# nyrs <- MSEobj@proyears
# nsim <- MSEobj@nsim
#
# perf <- MPStats(MSEobj)
# if (lastYrs > MSEobj@proyears)
# lastYrs <- 10
# yrs <- (nyrs - lastYrs + 1):nyrs
#
# ord <- perf[[1]]
# MPord <- match(ord$MP, MSEobj@MPs)
# sims <- perf$BySim
# Nrow <- ceiling(sqrt(nMPs))
# Ncol <- ceiling(nMPs/Nrow)
# Yvar <- sims[["F_FMSY"]]
# Xvar <- sims[[Var]]
# YLim <- c(0, 2)
# XMax <- ceiling(min(max(apply(Xvar[, , yrs], c(1, 2), mean), na.rm = TRUE),
# 3)/0.5) * 0.5
# XLim <- c(0, XMax)
# YLab <- expression(F/F[MSY])
# XLab <- switch(Var, SSB_SSB0 = expression(B/B[0]), B_BMSY = expression(B/B[MSY]))
#
# Bref <- switch(Var, SSB_SSB0 = B0ref, B_BMSY = BMSYref)
#
# ColVec <- colorRampPalette(c("green", "orange", "red"))(nsim)
# op <- par(mfrow = c(Nrow, Ncol), mar = c(1, 1, 2, 0), oma = c(4, 5, 2, 1))
#
# pmat <- matrix(NA, nrow = Ncol, ncol = Nrow)
# pmat[1:nMPs] <- 1:nMPs
# pmat <- t(pmat)
#
#
# for (mm in seq_along(MPord)) {
# xx <- MPord[mm]
# if (!All)
# Xs <- apply(Xvar[, xx, yrs], 1, mean)
# if (!All)
# Ys <- apply(Yvar[, xx, yrs], 1, mean)
# if (All)
# Xs <- as.numeric(Xvar[, xx, yrs])
# if (All)
# Ys <- as.numeric(Yvar[, xx, yrs])
# colN <- ceiling((Xs/max(XLim)) * nsim) + 1
# colN[colN > nsim] <- nsim
# Cols <- ColVec[colN]
# if (TP)
# Cols <- makeTransparent(Cols, alpha = 95)
# plot(Xs, Ys, xlab = "", ylab = "", bty = "n", xlim = XLim, ylim = YLim,
# axes = FALSE, pch = 18, col = Cols)
#
# if (nrow(pmat) > 1) {
# if (mm %in% pmat[, 1]) {
# axis(side = 2, labels = TRUE, las = 1)
# } else axis(side = 2, labels = FALSE)
# if (mm %in% pmat[Nrow, ]) {
# axis(side = 1, labels = TRUE)
# } else axis(side = 1, labels = FALSE)
# nas <- apply(pmat, 1, sum)
# rr <- which.max(nas)
# nas2 <- apply(pmat, 2, sum)
# cc <- which(is.na(nas2))
# if (mm %in% pmat[rr, cc])
# axis(side = 1, labels = TRUE)
# } else {
# if (mm == 1)
# axis(side = 2, labels = TRUE, las = 1)
# if (mm != 1)
# axis(side = 2, labels = FALSE)
# axis(side = 1, labels = TRUE)
# }
#
# abline(v = Fref, lty = 1, col = makeTransparent("grey", 150))
# abline(h = Bref, lty = 1, col = makeTransparent("grey", 150))
# mtext(side = 3, MSEobj@MPs[xx], cex = cex.MP)
#
# # Background colours
# Alpha <- 30
# # polygons
# OutCol <- rgb(red = 255, green = 0, blue = 0, alpha = Alpha, names = NULL,
# maxColorValue = 255)
# vl <- Fref
# hl <- Bref
# if (Bbg)
# polygon(x = c(0, max(max(Xs), XLim[2]), max(max(Xs), XLim[2]),
# 0), y = c(0, 0, hl, hl), col = OutCol, border = NA)
# if (Fbg)
# polygon(x = c(vl, max(max(Xs), XLim[2]), max(max(Xs), XLim[2]),
# vl), y = c(0, 0, max(max(Ys), YLim[2]), max(max(Ys), YLim[2])),
# col = OutCol, border = NA)
#
# if (Props) {
# P1 <- paste0(round(sum(Xs <= Fref & Ys >= Bref)/length(Xs) *
# 100, 1), "%")
# P2 <- paste0(round(sum(Xs > Fref & Ys >= Bref)/length(Xs) *
# 100, 1), "%")
# P3 <- paste0(round(sum(Xs > Fref & Ys < Bref)/length(Xs) *
# 100, 1), "%")
# P4 <- paste0(round(sum(Xs <= Fref & Ys < Bref)/length(Xs) *
# 100, 1), "%")
# legend("topleft", P1, bty = "n", cex = 1.25)
# legend("topright", P2, bty = "n", cex = 1.25)
# legend("bottomright", P3, bty = "n", cex = 1.25)
# legend("bottomleft", P4, bty = "n", cex = 1.25)
# }
# }
# mtext(side = 1, outer = TRUE, XLab, cex = 1.25, line = 2.5)
# mtext(side = 2, outer = TRUE, YLab, cex = 1.25, line = 2)
# Years <- paste("Years", (MSEobj@proyears - lastYrs) + 1, "-", MSEobj@proyears,
# "(last", lastYrs, "years)")
# mtext(side = 3, Years, cex = 1.25, outer = TRUE)
# par(op)
# }
#
# #' Trade-off plots for an MSE object
# #'
# #' Three figures showing trade-offs between fishing mortality, biomass, and yield.
# #'
# #' @param MSEobj An object of class 'MSE'
# #' @param nam Name of the plot
# #' @author T. Carruthers & A. Hordyk
# #' @seealso \link{TradePlot} \link{PerformanceMetric}
# #' @describeIn Tplot Used in the plot method for MSE objects that shows trade-off between
# #' yield versus probability of overfishing and biomass levels (relative to BMSY).
# #' @export
# Tplot_old <- function(MSEobj, nam = NA) {
# old_par <- par(no.readonly = TRUE)
# on.exit(par(old_par))
#
# FMSYr <- quantile(MSEobj@F_FMSY, c(0.001, 0.9), na.rm = T)
# BMSYr <- quantile(MSEobj@SB_SBMSY, c(0.001, 0.975), na.rm = T)
#
# colsse <- rainbow(100, start = 0, end = 0.36)[1:100]
# colB <- rep(colsse[100], ceiling(BMSYr[2] * 100))
# colB[1:100] <- colsse
# colB <- makeTransparent(colB, 60)
# colsse <- rainbow(200, start = 0, end = 0.36)[200:1]
# colF <- rep(colsse[200], ceiling(FMSYr[2] * 100))
# colF[1:200] <- colsse
# colF <- makeTransparent(colF, 60)
#
# Yd <- rep(NA, MSEobj@nMPs)
# P10 <- rep(NA, MSEobj@nMPs)
# P50 <- rep(NA, MSEobj@nMPs)
# P100 <- rep(NA, MSEobj@nMPs)
# POF <- rep(NA, MSEobj@nMPs)
# yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
# RefYd <- MSEobj@OM$RefY
#
# for (mm in 1:MSEobj@nMPs) {
# Yd[mm] <- round(mean(apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd,
# na.rm = T) * 100, 1)
# # cbind(MSEobj@Catch[,mm,yind],unlist(MSEobj@OM$MSY))
# POF[mm] <- round(sum(MSEobj@F_FMSY[, mm, ] >= 1, na.rm = T)/prod(dim(MSEobj@F_FMSY[,
# mm, ]), na.rm = T) * 100, 1)
# P10[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
# mm, ])) * 100, 1)
# P50[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] <= 0.5, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
# mm, ])) * 100, 1)
# P100[mm] <- round(sum(MSEobj@B_BMSY[, mm, ] <= 1, na.rm = T)/prod(dim(MSEobj@B_BMSY[,
# mm, ])) * 100, 1)
# }
#
# # dev.new2(width=7,height=7)
# par(mfrow = c(2, 2), mar = c(5, 4, 1, 1), oma = c(0, 0, 2, 0))
#
# tradeoffplot(POF, Yd, "Prob. of overfishing (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
# tradeoffplot(P100, Yd, "Prob. biomass < BMSY (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
# tradeoffplot(P50, Yd, "Prob. biomass < 0.5BMSY (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
# tradeoffplot(P10, Yd, "Prob. biomass < 0.1BMSY (%)", "Relative yield",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 50, hl = 100)
#
# if (is.na(nam))
# mtext(deparse(substitute(MSEobj)), 3, outer = T, line = 0.3, font = 2)
# if (!is.na(nam) & !is.character(nam))
# mtext(MSEobj@Name, 3, outer = T, line = 0.3, font = 2)
# if (!is.na(nam) & is.character(nam))
# mtext(nam, 3, outer = T, line = 0.3, font = 2)
# return(invisible())
# }
# #' @describeIn Tplot Simpler plot that compares long-term yield (LTY:
# #' fraction of simulations getting over half FMSY yield in the last ten years
# #' of the projection), short-term yield (STY: fraction of simulations getting
# #' over half FMSY yield in the first ten years of the projection), variability
# #' in yield (VY: fraction of simulations where average annual variability in
# #' yield is less than 10 per cent) and biomass level (B10: the fraction of
# #' simulations in which biomass stays above 10 percent of BMSY).
# #' @export
# Tplot2_old <- function(MSEobj, nam = NA) {
# old_par <- par(no.readonly = TRUE)
# on.exit(par(old_par))
#
# LTY <- rep(NA, MSEobj@nMPs)
# STY <- rep(NA, MSEobj@nMPs)
# VY <- rep(NA, MSEobj@nMPs)
# B10 <- rep(NA, MSEobj@nMPs)
# yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
# ystart <- 1:5
# RefYd <- MSEobj@OM$RefY
# y1 <- 1:(MSEobj@proyears - 1)
# y2 <- 2:MSEobj@proyears
# for (mm in 1:MSEobj@nMPs) {
# LTY[mm] <- round(sum(MSEobj@Catch[, mm, yend]/RefYd > 0.5, na.rm = T)/(MSEobj@nsim *
# length(yend)), 3) * 100
# STY[mm] <- round(sum(MSEobj@Catch[, mm, ystart]/RefYd > 0.5, na.rm = T)/(MSEobj@nsim *
# length(ystart)), 3) * 100
# AAVY <- apply(((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])^2)^0.5,
# 1, mean, na.rm = T)/apply(MSEobj@Catch[, mm, y2], 1, mean, na.rm = T)
# VY[mm] <- round(sum(AAVY < 0.1, na.rm = T)/MSEobj@nsim, 3) * 100
# B10[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] > 0.1, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[,
# mm, ])), 3) * 100
# }
# par(mfrow = c(1, 2), mar = c(5, 4, 1, 1), oma = c(0, 0, 2, 0))
#
# tradeoffplot(STY, LTY, "P(Short term yield > 0.5 FMSY)", "P(Long term yield > 0.5 FMSY)",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 1, hl = 1)
# tradeoffplot(B10, VY, "P(Biomass > 0.1 BMSY)", "P(CV in yield < 0.1)",
# MSEobj@MPs[1:MSEobj@nMPs], vl = 1, hl = 1)
# if (is.na(nam))
# mtext(deparse(substitute(MSEobj)), 3, outer = T, line = 0.3, font = 2)
# if (!is.na(nam))
# mtext(MSEobj@Name, 3, outer = T, line = 0.3, font = 2)
# return(invisible())
# }
#
#
# #' @describeIn Tplot By default, trade-off plots among LTY, STY, and biomass level B50
# #' (fraction of simulations in which biomass stays above 50 percent of BMSY), and
# #' Average Annual Variability in Yield (AAVY).
#
# #' @export
# Tplot3_old <- function(MSEobj, ..., lims=c(0.2, 0.2, 0.8, 0.8)) {
# PMlist <- unlist(list(...))
# if(length(PMlist) == 0) PMlist <- c("LTY", "STY", "P50", "AAVY")
# if (class(PMlist) != 'character') stop("Must provide names of PM methods")
# # check
#
# for (X in seq_along(PMlist))
# if (!PMlist[X] %in% avail("PM")) stop(PMlist[X], " is not a valid PM method")
# if (length(PMlist)<2) stop("Must provided more than 1 PM method")
#
# runPM <- vector("list", length(PMlist))
# for (X in 1:length(PMlist)) runPM[[X]] <- eval(call(PMlist[X], MSEobj))
#
# PlotList <- combn(unique(PMlist), 2)
# lims <- rep(lims, 100)[1:length(PMlist)]
#
# n.col <- ceiling(sqrt(ncol(PlotList)))
# n.row <- ceiling(ncol(PlotList)/n.col)
#
# m <- matrix(1:(n.col*n.row), ncol=n.col, nrow=n.row, byrow=FALSE)
# xmin <- xmax <- ymin <- ymax <- x <- y <- Class <- label <- fontface <- NULL
# plots <- listout <- list()
# for (pp in 1:ncol(PlotList)) {
# yPM <- PlotList[1,pp]
# yvals <- runPM[[match(yPM, PMlist)]]@Mean
# ycap <- runPM[[match(yPM, PMlist)]]@Caption
# yname <- runPM[[match(yPM, PMlist)]]@Name
# yline <- lims[match(yPM, PMlist)]
#
# xPM <- PlotList[2,pp]
# xvals <- runPM[[match(xPM, PMlist)]]@Mean
# xcap <- runPM[[match(xPM, PMlist)]]@Caption
# xname <- runPM[[match(xPM, PMlist)]]@Name
# xline <- lims[match(xPM, PMlist)]
#
# xlim <- c(0, max(max(xvals, 1)))
# ylim <- c(0, max(max(yvals, 1)))
#
# xrect <- data.frame(xmin=0, xmax=xline, ymin=0, ymax=max(ylim))
# yrect <- data.frame(xmin=0, xmax=max(xlim), ymin=0, ymax=yline)
#
# MPType <- MPtype(MSEobj@MPs)
# Class <- MPType[match(MSEobj@MPs, MPType[,1]),2]
#
# df <- data.frame(x=xvals, y=yvals, label=MSEobj@MPs, Class=Class,
# pass=xvals>xline & yvals>yline, fontface="plain", xPM=xPM, yPM=yPM)
# df$fontface <- as.character(df$fontface)
# df$fontface[!df$pass] <- "italic"
# df$fontface <- factor(df$fontface)
# listout[[pp]] <- df
# plots[[pp]] <- ggplot2::ggplot() +
# ggplot2::geom_rect(data=xrect, ggplot2::aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax), fill='gray80', alpha=0.4) +
# ggplot2::geom_rect(data=yrect, ggplot2::aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax), fill='gray80', alpha=0.4)
#
# # plots[[pp]] <- ggplot(df, aes(x, y, shape=Class, color=Class, label=label)) +
# plots[[pp]] <- plots[[pp]] +
# ggplot2::geom_point(data=df, ggplot2::aes(x, y, shape=Class, color=Class), size=2) +
# ggrepel::geom_text_repel(data=df, ggplot2::aes(x, y, color=Class, label=label, fontface = fontface), show.legend=FALSE) +
# ggplot2::xlab(xcap) + ggplot2::ylab(ycap) +
# ggplot2::xlim(xlim) + ggplot2::ylim(ylim) +
# ggplot2::theme_classic() +
# ggplot2::theme(axis.title.x = ggplot2::element_text(size=11),
# axis.title.y = ggplot2::element_text(size=11),
# legend.text=ggplot2::element_text(size=12)) +
# ggplot2::labs(shape= "MP Type", color="MP Type")
#
# }
# out <- do.call("rbind", listout)
# tab <- table(out$label, out$pass)
# passall <- rownames(tab)[tab[,ncol(tab)] == ncol(PlotList)]
# tt <- summary(MSEobj, PMlist, silent=TRUE)
# tt$Satisificed <- FALSE
# tt$Satisificed[match(passall, tt$MP)] <- TRUE
#
# join_plots(plots, n.col, n.row)
# tt
#
# }
#
#
#
# #' Generic Trade-off Plot
# #'
# #' Creates a trade-off plot (up to four panels) of built-in performance
# #' metrics.
# #'
# #' Returns a list containing the names of performance metrics that meet the
# #' minimum performance metrics for each trade-off, and ranks the MPs by
# #' increasing distance from the top-right corner.
# #'
# #' @param MSEobj Object of class MSE, output of the runMSE function
# #' @param XAxis Character string describing the performance metrics for the
# #' x-axis (or x-axes if vector; max 4). Must be chosen for list of existing PMs
# #' and same length as YAxis. See \code{PMs}
# #' @param YAxis Character string describing the performance metrics for the
# #' y-axis (or y-axes if vector; max 4). Must be chosen for list of existing PMs
# #' and same length as XAxis. See \code{PMs}
# #' @param XThresh Minimum threshold values in percent (i.e., 50 = 50\%) for the
# #' x-axes (must be same length as XAxis)
# #' @param YThresh Minimum threshold values in percent (i.e., 50 = 50\%) for the
# #' y-axes (must be same length as YAxis)
# #' @param maxVar Reference for average annual variability in yield in percent
# #' @param BmsyRef Reference level of BMSY, in proportion, i.e., 0.5 = 0.5BMSY
# #' @param B0Ref Reference level of B0, in proportion, i.e., 0.2 = 0.2B0
# #' @param AvailMPs vector of MPs that *could* be applied to the fishery, i.e.,
# #' sufficient data exists. These a plotted with different symbol
# #' @param ShowLabs Logical to specify if MP labels are shown
# #' @param ShowCols Logical to specify if background colors are shown
# #' @author A. Hordyk
# #' @export
# TradePlot_old <- function(MSEobj, XAxis = c("Overfishing", "Biomass:BMSY"),
# YAxis = c("Long-term Yield", "AnnualVar"), XThresh = c(30, 80),
# YThresh = c(0, 50), maxVar = 15, BmsyRef = 0.5, B0Ref = 0.2,
# AvailMPs = NULL, ShowLabs = FALSE, ShowCols = TRUE) {
# PMs <- c("Long-term Yield", "Short-term Yield", "Overfishing", "Biomass:BMSY",
# "Biomass:B0", "AnnualVar")
# op <- par(no.readonly=TRUE)
# on.exit(par(op))
# # Error Checks
# if (prod(XAxis %in% PMs) != 1) {
# message("Available Performance Metrics")
# print(PMs)
# stop("Invalid XAxis Performance Metrics")
# }
# if (prod(YAxis %in% PMs) != 1) {
# message("Available Performance Metrics")
# print(PMs)
# stop("Invalid YAxis Performance Metrics")
# }
# if (length(XAxis) > 4)
# stop("Too many Performance Metrics (max 4)")
# if (length(YAxis) > 4)
# stop("Too many Performance Metrics (max 4)")
# if (length(XAxis) != length(YAxis))
# stop("XAxis must be of same length as YAxis")
# if (length(XThresh) != length(XAxis) | length(YThresh) != length(XAxis))
# warning("Risk Threshold not same length as number of PMs")
#
# Yd <- rep(NA, MSEobj@nMPs)
# BMSYref <- rep(NA, MSEobj@nMPs)
# B0ref <- rep(NA, MSEobj@nMPs)
# PNOF <- rep(NA, MSEobj@nMPs)
# LTY <- rep(NA, MSEobj@nMPs)
# STY <- rep(NA, MSEobj@nMPs)
# VY <- rep(NA, MSEobj@nMPs)
#
# y1 <- 1:(MSEobj@proyears - 1)
# y2 <- 2:MSEobj@proyears
#
# ystart <- 1:5
# yend <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
#
# RefYd <- MSEobj@OM$RefY
# if (maxVar < 1)
# maxVar <- maxVar * 100
#
# for (mm in 1:MSEobj@nMPs) {
# PNOF[mm] <- round(sum(MSEobj@F_FMSY[, mm, ] <= 1, na.rm = T)/prod(dim(MSEobj@F_FMSY[,
# mm, ]), na.rm = T) * 100, 1)
# BMSYref[mm] <- round(sum(MSEobj@SB_SBMSY[, mm, ] > BmsyRef, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[, mm, ])) * 100, 1)
# B0ref[mm] <- round(sum((MSEobj@SB_SBMSY[, mm, ] * MSEobj@OM$SSBMSY_SSB0) >
# B0Ref, na.rm = T)/prod(dim(MSEobj@SB_SBMSY[, mm, ])) * 100, 1)
# # LTY[mm]<-round(sum(MSEobj@Catch[,mm,yend]/RefYd>0.5,na.rm=T)/(MSEobj@nsim*length(yend)),3)*100
# # STY[mm]<-round(sum(MSEobj@Catch[,mm,ystart]/RefYd>0.5,na.rm=T)/(MSEobj@nsim*length(ystart)),3)*100
# LTY[mm] <- round(mean(apply(MSEobj@Catch[, mm, yend], 1, mean, na.rm = T)/RefYd,
# na.rm = T) * 100, 1)
# STY[mm] <- round(mean(apply(MSEobj@Catch[, mm, ystart], 1, mean, na.rm = T)/RefYd,
# na.rm = T) * 100, 1)
# AAVY <- apply((((MSEobj@Catch[, mm, y1] - MSEobj@Catch[, mm, y2])/MSEobj@Catch[,
# mm, y2])^2)^0.5, 1, mean, na.rm = T)
# VY[mm] <- round(sum(AAVY < (maxVar/100), na.rm = T)/MSEobj@nsim,
# 3) * 100
# }
#
# for (xx in seq_along(XAxis)) {
# name <- paste0("X", xx)
# name1 <- paste0("XLab", xx)
# assign(name, GetStat(XAxis[xx], LTY, STY, PNOF, BMSYref, B0ref,
# VY))
# assign(name1, StatLab(XAxis[xx], maxVar, BmsyRef, B0Ref))
# name <- paste0("Y", xx)
# name1 <- paste0("YLab", xx)
# assign(name, GetStat(YAxis[xx], LTY, STY, PNOF, BMSYref, B0ref,
# VY))
# assign(name1, StatLab(YAxis[xx], maxVar, BmsyRef, B0Ref))
# }
#
# Nplot <- length(XAxis)
# if (Nplot == 1)
# par(mfrow = c(1, 1), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
# if (Nplot == 2)
# par(mfrow = c(1, 2), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
# if (Nplot == 3)
# par(mfrow = c(1, 3), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
# if (Nplot == 4)
# par(mfrow = c(2, 2), mar = c(4, 4.5, 1, 1), oma = c(1, 1, 0, 0))
#
# OutList <- list()
# for (xx in seq_along(XAxis)) {
# Xname <- paste0("X", xx)
# XLab <- paste0("XLab", xx)
# Yname <- paste0("Y", xx)
# YLab <- paste0("YLab", xx)
# rr <- tradeoffplot4(x = get(Xname), y = get(Yname), get(XLab),
# get(YLab), labs = MSEobj@MPs[1:MSEobj@nMPs], vl = XThresh[xx],
# hl = YThresh[xx], ShowLabs = ShowLabs, ShowCols = ShowCols,
# AvailMPs = AvailMPs)
#
# labs <- MSEobj@MPs[1:MSEobj@nMPs]
# ind <- which(labs %in% rr)
# tempDF <- data.frame(MP = rr, X = get(Xname)[ind], Y = get(Yname)[ind])
# Dist <- NULL # calculate distance from corner
# for (X in 1:length(tempDF[, 2])) Dist[X] <- euc.dist(c(tempDF[X,
# 2], tempDF[X, 3]), c(100, 100))
# tempDF <- tempDF[order(Dist), ]
# rownames(tempDF) <- 1:nrow(tempDF)
# OutList[[xx]] <- tempDF
# }
#
#
# OutList
#
# }
#' Calculate Value Of Information
#'
#' A function that relates operating model parameters and parameters of the
#' observation model to yield (by default). A user can also specific their own
#' utility values (Ut) which is arranged in a matrix of nsim rows and nMP
#' columns.
#'
#'
#' @param MSEobj An object of class MSE
#' @param ncomp Maximum number of variables to examine per MP
#' @param nbins Number of percentile bins for sampled parameters of the
#' operating model or observation model, which is used for calculating
#' variability in utility across the sampled range of each parameter
#' @param maxrow maximum number of MPs per plot
#' @param Ut A matrix of user-specified utility values of nsim rows and nMPs
#' columns
#' @param Utnam The name of the utility measure for plotting
#' @param plot Logical. Show the plot?
#' @author T. Carruthers
#' @export VOI
VOI <- function(MSEobj, ncomp = 6, nbins = 8, maxrow = 8, Ut = NA, Utnam = "Utility", plot=TRUE) {
objnam <- deparse(substitute(MSEobj))
nsim <- MSEobj@nsim
if (is.na(Ut[1])) {
Ut <- array(NA, c(nsim, MSEobj@nMPs))
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
Ut[, mm] <- apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd * 100
# POF[,mm]<-apply(MSEobj@F_FMSY[,mm,]>1,1,sum)/MSEobj@proyears
# P10[,mm]<-apply(MSEobj@B_BMSY[,mm,]<0.1,1,sum)/MSEobj@proyears
}
Utnam <- "Long-term yield relative to MSY (%)"
}
MPs <- MSEobj@MPs
nMPs <- MSEobj@nMPs
onlycor <- c("RefY", "A", "MSY", "Linf", "t0", "OFLreal", "Spat_targ")
vargood <- (apply(MSEobj@OM, 2, sd)/(apply(MSEobj@OM, 2, mean)^2)^0.5) > 0.005
# MSEobj@OM<- MSEobj@OM[,(!names(MSEobj@OM)%in%onlycor)&vargood]
vargood[grep("qvar", names(MSEobj@OM))] <- FALSE
MSEobj@OM <- MSEobj@OM[, which((!names(MSEobj@OM) %in% onlycor) & vargood)]
OMp <- apply(MSEobj@OM, 2, quantile, p = seq(0, 1, length.out = nbins + 1), na.rm = TRUE)
Obsp <- apply(MSEobj@Obs, 2, quantile, p = seq(0, 1, length.out = nbins + 1), na.rm = TRUE)
OMv <- array(NA, c(nMPs, ncol(MSEobj@OM), nbins))
Obsv <- array(NA, c(nMPs, ncol(MSEobj@Obs), nbins))
for (mm in 1:nMPs) {
for (j in 1:nbins) {
for (i in 1:ncol(MSEobj@OM)) {
cond <- MSEobj@OM[, i] > OMp[j, i] & MSEobj@OM[, i] < OMp[j + 1, i]
OMv[mm, i, j] <- mean(Ut[cond, mm], na.rm = T)
}
for (i in 1:ncol(MSEobj@Obs)) {
cond <- MSEobj@Obs[, i] > Obsp[j, i] & MSEobj@Obs[, i] < Obsp[j + 1, i]
Obsv[mm, i, j] <- mean(Ut[cond, mm], na.rm = T)
}
}
}
# cbind(names(MSEobj@OM), OMs[mm,])
# -- Operating model variables
OMs <- apply(OMv, 1:2, sd, na.rm = T)
OMstr <- array("", c(nMPs * 2, ncomp + 1))
for (mm in 1:nMPs) {
ind <- order(OMs[mm, ], decreasing = T)[1:ncomp]
OMstr[1 + (mm - 1) * 2, 1] <- MPs[mm]
OMstr[1 + (mm - 1) * 2, 2:(1 + ncomp)] <- names(MSEobj@OM[ind])
OMstr[2 + (mm - 1) * 2, 2:(1 + ncomp)] <- round(OMs[mm, ind], 2)
}
OMstr <- data.frame(OMstr)
names(OMstr) <- c("MP", 1:ncomp)
# -- Observation model variables
slots <- c("Cat", "Cat", "AvC", "AvC", "CAA", "CAA", "CAL", "CAL",
"Ind", "Dep", "Dep", "Dt", "Dt", "Mort", "FMSY_M", "SSBMSY_SSB0", "L50",
"L95", "LFC", "LFS", "Abun", "Abun", "vbK", "vbt0", "vbLinf", "Steep",
"Iref", "Cref", "Bref", "ML")
Obsnam <- c("Cbias", "Csd", "Cbias", "Csd", "CAA_nsamp", "CAA_ESS",
"CAL_nsamp", "CAL_ESS", "Isd", "Dbias", "Derr", "Dbias", "Derr",
"Mbias", "FMSY_Mbias", "BMSY_B0bias", "lenMbias", "lenMbias", "LFCbias",
"LFSbias", "Abias", "Aerr", "Kbias", "t0bias", "Linfbias", "hbias",
"Irefbias", "Crefbias", "Brefbias", "")
Obss <- apply(Obsv, 1:2, sd, na.rm = T)
Obsstr <- array("", c(nMPs * 2, ncomp + 1))
for (mm in 1:nMPs) {
relobs <- Obsnam[slots %in% unlist(strsplit(Required(MPs[mm])[,
2], split = ", "))]
ind <- (1:ncol(MSEobj@Obs))[match(relobs, names(MSEobj@Obs))]
pos <- names(MSEobj@Obs)[ind] # possible observation processes
maxy <- min(max(1, length(pos)), ncomp, na.rm = T)
ind2 <- order(Obss[mm, ind], decreasing = T)[1:maxy]
Obsstr[1 + (mm - 1) * 2, 1] <- MPs[mm]
Obsstr[1 + (mm - 1) * 2, 2:(1 + maxy)] <- pos[ind2]
Obsstr[2 + (mm - 1) * 2, 2:(1 + maxy)] <- round(Obss[mm, ind][ind2],
2)
}
Obsstr <- data.frame(Obsstr)
names(Obsstr) <- c("MP", 1:ncomp)
ncols <- 40
# colsse<-makeTransparent(rainbow(ncols,start=0,end=0.36),95)[ncols:1]
colsse <- makeTransparent(rainbow(ncols, start = 0, end = 0.36), 90)[ncols:1]
minsd <- 0
maxsd <- max(OMs, na.rm = T)
coly <- ceiling(OMs/maxsd * ncols)
# Operating model variables
mbyp <- split(1:nMPs, ceiling(1:nMPs/maxrow))
ylimy = c(0, max(OMv, na.rm = T) * 1.2)
if (plot) {
for (pp in 1:length(mbyp)) {
op <- par(mfrow = c(length(mbyp[[pp]]), ncomp), mai = c(0.15, 0.1, 0.15,
0.05), omi = c(0.1, 0.9, 0.3, 0.05))
for (mm in mbyp[[pp]]) {
for (cc in 1:ncomp) {
rind <- (mm - 1) * 2 + 1
y <- Ut[, mm]
cind <- match(OMstr[rind, 1 + cc], names(MSEobj@OM))
x <- MSEobj@OM[, cind]
plot(x, y, col = "white", axes = F, ylim = ylimy)
axis(1, pretty(OMp[, cind]), pretty(OMp[, cind]), cex.axis = 0.8,
padj = -1.5)
abline(v = OMp[, cind], col = "#99999960")
points(x, y, col = colsse[coly[mm, cind]], pch = 19, cex = 0.8)
x2 <- (OMp[1:nbins, cind] + OMp[2:(nbins + 1), cind])/2
y2 <- OMv[mm, cind, ]
lines(x2, y2)
legend("bottomright", legend = round(OMs[mm, cind], 2), bty = "n", cex = 0.8)
legend("topleft", legend = OMstr[rind, 1 + cc], bty = "n", cex = 0.85)
if (cc == 1) {
mtext(MPs[mm], 2, font = 2, outer = F, cex = 0.8, line = 2)
ytick <- pretty(seq(ylimy[1], ylimy[2] * 1.3, length.out = 10))
axis(2, ytick, ytick, cex.axis = 0.8)
} # only first column
} # parameters (columns)
} # MPs (rows)
mtext(Utnam, 2, outer = T, cex = 0.9, line = 3.5)
mtext(paste("Operating model parameters: ", objnam, "@OM", sep = ""), 3, outer = T, font = 2, cex = 0.9)
} # Plots
# Observation model values
ylimy = c(0, max(Obsv, na.rm = T) * 1.2)
minsd <- 0
maxsd <- max(Obss)
coly <- ceiling(Obss/maxsd * ncols)
if (sum(is.na(Obsstr) | Obsstr == "") < (ncomp + 1) * nMPs * 2 - nMPs)
{
# only if there is data to plot
for (pp in 1:length(mbyp)) {
op <- par(mfrow = c(length(mbyp[[pp]]), ncomp), mai = c(0.15,
0.1, 0.15, 0.05), omi = c(0.1, 0.9, 0.3, 0.05),no.readonly=TRUE)
on.exit(par(op))
for (mm in mbyp[[pp]]) {
rind <- (mm - 1) * 2 + 1
npres <- sum(Obsstr[rind + 1, ] != "")
for (cc in 1:ncomp) {
if (!is.na(npres) & cc < (npres + 1)) {
y <- Ut[, mm]
cind <- match(Obsstr[rind, 1 + cc], names(MSEobj@Obs))
x <- MSEobj@Obs[, cind]
plot(x, y, col = "white", axes = F, ylim = ylimy)
axis(1, pretty(Obsp[, cind]), pretty(Obsp[, cind]), cex.axis = 0.8, padj = -2)
abline(v = Obsp[, cind], col = "#99999960")
points(x, y, col = colsse[coly[mm, cind]], pch = 19, cex = 0.8)
x2 <- (Obsp[1:nbins, cind] + Obsp[2:(nbins + 1), cind])/2
y2 <- Obsv[mm, cind, ]
lines(x2, y2)
legend("bottomright", legend = round(Obss[mm, cind], 2), bty = "n", cex = 0.8)
legend("topleft", legend = Obsstr[rind, 1 + cc], bty = "n", cex = 0.75)
if (cc == 1) {
mtext(MPs[mm], 2, font = 2, outer = F, cex = 0.6, line = 2)
ytick <- pretty(seq(ylimy[1], ylimy[2] * 1.3, length.out = 10))
axis(2, ytick, ytick, cex.axis = 0.8)
} # only first column
} else {
plot(0, type = "n", axes = FALSE, ann = FALSE)
if (cc == 1) {
mtext(MPs[mm], 2, font = 2, outer = F, cex = 0.6, line = 2)
} # only first column
}
} # parameters (columns)
} # MPs (rows)
mtext(Utnam, 2, outer = T, cex = 0.9, line = 3.5)
mtext(paste("Observation model parameters: ", objnam, "@Obs", sep = ""), 3, outer = T, font = 2, cex = 0.9)
} # Plots
} # if there is data to plot
}
list(OMstr, Obsstr)
} # VOI
#' Calculate Value Of Information 2
#'
#' A function that relates operating model parameters and parameters of the
#' observation model to relative yield (yield over last 5 years of projection
#' relative to a 'best F' scenario that maximizes yield).
#'
#' @param MSEobj An object of class MSE
#' @param ncomp Maximum number of observation variables to examine per MP
#' @param nbins Number of bins for sampled observation variables used for
#' calculating variability in utility across the sampled range of each
#' parameter
#' @param Ut A matrix of user-specified utility values of nsim rows and nMPs
#' columns
#' @param Utnam The name of the utility measure for plotting
#' @param lay Controls whether labels are in lay terms or not
#' @note VOI2 assumes that relative cost for each type of improvement in data
#' is linearly related to the number of samples (e.g. nCAAobs) or square
#' function of improved precision and bias e.g.: relative cost=
#' 1/(newCV/oldCV)^2
#' @author T. Carruthers
#' @export VOI2
VOI2 <- function(MSEobj, ncomp = 6, nbins = 4, Ut = NA, Utnam = "yield",
lay = F) {
op <- par(no.readonly=TRUE)
on.exit(par(op))
objnam <- deparse(substitute(MSEobj))
nsim <- MSEobj@nsim
if (is.na(Ut[1])) {
Ut <- array(NA, c(nsim, MSEobj@nMPs))
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (mm in 1:MSEobj@nMPs) {
Ut[, mm] <- apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd *
100
# POF[,mm]<-apply(MSEobj@F_FMSY[,mm,]>1,1,sum)/MSEobj@proyears
# P10[,mm]<-apply(MSEobj@B_BMSY[,mm,]<0.1,1,sum)/MSEobj@proyears
}
}
MPs <- MSEobj@MPs
nMPs <- MSEobj@nMPs
# -- Observation model variables
slots <- c("Cat", "Cat", "AvC", "AvC", "CAA", "CAA", "CAL", "CAL",
"Ind", "Ind", "Dep", "Dep", "Dt", "Dt", "Mort", "FMSY_M", "SSBMSY_SSB0",
"L50", "L95", "LFC", "LFS", "Abun", "Abun", "vbK", "vbt0", "vbLinf",
"Steep", "Iref", "Cref", "Bref")
Obsnam <- c("Cbias", "Csd", "Cbias", "Csd", "CAA_nsamp", "CAA_ESS",
"CAL_nsamp", "CAL_ESS", "Isd", "betas", "Dbias", "Derr", "Dbias",
"Derr", "Mbias", "FMSY_Mbias", "BMSY_B0bias", "lenMbias", "lenMbias",
"LFCbias", "LFSbias", "Abias", "Aerr", "Kbias", "t0bias", "Linfbias",
"hbias", "Irefbias", "Crefbias", "Brefbias")
Obsnam2 <- c("Cbias", "Csd", "CAA_nsamp", "CAA_ESS", "CAL_nsamp", "CAL_ESS",
"Isd", "betas", "Dbias", "Derr", "Mbias", "FMSY_Mbias", "BMSY_B0bias",
"lenMbias", "LFCbias", "LFSbias", "Abias", "Aerr", "Kbias", "t0bias",
"Linfbias", "hbias", "Irefbias", "Crefbias", "Brefbias")
Obsnam3 <- c("Catch bias", "Catch error", "n CAA samples", "CAA ESS",
"n CAL samples", "CAL ESS", "Abun. Ind. error", "Hyperstability",
"Depln. bias", "Depln. error", "M bias", "FMSY/M bias", "BMSY/B0 bias",
"lenMbias", "Len 1st Cap bias", "Len full sel bias", "Cur Abund bias",
"Cur Abun err", "vB K bias", "vB t0 bias", "vB Linf bias", "Steepness bias",
"Ref index bias", "Ref catch bias", "Ref biomass bias")
# Types of observation error model 1:lognorm 2:percentile 3:replicates
# (higher is better) ##4:uniform on log 5:logit space
oem <- c(1, 2, 3, 3, 3, 3, 2, 4, 1, 2, 1, 1, 1, 1, 1, 1, 4, 2, 1, 1,
1, 2, 1, 1, 1)
# oem<-c( 2, 2, 3, 3, 3, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2,
# 2, 2, 1, 1, 1)
Obsd <- apply(MSEobj@Obs, 2, sd, na.rm = TRUE)
Obm <- apply(MSEobj@Obs, 2, mean, na.rm = TRUE)
Obmd <- apply(MSEobj@Obs, 2, quantile, p = 0.5, na.rm = TRUE)
maxcomp <- length(Obsnam2)
Obsv <- array(NA, c(nMPs, maxcomp, nbins))
Obsval <- array(NA, c(nMPs, maxcomp, nbins))
Obscost <- array(NA, c(nMPs, maxcomp, nbins))
Obsname <- list()
div <- seq(1, 2, length.out = nbins + 1)[2:(nbins + 1)] # for distributions
percs <- seq(0.5, 1, length.out = nbins + 1)[1:nbins] # for samples
percsCAA <- seq(0, 1, length.out = nbins + 2)[2:(nbins + 1)]
percUL <- seq(0, 0.25, length.out = nbins + 1)[2:(nbins + 1)]
percUU <- 1 - percUL
for (mm in 1:nMPs) {
Y1 <- Ut[, mm]
relobs <- Obsnam[slots %in% unlist(strsplit(Required(MPs[mm])[,
2], split = ", "))]
Obsname[[mm]] <- relobs
nr <- length(relobs)
if (length(relobs) > 0)
{
for (r in 1:nr) {
oemi <- match(relobs[r], Obsnam2)
obsi <- match(relobs[r], names(MSEobj@Obs))
for (cc in 1:nbins) {
if (oem[oemi] == 1) {
# Redundant SIR code for log-normal biases
T1 <- tdlnorm(MSEobj@Obs[, obsi], Obm[obsi], Obsd[obsi]/Obm[obsi])
# plot(MSEobj@Obs[,obsi],T1) # check
T2 <- tdlnorm(MSEobj@Obs[, obsi], Obm[obsi], Obsd[obsi]/(div[cc] *
Obm[obsi]))
W <- T2/T1
nrep2 <- nsim * 20
Y2 <- sample(Y1, nrep2 * 5, replace = T, prob = W)
Obsv[mm, r, cc] <- (mean(Y2) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- Obsd[obsi]/(div[cc] * Obm[obsi])
Obscost[mm, r, cc] <- div[cc]^2
} else if (oem[oemi] == 2) {
refval <- quantile(MSEobj@Obs[, obsi], percs[nbins:1][cc],
na.rm = TRUE)
ind <- MSEobj@Obs[, obsi] < refval
Obsv[mm, r, cc] <- (mean(Y1[ind]) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- mean(MSEobj@Obs[ind, obsi])
Obscost[mm, r, cc] <- 1/(Obsval[mm, r, cc]/mean(MSEobj@Obs[,
obsi]))^2
} else if (oem[oemi] == 3) {
refval <- quantile(MSEobj@Obs[, obsi], percsCAA[cc],
na.rm = TRUE)
ind <- MSEobj@Obs[, obsi] > refval
Obsv[mm, r, cc] <- (mean(Y1[ind]) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- mean(MSEobj@Obs[ind, obsi])
Obscost[mm, r, cc] <- Obsval[mm, r, cc]
} else if (oem[oemi] == 4) {
refval <- quantile(MSEobj@Obs[, obsi], percUL[cc],
na.rm = TRUE)
refval2 <- quantile(MSEobj@Obs[, obsi], percUU[cc],
na.rm = TRUE)
ind <- (MSEobj@Obs[, obsi] > refval) & (MSEobj@Obs[,
obsi] < refval2)
Obsv[mm, r, cc] <- (mean(Y1[ind]) - mean(Y1))/mean(Y1) *
100
Obsval[mm, r, cc] <- sd(MSEobj@Obs[ind, obsi])
Obscost[mm, r, cc] <- 1/(Obsval[mm, r, cc]/sd(MSEobj@Obs[,
obsi]))^2
}
# observation model type
} # loop over bins
} # loop over r
} # observation variables?
} # loop over MPs
cb <- array(NA, c(MSEobj@nMPs, maxcomp))
for (mm in 1:MSEobj@nMPs) {
if (sum(!is.na(Obscost[mm, , ])) > 0) {
for (r in 1:length(Obsname[[mm]])) {
dat <- data.frame(x = Obscost[mm, r, ], y = Obsv[mm, r,
])
if (prod(apply(dat, 2, is.finite)) > 0) {
# plot(dat$x,dat$y)
cb[mm, r] <- lm(y ~ x - 1, data = dat)$coefficients[1]
}
}
}
}
ncols <- 100
# colsse<-makeTransparent(rainbow(ncols,start=0,end=0.36),95)[ncols:1]
colt <- rainbow(ncols, start = 0, end = 0.36)[1:ncols]
colsse <- makeTransparent(colt, 98)
cb[cb < 0 | is.na(cb)] <- 0
coly <- ceiling((cb/max(cb, na.rm = T))^0.5 * ncols)
coly[coly == 0] <- 1
ncol <- ceiling(MSEobj@nMPs^0.5)
nrow <- ceiling(MSEobj@nMPs/ncol)
op <- par(mfrow = c(nrow, ncol), mar = c(2.4, 2.4, 0.1, 0.1), omi = c(0.4,
0.35, 0.3, 0))
gcol1 <- "#99999960"
gcol2 <- "#99999940"
gcol3 <- "#99999920"
for (mm in 1:MSEobj@nMPs) {
if (sum(!is.na(Obscost[mm, , ])) > 0)
{
plot(c(1, 5), range(Obsv, na.rm = T), col = "white", main = "")
legend("topleft", legend = MSEobj@MPs[mm], bty = "n", text.font = 2,
cex = 1.4)
abline(h = (-20:50) * 4, col = gcol2, lwd = 1.5)
abline(h = (-20:50) * 4 + 2, col = gcol3, lwd = 1)
abline(v = 1:4, col = gcol2, lwd = 1.5)
abline(v = (1:4) + 0.5, col = gcol3, lwd = 1)
abline(h = 0, col = gcol1, lwd = 3)
no <- length(Obsname[[mm]])
ind <- order(cb[mm, 1:no], decreasing = T)[1:ncomp]
ind <- ind[!is.na(ind)]
ind2 <- order(Obsv[mm, 1:no, nbins])[1:ncomp]
ind2 <- ind2[!is.na(ind2)]
lpos <- Obsv[mm, ind2, nbins]
ppos <- seq(min(Obsv, na.rm = T), max(Obsv, na.rm = T),
length.out = length(ind2))
wt <- (max(Obsv[mm, 1:no, nbins], na.rm = T) - min(Obsv[mm,
1:no, nbins], na.rm = T))/(max(Obsv, na.rm = T) - min(Obsv,
na.rm = T))/(no/ncomp)
wt <- wt^0.66
nupos <- wt * lpos + (1 - wt) * ppos
for (r2 in 1:length(ind)) {
r <- ind2[r2]
lines(c(1, Obscost[mm, r, ]), c(0, Obsv[mm, r, ]), col = colsse[coly[mm,
r]], lwd = 3)
if (!lay) {
text(4.5, nupos[r2], Obsname[[mm]][r], col = colt[coly[mm,
r]], font = 2, cex = 1.2)
} else {
text(4.5, nupos[r2], Obsnam3[match(Obsname[[mm]][r],
Obsnam2)], col = colt[coly[mm, r]], font = 2, cex = 1.2)
}
} # observation quantities (lines)
# legend('topleft',legend=Obsname[[mm]][ind],text.col=colt[coly[mm,ind]],text.font=2,cex=1.2,bty='n')
} # if there is data to plot
} # MPs (plots)
mtext("Cost relative to today", 1, outer = T, cex = 0.9, line = 1,
font = 2)
# mtext(paste('Operating model parameters:
# ',objnam,'@OM',sep=''),3,outer=T,font=2,cex=0.9)
mtext(paste("% Change in ", Utnam, " relative to today", sep = ""),
2, outer = T, line = 0.6, font = 2, cex = 0.9)
par(op)
invisible(list(Obscost, Obsv, Obsval, cb, Obsname, MSEobj@MPs))
} # VOI2
#' Yet another Value of Information Plot
#'
#' A function that relates parameters of the observation model and the
#' operating model parameters to yield.
#'
#'
#' @param MSEobj An object of class MSE
#' @param MPs The MPs to plot. If NA it will plot the first nMP from MSEobj
#' @param nvars The number of observation or operating model parameters to plot
#' (number of columns)
#' @param nMP The maximum number of MPs to plot (number of rows)
#' @param Par Plot Operating Model (OM) or Observation (Obs) parameters?
#' @param YVar Variable for Y-Axis: Yield (Y) or Biomass (B) (relative to BMSY)
#' @param doPlot Output the plot?
#' @param incStat Include a print out of statistic describing the curviness of
#' the line?
#' @param availMP Optional character string of MPs that are available. These
#' names are colored black
#' @param acceptMP Optional character string of MPs that are acceptable. These
#' names are colored green if they are also in availMP
#' @param incNames Include the names?
#' @param labcex Character size of the label
#' @param quants Quantiles to calculate
#' @return A list of all the information included in the plot
#' @author A. Hordyk
#' @export VOIplot
VOIplot <- function(MSEobj, MPs = NA, nvars = 5, nMP = 4,
Par = c("Obs", "OM"), YVar = c("Y", "B"), doPlot = TRUE, incStat = FALSE,
availMP = NULL, acceptMP = NULL, incNames = TRUE, labcex = 0.8,
quants = c(0.05, 0.95)) {
YVar <- match.arg(YVar)
nvars <- max(nvars, 2) # maximum number of variables
Par <- match.arg(Par) # Operating Model or Observation
nMPs <- MSEobj@nMPs # Number of MPs
# Subset to specified MPs
if (any(is.na(MPs))) MPs <- MSEobj@MPs
if (methods::is(MPs, "numeric") | methods::is(MPs, "integer")) MPs <- MSEobj@MPs[MPs]
if (length(MPs) < 1) stop("No MPs found")
nMPss <- length(MPs)
if (nMP > nMPs) nMP <- nMPs
if (!all(MSEobj@MPs %in% MPs)) {
mse <- Sub(MSEobj, MPs = MPs)
nMPs <- mse@nMPs
} else {
mse <- MSEobj
}
# Calculate MSE sensitivities per MP
if (length(MPs) > 1) senseDat <- sapply(1:nMPs, calcMSESense, MSEobj = mse,
YVar = YVar, Par = Par,
simplify = FALSE, quants = quants)
if (length(MPs) == 1) senseDat <- calcMSESense(MP = MPs, MSEobj = mse,
YVar = YVar, Par = Par,
quants = quants)
# Operating Model or Observation Statistics
if (nMPs == 1) {
varNames <- senseDat$OMNames
} else varNames <- senseDat[[1]]$OMNames
used <- matrix(FALSE, nrow = length(varNames), ncol = nMPs)
if (Par == "OM") {
used <- matrix(TRUE, nrow = length(varNames), ncol = nMPs) # all OM parameters used
Obsnam <- varNames
LnName <- varNames
# LnName <- c("Reference yield", "Natural mortality", "Depletion", "Abundance",
# "BMSY/B0", "FMSY/M", "M gradient", "Inter-annual variability M",
# "Recruitment variability", "Inter-annual variability effort",
# "Final effort", "MSY", "Average change in catchability", "Inter-annual variabilility in catchability",
# "FMSY", "von Bert. Linf", "von Bert. K", "von Bert. t0", "Steepness",
# "Linf gradient", "K gradient", "Inter-annual variability in Linf",
# "Recruitment gradient", "Inter-annual variability in K", "Age at maturity",
# "Length at 5% selection", "Length at full selection", "Dome-shaped selectivity",
# "Length at first capture", "Auto-correlation recruitment",
# "Length 50% maturity", "Length 95% maturity", "B0", "N0", "SSB0", "BMSY_B0",
# "TACSD", "TACFrac", "TAESD", "TAEFrac", "SizeLimSD", "SizeLimFrac", "Blow", "BMSY",
# "SSBMSY", "Mexp", "Discard mortality", "LR5", "LFR", "DR", "Lm/SL")
# # cbind(Obsnam, LnName)
}
if (Par == "Obs") {
slots <- c("Cat", "Cat", "AvC", "AvC", "CAA", "CAA", "CAL", "CAL",
"Ind", "Ind", "Dep", "Dep", "Dt", "Dt", "Mort", "FMSY_M", "SSBMSY_SSB0",
"L50", "L95", "LFC", "LFS", "Abun", "Abun", "vbK", "vbt0",
"vbLinf", "Steep", "Iref", "Cref", "Bref", "ML", "ML")
Obsnam <- c("Cbias", "Csd", "Cbias", "Csd", "CAA_nsamp", "CAA_ESS",
"CAL_nsamp", "CAL_ESS", "Isd", "betas", "Dbias", "Derr", "Dbias",
"Derr", "Mbias", "FMSY_Mbias", "BMSY_B0Bias", "lenMbias", "lenMbias",
"LFCbias", "LFSbias", "Abias", "Aerr", "Kbias", "t0bias", "Linfbias",
"hbias", "Irefbias", "Crefbias", "Brefbias", "CAL_nsamp", "CAL_ESS")
LnName <- Obsnam
# LnName <- c("Catch bias", "Catch error", "Catch bias", "Catch error",
# "n CAA samples", "CAA effective sample size", "n CAL samples",
# "CAL effective sample size", "Index Abundance error", "Hyperstability/hyperdepletion",
# "Depletion bias", "Depletion error", "Depletion bias", "Depletion error",
# "M bias", "FMSY/M bias", "BMSY/B0 bias", "Length maturity bias",
# "Length maturity bias", "Length first capture bias", "Length full capture bias",
# "Current abundance bias", "Current abundance error", "vB K bias",
# "vB t0 bias", "vB Linf bias", "Steepness bias", "Reference index bias",
# "Reference catch bias", "Reference biomass bias", "Mean length",
# "Mean length")
# print(cbind(slots, Obsnam, LnName))
for (mm in 1:nMPs) {
ids <- Obsnam[slots %in% unlist(strsplit(Required(MPs[mm])[, 2], split = ", "))]
used[match(ids, varNames), mm] <- TRUE
}
}
colnames(used) <- MPs
rownames(used) <- varNames
# Find the highest Stat for each variable
if (nMPs > 1) {
stat <- lapply(senseDat, "[[", "OMStat")
Stat <- matrix(unlist(stat), ncol = nMPs) * used
if (max(Stat, na.rm = TRUE) > 100)
Stat <- Stat/100
rownames(Stat) <- varNames
statord <- apply(Stat, 2, order, decreasing = TRUE)
topStat <- statord[1:nvars, ] # highest nvars for each MP
} else {
stat <- senseDat$OMStat
Stat <- matrix(stat, ncol = nMPs)
rownames(Stat) <- varNames
colnames(Stat) <- MPs
statord <- order(Stat, decreasing = TRUE)
topStat <- statord[1:nvars]
}
if (doPlot) {
## Create Plotting Space ##
Ncol <- nvars
if (nMPs < 2)
Ncol <- min(sum(used[, MPs]), nvars)
if (nMPs > 1) {
temp <- apply(used[, MPs], 2, sum)
if (all(temp < nvars))
Ncol <- max(temp)
}
Nrow <- min(nMP, sum(apply(used, 2, sum) > 0))
if (sum(apply(used, 2, sum) > 0) == 0)
print(paste("No", Par, "used for these MPs"))
mat <- matrix(1:(Nrow * Ncol), nrow = Nrow, byrow = TRUE)
op <- par(mfrow = c(Nrow, Ncol), oma = c(3, 6, 2, 0), mar = c(3, 2, 2, 1))
if (Par == "OM") Title <- "Operating Model Parameters"
if (Par == "Obs") Title <- "Observation Parameters"
# Colors and Controls
ncols <- length(topStat) # nrow(Stat) * ncol(Stat)
Cols <- colorRampPalette(c("green", "red"))(ncols)
# rev(rainbow(ncols,start=0,end=0.36))
highest <- max(Stat, na.rm = TRUE)
pch <- 18
LWD <- 3
LCol <- "black"
count <- 1
mm <- 1
AxCex <- 1.15
doneMP <- 1
# Make MP colors for Available, Acceptable, and Not-Acceptable
availCol <- "green"
acceptCol <- "black"
nonAAcol <- "darkgray"
mpcol <- "black" # default
mpCols <- data.frame(MPs = MPs, col = rep(mpcol, nMPs), stringsAsFactors = FALSE)
if (is.null(acceptMP))
acceptMP <- MPs
if (!is.null(availMP) & (!is.null(acceptMP))) {
mpCols[, 2] <- nonAAcol
mpCols[MPs %in% acceptMP, 2] <- acceptCol
mpCols[MPs %in% acceptMP & MPs %in% availMP, 2] <- availCol
}
AllMPs <- 1:nMPs
AllMPs <- AllMPs[apply(used, 2, sum) > 0] # only include MPs which use the parameter
AllMPs <- AllMPs[1:Nrow] # first nMPs
# find max y
maxY <- 1
for (mm in AllMPs) {
for (vr in 1:Ncol) {
if (nMPs > 1) varind <- topStat[vr, mm] # Variable index
if (nMPs == 1) varind <- topStat[vr]
if (nMPs > 1) {
ys <- senseDat[[mm]]$OMPoints[[varind]][, 2]
} else {
ys <- senseDat$OMPoints[[varind]][, 2]
}
}
maxY <- max(maxY, max(ys))
}
YLim <- c(0, maxY)
for (mm in AllMPs) {
# Loop along MPs Loop along variables
for (vr in 1:Ncol) {
if (nMPs > 1) varind <- topStat[vr, mm] # Variable index
if (nMPs == 1) varind <- topStat[vr]
varSN <- varNames[varind]
varLN <- LnName[match(varSN, Obsnam)]
if (nMPs > 1) {
xs <- senseDat[[mm]]$OMPoints[[varind]][, 1]
ys <- senseDat[[mm]]$OMPoints[[varind]][, 2]
} else {
xs <- senseDat$OMPoints[[varind]][, 1]
ys <- senseDat$OMPoints[[varind]][, 2]
}
if (used[varSN, MPs[mm]]) {
# variable is used
Col <- makeTransparent(Cols[ceiling(Stat[varind, MPs[mm]]/highest * ncols)])
# ylim <- c(0, quantile(ys, 0.95, na.rm = TRUE))
plot(xs, ys, col = Col, pch = pch, bty = "n", axes = FALSE,
xlab = "", ylab = "", ylim = YLim)
if (vr == 1) {
MyCol <- mpCols[match(MPs[mm], mpCols[, 1]), 2]
axis(side = 2, las = 1, cex.axis = AxCex)
mtext(side = 2, MPs[mm], line = 2.75, cex = 1.2, col = MyCol)
}
if (vr != 1)
axis(side = 2, labels = FALSE)
axis(side = 1, cex.axis = AxCex)
if (incStat)
text(max(xs), 0.05 * max(ys), round(Stat[varind, MPs[mm]],
2), pos = 2)
# Smoother line
if (nMPs > 1) {
smX <- senseDat[[mm]]$OMSmooth[[varind]]$x
smY <- senseDat[[mm]]$OMSmooth[[varind]]$y
} else {
smX <- senseDat$OMSmooth[[varind]]$x
smY <- senseDat$OMSmooth[[varind]]$y
}
lines(smX, smY, lwd = LWD, col = LCol)
# Variable Name
if (!incNames)
mtext(side = 1, varSN, cex = 1, line = 2.5)
if (incNames)
mtext(side = 1, varLN, cex = labcex, line = 2.5)
} else {
plot(c(0, 1), axes = FALSE, type = "n", xlab = "", ylab = "")
}
}
}
mtext(side = 3, outer = TRUE, Title, cex = 1.5)
if (YVar == "Y")
mtext(side = 2, outer = TRUE, "Long-term yield relative to MSY (%)",
cex = 1.25, line = 3)
if (YVar == "B")
mtext(side = 2, outer = TRUE, "B/BMSY in last 5 years", cex = 1.25,
line = 3)
}
par(op)
# invisible(Out)
}
#' Biomass wormplot
#'
#' A worm plot for plotting the likelihood of meeting biomass targets in future
#' years.
#'
#' Returns a matrix of nMPs rows and proyears columns which is the fraction of
#' simulations for which biomass was above Bref.
#'
#' @param MSEobj Object of class MSE, output of the runMSE function
#' @param Bref The reference fraction of BMSY (to evaluate the probability of
#' exceeding this level)
#' @param LB The lower bound probability that separates red (bad) and yellow
#' (O.K.) colored segments
#' @param UB The upper bound probability that separates yellow (O.K.) and green
#' (good) colored segments
#' @author T. Carruthers
#' @export wormplot
wormplot <- function(MSEobj, Bref = 0.5, LB = 0.25, UB = 0.75) {
if (UB < LB)
stop("LB parameter must be lower than UB parameter")
if (LB < 0 | LB > 1)
stop("LB parameter must be in the range of 0 to 1")
if (UB < 0 | UB > 1)
stop("UB parameter must be in the range of 0 to 1")
ncol <- ceiling(MSEobj@nMPs^0.3)
nrow <- ceiling(MSEobj@nMPs/ncol)
op <- par(mfcol = c(nrow, ncol), mar = c(0.1, 0.1, 0.1, 0.1), omi = c(0.6,
0.25, 0.3, 0))
Bprob <- apply(MSEobj@SB_SBMSY > Bref, 2:3, sum)/MSEobj@nsim
ind <- order(apply(Bprob, 1, sum), decreasing = T)
BLB <- Bprob > LB
BUB <- Bprob > UB
col <- array("red", dim(Bprob))
col[BLB & !BUB] = "yellow"
col[BUB] = "green"
for (i in 1:(nrow * ncol)) {
if (i < (MSEobj@nMPs + 1)) {
MP <- ind[i]
plot(c(1, MSEobj@proyears + 2), c(-1, 1), col = "white", axes = F)
# abline(h=0)
for (ys in 1:MSEobj@proyears) {
x <- c(ys - 1, ys, ys, ys - 1)
y <- c(rep(Bprob[MP, ys], 2), rep(-Bprob[MP, ys], 2))
pol <- data.frame(x, y)
polygon(pol, col = col[MP, ys], border = NA)
}
legend("top", legend = MSEobj@MPs[MP], bty = "n")
if ((i/nrow) == round(i/nrow, 0))
axis(1, pretty(1:MSEobj@proyears), pretty(1:MSEobj@proyears))
} else {
plot.new()
}
if (i == (nrow * ncol)) {
legend("topright", fill = c("green", "red"), legend = c(paste(">",
round(UB * 100, 0), "% prob.", sep = ""), paste("<", round(LB *
100, 0), "% prob.", sep = "")), bty = "n")
}
}
mtext(paste("Probability of biomass above ", round(Bref * 100, 0),
"% BMSY for ", deparse(substitute(MSE)), sep = ""), 3, outer = T,
line = 0.5)
mtext("Projection year", 1, outer = T, line = 2.5)
mtext(paste("Fraction of simulations above ", round(Bref * 100, 0),
"% BMSY", sep = ""), 2, outer = T, line = 0.25)
par(op)
invisible(Bprob)
}
## old functions - may not be used anymore ####
# #' Calculate Statistics for MP Performance
# #'
# #' Function calculates probabilities and other statistics for a range of
# #' performance metrics
# #'
# #' @param MSEobj An object of class MSE
# #' @param PMRefs A list of reference points for the performance metrics (must
# #' be named)
# #' @param lastYrs The last number of years in the projection to calculate the
# #' statistics
# #' @param UseMean Logical. Calculate mean (TRUE) or median (FALSE)?
# #' @param msg Logical. Print out messages?
# #' @author A. Hordyk
# #' @export MPStats
# MPStats <- function(MSEobj, PMRefs = list(SB_SBMSY = 0.5, SSB_SSB0 = 0.2, F_FMSY = 1,
# AAVY = 30, AAVE = 30), lastYrs = 10, UseMean = TRUE, msg = TRUE) {
#
# if (msg)
# message("Calculating MP Performance for last ", lastYrs, " years")
# flush.console()
#
# sumFun <- ifelse(UseMean, mean, median)
#
# # Assign Variables
# Nyears <- MSEobj@nyears
# Pyears <- MSEobj@proyears
# nMPs <- MSEobj@nMPs
# MPs <- MSEobj@MPs
# nsim <- MSEobj@nsim
# RefYd <- MSEobj@OM$RefY
#
# trefs <- PMRefs
# if (length(names(trefs)) != 5) {
# PMnames <- c("SB_SBMSY", "SSB_SSB0", "F_FMSY", "AAVY", "AAVE")
# DF <- c(0.5, 0.2, 1, 30, 30)
# ind <- which(!PMnames %in% names(trefs))
# if (length(ind) > 0) {
# for (xx in ind) {
# trefs[[PMnames[xx]]] <- DF[xx]
# }
# }
# }
#
# # Error Checks
# if (!class(lastYrs) == "character" & lastYrs >= Pyears) {
# message("lastYrs set too high. Defaulting to last 10 years")
# lastYrs <- 10
# }
# if (lastYrs <= 0 | lastYrs == FALSE | lastYrs == "all" | lastYrs ==
# "All")
# lastYrs <- Pyears
#
# yrs <- (Pyears - lastYrs + 1):Pyears # Years to summarize performance
# yrs <- yrs[!yrs == 1]
# ystart <- 1:lastYrs # First 10 years
# y1 <- (yrs[1] - 1):(yrs[length(yrs)] - 1) # for calculating interannual variability
# y2 <- y1 + 1
#
# # Biomass/BMSY
# B_BMSYm <- apply(MSEobj@SB_SBMSY[, , yrs, drop = FALSE], 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# B_BMSYsd <- apply(MSEobj@SB_SBMSY[, , yrs, drop = FALSE], 2, sd, na.rm = TRUE) # sd in last yrs
# B_BMSYref <- MSEobj@SB_SBMSY[, , yrs, drop = FALSE] > trefs$B_BMSY # above reference?
# B_BMSYp <- round(apply(SB_SBMSYref, 2, sum, na.rm = TRUE)/(lastYrs *
# nsim), 2) # prob above ref
#
# # Biomass/B0
# temp <- as.matrix(expand.grid(1:nsim, 1:nMPs, 1:Pyears))
# Deplet <- array(NA, dim = dim(MSEobj@B_BMSY))
# Deplet[temp] <- (MSEobj@B_BMSY[temp] * MSEobj@OM$SSBMSY_SSB0[temp[, 1]])
#
# SSB_SSB0ref <- Deplet[, , yrs, drop = FALSE] > trefs$SSB_SSB0 # above reference?
# SSB_SSB0m <- apply(Deplet[, , yrs, drop = FALSE], 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# SSB_SSB0sd <- apply(Deplet[, , yrs, drop = FALSE], 2, sd, na.rm = TRUE) # sd in last yrs
# SSB_SSB0p <- round(apply(SSB_SSB0ref, 2, sum, na.rm = TRUE)/(lastYrs * nsim),
# 2) # prob above ref
#
# # F/FMSY
# F_FMSYm <- apply(MSEobj@F_FMSY[, , yrs, drop = FALSE], 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# F_FMSYsd <- apply(MSEobj@F_FMSY[, , yrs, drop = FALSE], 2, sd, na.rm = TRUE) # sd in last yrs
# F_FMSYref <- MSEobj@F_FMSY[, , yrs, drop = FALSE] < trefs$F_FMSY # below reference?
# F_FMSYp <- round(apply(F_FMSYref, 2, sum, na.rm = TRUE)/(lastYrs * nsim), 2) # prob below ref
#
# # AAVY - Interannual variability in yield
# maxVar <- ifelse(trefs$AAVY > 1, trefs$AAVY/100, trefs$AAVY)
# MSEobj@Catch[(!is.finite(MSEobj@Catch[, , , drop = FALSE]))] <- 0 # if catch is NAN or NA, make it 0
# aavy <- (((MSEobj@Catch[, , y1, drop = FALSE] - MSEobj@Catch[, , y2, drop = FALSE])/MSEobj@Catch[,
# , y2, drop = FALSE])^2)^0.5
#
# aavy[is.nan(aavy)] <- NA
# if (sum(aavy == Inf, na.rm=TRUE) > 0) aavy[aavy == Inf] <- NA
#
# # aavy[aavy > 10 * median(aavy, na.rm = TRUE)] <- NA # remove huge spikes from F=0
# AAVYm <- apply(aavy, 2, sumFun, na.rm = TRUE) # median/mean in last yrs
#
# AAVYsd <- apply(aavy, 2, sd, na.rm = TRUE) # sd in last yrs
# AAVYref <- aavy < maxVar
# AAVYp <- round(apply(AAVYref, 2, sum, na.rm = TRUE)/(lastYrs * nsim), 2)
#
# # AAVE - Interannual varialility in effort
# maxVar <- ifelse(trefs$AAVE > 1, trefs$AAVE/100, trefs$AAVE)
# eff <- MSEobj@Effort
# if (all(is.na(eff))) {
# message("Variability in effort unable to be calculated from this version of MSE object")
# aave <- NA
# AAVEm <- rep(NA, length(AAVYm))
# AAVEsd <- rep(NA, length(AAVYsd))
# AAVEref <- array(NA, dim = dim(AAVYref))
# AAVEp <- rep(NA, length(AAVYp))
# flush.console()
# } else {
# aave <- (((eff[, , y1 - 1, drop = FALSE] - eff[, , y2 - 1, drop = FALSE])/eff[,
# , y2 - 1, drop = FALSE])^2)^0.5
# aave[is.nan(aave)] <- NA
# aave[aave == Inf] <- NA
# # aave[aave > 10 * apply(aave, 2, median, na.rm = TRUE)] <- NA # remove huge spikes from F=0
# AAVEm <- apply(aave, 2, sumFun, na.rm = TRUE) # median/mean in last yrs
# AAVEsd <- apply(aave, 2, sd, na.rm = TRUE) # sd in last yrs
# AAVEref <- aave < maxVar
# AAVEp <- round(apply(AAVEref, 2, sum, na.rm = TRUE)/(lastYrs *
# nsim), 2)
# }
#
# # Yield
# LTY <- round(apply(MSEobj@Catch[, , yrs, drop = FALSE]/RefYd, 2, sumFun,
# na.rm = TRUE) * 100, 1) # long-term yield
# STY <- round(apply(MSEobj@Catch[, , ystart, drop = FALSE]/RefYd, 2, sumFun,
# na.rm = TRUE) * 100, 1) # short-term yield
#
# MPtype <- sapply(1:nMPs, function(X) class(get(MPs[X])))
#
# DF <- data.frame(MP = MPs, B_BMSYm = B_BMSYm, B_BMSYsd = B_BMSYsd,
# B_BMSYp = B_BMSYp, SSB_SSB0m = SSB_SSB0m, SSB_SSB0sd = SSB_SSB0sd, SSB_SSB0p = SSB_SSB0p,
# F_FMSYm = F_FMSYm, F_FMSYsd = F_FMSYsd, F_FMSYp = F_FMSYp, AAVYm = AAVYm,
# AAVYsd = AAVYsd, AAVYp = AAVYp, AAVEm = AAVEm, AAVEsd = AAVEsd,
# AAVEp = AAVEp, LTY = LTY, STY = STY, lastYrs = lastYrs, B_BMSYRef = trefs$B_BMSY,
# SSB_SSB0Ref = trefs$SSB_SSB0, F_FMSYRef = trefs$F_FMSY, AAVYRef = trefs$AAVY,
# AAVERef = trefs$AAVE, MPtype = MPtype, stringsAsFactors = FALSE)
#
# Dist <- NULL # calculate distance from corner
# for (X in 1:nrow(DF)) Dist[X] <- euc.dist(c(DF[X, "B_BMSYp"] * 100,
# DF[X, "LTY"]), c(100, max(DF[, "LTY"], na.rm = TRUE)))
#
# DF$Dist <- Dist
#
# Probs <- list(F_FMSYref = F_FMSYref, SSB_SSB0ref = SSB_SSB0ref, B_BMSYref = B_BMSYref,
# AAVYref = AAVYref, AAVEref = AAVEref, Effort = eff)
#
# pastC <- apply(MSEobj@CB_hist[, , , , drop = FALSE], c(1, 3), sum,
# na.rm = TRUE)/RefYd
# temp <- replicate(nMPs, pastC)
# temp <- aperm(temp, c(1, 3, 2))
#
# lastYr <- temp[, , Nyears, drop = FALSE]
# lastYr2 <- replicate(Pyears, lastYr)
# Yield <- abind::abind(lastYr, MSEobj@Catch[, , , drop = FALSE]/RefYd, along = 3)
# # dim(Yield)
#
# # totC <- abind(temp, MSEobj@Catch[,,, drop=FALSE]/RefYd, along=3)
#
# bySim <- list(SSB_SSB0 = Deplet, B_BMSY = MSEobj@B_BMSY, F_FMSY = MSEobj@F_FMSY,
# AAVY = aavy, AAVE = aave, LTY = MSEobj@Catch[, , yrs, drop = FALSE]/RefYd,
# STY = MSEobj@Catch[, , ystart, drop = FALSE]/RefYd, Yield = Yield)
#
# Out <- list(Perf = DF, BySim = bySim, Probs = Probs)
# Out
# }
#
## Supporting functions - not exported ####
calcStat <- function(rr, evalbreaks) {
# supporting function
ind <- as.integer(evalbreaks/2)
ind2 <- as.integer(0.1 * evalbreaks)
ind3 <- as.integer(0.9 * evalbreaks)
if (all(is.na(rr))) return(0)
if (all(rr$x == 0)) return(0)
sum((rr$y - mean(rr$y, na.rm = TRUE))^2)
}
calcMSESense <- function(MP = 1, MSEobj, YVar = c("Y", "B"), Par = c("Obs","OM"),
evalbreaks = NULL, quants = c(0.05, 0.95)) {
# supporting function
YVar <- match.arg(YVar)
# Calculate for a single MP
if (length(MP) > 1) stop("Only one MP")
nMPs <- MSEobj@nMPs
MPs <- MSEobj@MPs
if (methods::is(MP,"character")) mm <- which(MPs %in% MP)
if (methods::is(MP, "numeric") | methods::is(MP,"integer")) {
mm <- MP
MP <- MPs[mm]
}
nsims <- MSEobj@nsim
RefYd <- MSEobj@OM$RefY
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
if (is.null(evalbreaks)) evalbreaks <- as.integer(nsims/4) # number of breaks for loess smoother
if (YVar == "Y") {
if (length(dim(MSEobj@Catch)) > 2) {
yout <- apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd * 100
} else {
yout <- apply(MSEobj@Catch[, yind], 1, mean, na.rm = T)/RefYd * 100
}
}
if (YVar == "B") {
if (length(dim(MSEobj@B_BMSY)) > 2) {
yout <- apply(MSEobj@B_BMSY[, mm, yind], 1, mean, na.rm = T)
} else {
yout <- apply(MSEobj@B_BMSY[, yind], 1, mean, na.rm = T)
}
}
# Operating Model names to include
MSEobj@OM$Lm_SL <- MSEobj@OM$L50/MSEobj@OM$LFS
varnames <- names(MSEobj@OM)
vars <- MSEobj@OM
vargood <- (apply(vars, 2, sd, na.rm = TRUE)/(apply(vars, 2, mean, na.rm = TRUE)^2)^0.5) > 0.005
# vargood[grep("qvar", varnames)] <- FALSE
vargood[is.na(vargood)] <- TRUE
varnames <- varnames[vargood]
omvals <- MSEobj@OM[, varnames]
# Ignore these parameters from VOI plot
omvals$RefY <- 0
omvals$A <- 0
omvals$Asp <- 0
omvals$OFLreal <- 0
omvals$FMSY <- 0
omvals$MSY <- 0
omvals$B0 <- 0
omvals$N0 <- 0
omvals$SSB0 <- 0
omvals$BMSY <- 0
omvals$SSBMSY <- 0
omvals$LFC <- 0
omvals$FMSY_M <- 0
omvals$BMSY_B0 <- 0
omvals$SSBMSY_SSB0 <- 0
omvals$MGT <- 0
omvals$Blow <- 0
omvals$FMSY_M <- 0
omvals$maxlen <- 0
omvals$SRrel <- 0
omvals$FinF <- 0
omvals[is.na(omvals)] <- 0
OMSmooth <- OMStat <- obvals <- OBSmooth <- OMPoints <- OMNames <- NULL
if (Par == "OM") {
# Apply loess smoother to Operating Model parameters
rr <- as.list(omvals)
OMNames <- names(rr)
OMSmooth <- list()
for (xx in 1:length(rr)) {
m <- matrix(c(rr[[xx]], yout), ncol = 2)
m <- m[order(m[, 1]), ]
qnts <- quantile(m[, 1], quants, na.rm = TRUE)
if (all(m[, 1] == 0)) {
OMSmooth[[xx]] <- NA
} else {
nas <- which(apply(!apply(m, 2, is.na), 1, prod) == 0)
if (length(nas) > 0) m <- m[-nas, ]
ind <- findInterval(qnts, m[, 1])
m <- m[ind[1]:ind[2], ]
rr[[xx]] <- m
OMSmooth[[xx]] <- suppressWarnings(loess.smooth(rr[[xx]][, 1], rr[[xx]][, 2]))
OMPoints[[xx]] <- m
}
}
# Calculate stat for OM curve
OMStat <- unlist(lapply(OMSmooth, calcStat, evalbreaks = evalbreaks))
}
if (Par == "Obs") {
# Observation Parameters
varnames <- names(MSEobj@Obs)
vars <- MSEobj@Obs
vargood <- (apply(vars, 2, sd, na.rm = TRUE)/(apply(vars, 2, mean, na.rm = TRUE)^2)^0.5) > 0.005
varnames <- varnames[vargood]
varnames <- varnames[!is.na(varnames)]
obvals <- MSEobj@Obs[, varnames]
rr <- as.list(obvals)
OMNames <- names(rr)
OMSmooth <- list()
for (xx in 1:length(rr)) {
m <- matrix(c(rr[[xx]], yout), ncol = 2)
m <- m[order(m[, 1]), ]
qnts <- quantile(m[, 1], quants, na.rm = TRUE)
if (all(m[, 1] == 0)) {
OMSmooth[[xx]] <- NULL
} else {
nas <- which(apply(!apply(m, 2, is.na), 1, prod) == 0)
if (length(nas) > 0)
m <- m[-nas, ]
ind <- findInterval(qnts, m[, 1])
m <- m[ind[1]:ind[2], ]
rr[[xx]] <- m
OMSmooth[[xx]] <- suppressWarnings(loess.smooth(rr[[xx]][, 1], rr[[xx]][, 2]))
OMPoints[[xx]] <- m
}
}
# Calculate stat for OM curve
OMStat <- unlist(lapply(OMSmooth, calcStat, evalbreaks = evalbreaks))
}
Out <- list(OMPoints = OMPoints, OMSmooth = OMSmooth, OMStat = OMStat, OMNames = OMNames)
Out
}
custombar<-function(dat,MPnams,tickwd1=0.1,tickwd2=0.05,lwd1=2,lwd2=2,xlab=T,bcol="azure2",barcol='dark grey'){
nMPer<-nrow(dat)
incr<-(max(dat)-min(dat))*0.05
plot(dat[,5],ylim=c(min(dat)-incr,max(dat)+incr),xlim=c(0.25,nMPer+0.25),col='white',axes=F,ylab="",xlab="")
if(xlab){
axis(1,-1:(nMPer+1),c("","",MPnams,""),las=2,font=2,cex.axis=1.2)
}else{
axis(1,-1:(nMPer+1),rep("",nMPer+3))
}
incr<-(max(dat)-min(dat))*0.2
yp<-pretty(seq(min(dat)-incr,max(dat)+incr,length.out=12))
axis(2,yp,yp,las=2)
big<-1E20
polygon(c(-big,big,big,-big),c(-big,-big,big,big),col=bcol)# grey92
abline(h=yp,col='white')
for(i in 1:nMPer){
lines(c(i,i),c(dat[i,1],dat[i,5]),lwd=lwd2) # 80%
lines(c(i,i),c(dat[i,2],dat[i,4]),lwd=lwd1) # 80%
polygon(c(i-tickwd1/2,i+tickwd1/2,i+tickwd1/2,i-tickwd1/2),c(dat[i,2],dat[i,2],dat[i,4],dat[i,4]),lwd=lwd1,col=barcol) # lower interquartile
#lines(c(i-tickwd1/2,i+tickwd1/2),c(dat[i,4],dat[i,4]),lwd=lwd1) # upper interquartile
lines(c(i-tickwd2/2,i+tickwd2/2),c(dat[i,1],dat[i,1]),lwd=lwd1) # lower 80%
lines(c(i-tickwd2/2,i+tickwd2/2),c(dat[i,5],dat[i,5]),lwd=lwd2) # upper 80%
}
points(dat[,3],pch=19,cex=1.1)
}
# A simulation by simulation approach to plotting results
comp <- function(MSEobj, MPs = NA) {
if (is.na(MPs))
MPs <- MSEobj@MPs
notm <- MPs[!(MPs %in% MSEobj@MPs)]
canm <- MPs[MPs %in% MSEobj@MPs]
if (length(notm) > 0)
print(paste("Methods", paste(notm, collapse = ", "), "were not carried out in MSE",
deparse(substitute(MSEobj)), sep = " "))
if (length(canm) == 0)
stop(paste("None of the methods you specified were carried out in the MSE",
deparse(substitute(MSEobj)), sep = ""))
if (length(canm) > 4) {
print(paste("A maximum of four methods can be compared at once. Plotting first four:",
paste(canm[1:4], collapse = ", "), sep = " "))
canm <- canm[1:4]
}
mind <- match(canm, MSEobj@MPs)
nm <- length(mind)
nsim <- MSEobj@nsim
proyears <- MSEobj@proyears
Yd <- array(NA, c(nm, nsim))
P10 <- array(NA, c(nm, nsim))
P50 <- array(NA, c(nm, nsim))
P100 <- array(NA, c(nm, nsim))
POF <- array(NA, c(nm, nsim))
yind <- max(MSEobj@proyears - 4, 1):MSEobj@proyears
RefYd <- MSEobj@OM$RefY
for (m in 1:nm) {
mm <- mind[m]
Yd[m, ] <- round(apply(MSEobj@Catch[, mm, yind], 1, mean, na.rm = T)/RefYd *
100, 1)
POF[m, ] <- round(apply(MSEobj@F_FMSY[, mm, ] > 1, 1, sum, na.rm = T)/proyears *
100, 1)
P10[m, ] <- round(apply(MSEobj@B_BMSY[, mm, ] < 0.1, 1, sum, na.rm = T)/proyears *
100, 1)
P50[m, ] <- round(apply(MSEobj@B_BMSY[, mm, ] < 0.5, 1, sum, na.rm = T)/proyears *
100, 1)
P100[m, ] <- round(apply(MSEobj@B_BMSY[, mm, ] < 1, 1, sum, na.rm = T)/proyears *
100, 1)
}
MSEcols <- c("red", "green", "blue", "orange")
# dev.new2(width=7,height=7)
op <- par(mfrow = c(2, 2), mai = c(0.85, 0.7, 0.1, 0.1), omi = rep(0.01, 4))
tradeoffplot2(POF, Yd, "Prob. of overfishing (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = NA)
tradeoffplot2(P100, Yd, "Prob. biomass < BMSY (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = canm)
tradeoffplot2(P50, Yd, "Prob. biomass < 0.5BMSY (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = NA)
tradeoffplot2(P10, Yd, "Prob. biomass < 0.1BMSY (%)", "Relative yield",
vl = 50, hl = 100, coly = MSEcols, leg = NA)
par(op)
}
# Supporting Functions for Plots
tradeoffplot <- function(x, y, xlab, ylab, labs, cex, vl, hl) {
adjj <- c(0.7, 1.3)
XLim <- c(min(c(-10, min(x, na.rm = T) * adjj)), max(c(max(x, na.rm = T) *
adjj, 110)))
YLim <- c(min(c(-10, min(y, na.rm = T) * adjj)), max(c(max(y, na.rm = T) *
adjj, 110)))
coly <- rep(c("#0000ff95", "#ff000095", "#20ff1095"), 50)[1:length(labs)]
coly[labs %in% c("AvC", "curE", "FMSYref")] <- "black"
# plot(NA,xlim=range(x,na.rm=T)*adjj,ylim=range(y,na.rm=T)*adjj,xlab=xlab,ylab=ylab)
plot(NA, xlim = XLim, ylim = YLim, xlab = xlab, ylab = ylab)
abline(v = vl, col = "#99999940", lwd = 2)
abline(h = hl, col = "#99999940", lwd = 2)
text(x, y, labs, font = 2, col = coly, cex = 0.9)
}
tradeoffplot2 <- function(x, y, xlab, ylab, cex = 1, vl, hl, coly, leg) {
adjj <- c(0.7, 1.3)
plot(NA, xlim = range(x, na.rm = T) * adjj, ylim = range(y, na.rm = T) *
adjj, xlab = xlab, ylab = ylab)
abline(v = vl, col = "grey", lwd = 2)
abline(h = hl, col = "grey", lwd = 2)
for (m in 1:nrow(x)) points(x[m, ], y[m, ], col = makeTransparent(coly[m],
50), pch = 19, cex = cex)
if (!is.na(leg[1]))
legend("topright", legend = leg, text.col = coly, bty = "n")
}
tradeoffplot3 <- function(x, y, xlab, ylab, labs, cex, vl, hl, xlim, ylim) {
coly <- rep(c("#0000ff95", "#ff000095", "#20ff1095"), 10)
plot(NA, xlim = xlim, ylim = ylim, xlab = xlab, ylab = ylab)
abline(v = vl, col = "#99999940", lwd = 2)
abline(h = hl, col = "#99999940", lwd = 2)
text(x, y, labs, font = 2, col = coly, cex = 1)
}
tradeoffplot4 <- function(x, y, xlab, ylab, labs, cex, vl, hl, ShowLabs = FALSE,
ShowCols = FALSE, AvailMPs = NULL) {
adjj <- c(0.9, 1.1)
XLim <- c(min(c(-10, min(x, na.rm = T) * adjj)), max(c(max(x, na.rm = T) *
adjj, 110)))
YLim <- c(min(c(-10, min(y, na.rm = T) * adjj)), max(c(max(y, na.rm = T) *
adjj, 110)))
# Which MPs meet minimum PMs
ind <- which(x >= vl & y >= hl)
coly <- rep("darkgray", length(labs))
coly[ind] <- "black"
# coly[labs%in%c('AvC','curE','FMSYref')]<-'black'
Pch <- rep(21, length(labs))
# Pch[labs%in%c('AvC','curE','FMSYref')] <- 17
coly[grep("FMSY", labs)] <- "black"
Pch[grep("FMSY", labs)] <- 24
if (!is.null(AvailMPs))
Pch[labs %in% AvailMPs] <- 21
if (!is.null(AvailMPs))
coly[labs %in% AvailMPs & (x >= vl & y >= hl)] <- "green"
# coly<-rep(c('#0000ff95','#ff000095','#20ff1095'),50)[1:length(labs)]
plot(NA, xlim = XLim, ylim = YLim, xlab = xlab, ylab = ylab, bty = "l",
las = 1)
abline(v = vl, col = "#99999940", lwd = 2)
abline(h = hl, col = "#99999940", lwd = 2)
Alpha <- 30
# polygons
LeftCol <- rgb(red = 255, green = 0, blue = 0, alpha = Alpha, names = NULL,
maxColorValue = 255)
RightCol <- rgb(red = 0, green = 255, blue = 0, alpha = Alpha, names = NULL,
maxColorValue = 255)
if (ShowCols) {
polygon(x = c(0, vl, vl, 0), y = c(0, 0, hl, hl), col = LeftCol,
border = NA)
polygon(x = c(0, vl, vl, 0), y = c(0, 0, 100, 100), col = LeftCol,
border = NA)
polygon(x = c(vl, 100, 100, vl), y = c(0, 0, 100, 100), col = RightCol,
border = NA)
polygon(x = c(vl, 100, 100, vl), y = c(hl, hl, 100, 100), col = RightCol,
border = NA)
}
Cex <- 1.5
if (!ShowLabs)
points(x, y, bg = coly, pch = Pch, cex = Cex, col = "black")
if (ShowLabs)
text(x, y, labs, font = 2, col = coly, cex = 1)
# if(IdPoints) { message('Click points on plot to display MP name')
# message('Click Stop to finish') flush.console() identify(x,y,
# labels=labs) }
labs[ind]
}
#' Plot Spawning stock biomass and reference points for both historical and projected period
#'
#'
#' @param MSE An object of class 'MSE' produced by from runMSE()
#' @param simno Positive integer, the simulation number you wish to plot
#' @param ystart Positive integer, the calendar year corresponding with the first historical year
#' @param log Boolean, whether log SSB and reference points should be plotted
#' @param leg Boolean, should a legend be included in the plot?
#' @author T. Carruthers
#' @export SSBrefplot
SSBrefplot<-function(MSE,simno=1,ystart=1,log=F,leg=T){
ylaby<-ystart-1+(1:(MSE@nyears+MSE@proyears))
SSB <- c(MSE@SSB_hist[simno,],MSE@SSB[simno,1,])
# SSB <- c(apply(MSE@SSB_hist[simno,,,],2,sum),MSE@SSB[simno,1,])
dSSB0 <- MSE@RefPoint$Dynamic_Unfished$SSB0[simno,]
hSSB0 <- SSB[1]
aSSB0 <- MSE@RefPoint$ByYear$SSB0[simno,]
aSSBMSY <- MSE@RefPoint$ByYear$SSBMSY[simno,]
aBrat<-aSSBMSY/aSSB0
dSSBMSY <- aBrat*dSSB0
ylab="Spawning stock biomass"
ymin=0
if(log){
SSB<-log(SSB); dSSB0<-log(dSSB0); hSSB0<-log(hSSB0); aSSB0=log(aSSB0); dSSBMSY=log(dSSBMSY); aSSBMSY=log(aSSBMSY)
ylab = "log Spawning Stock Biomass"; ymin=min(SSB,dSSB0,hSSB0,aSSB0)
}
plot(ylaby,SSB,ylim=c(ymin,max(SSB,dSSB0,hSSB0,aSSB0)),yaxs='i',type='l',xlab="Year",ylab=ylab,col="white")
abline(h=hSSB0,lty=2,col="grey")
abline(v=ystart+MSE@nyears-0.5,col='grey')
lines(ylaby,dSSB0,col='#ff000090',lty=2,lwd=2)
lines(ylaby,dSSBMSY,col='#ff000090',lty=2)
lines(ylaby,aSSB0,col='#0000ff90',lty=2,lwd=2)
lines(ylaby,aSSBMSY,col='#0000ff90',lty=2)
lines(ylaby,SSB,lwd=2,col='#99999999')
if(leg)legend('topright',legend=c("Simulated","Asymp. SSB0","Asymp. SSBMSY","Dyn. SSB0","Dyn. SSBMSY","Historical"),
text.col=c('dark grey','blue','blue','red','red','dark grey'),
col=c('dark grey','blue','blue','red','red','dark grey'), lty=c(1,2,2,2,2,2),lwd=c(2,2,1,2,1,1),bty='n')
}
#' Make colors transparent
#'
#'
#' @param someColor Character string describing color
#' @param alpha transparency
#' @author T. Carruthers
#' @export makeTransparent
makeTransparent <- function(someColor, alpha = 100) {
newColor <- col2rgb(someColor)
apply(newColor, 2, function(curcoldata) {
rgb(red = curcoldata[1], green = curcoldata[2], blue = curcoldata[3],
alpha = alpha, maxColorValue = 255)
})
}
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2)^2))
GetStat <- function(PM, LTY, STY, PNOF, BMSYref, B0ref, VY) {
switch(PM, `Long-term Yield` = LTY, `Short-term Yield` = STY, Overfishing = PNOF,
`Biomass:BMSY` = BMSYref, `Biomass:B0` = B0ref, AnnualVar = VY)
}
StatLab <- function(PM, maxVar, BmsyRef, B0Ref) {
switch(PM, `Long-term Yield` = "Long-term Yield", `Short-term Yield` = "Short-term Yield",
Overfishing = "Prob. of Not Overfishing (%)", `Biomass:BMSY` = paste0("Prob. Biomass >",
BmsyRef, "BMSY (%)"), `Biomass:B0` = paste0("Prob. Biomass >",
B0Ref, "B0 (%)"), AnnualVar = paste0("Prob. AAVY <", maxVar,
"%"))
}
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