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Functions.r
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Functions.r
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gg_color_hue <- function(n)
{
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
rbeta.ab <- function(n, m, s, a, b)
{
# calculate mean of corresponding standard beta dist
mu.std <- (m-a)/(b-a)
# calculate parameters of std. beta with mean=mu.std and sd=s
alpha <- (mu.std^2 - mu.std^3 - mu.std*s^2) / s^2
beta <- (mu.std - 2*mu.std^2 + mu.std^3 - s^2 + mu.std*s^2) / s^2
# generate n draws from standard beta
b.std <- rbeta(n, alpha, beta)
# linear transformation from beta(0,1) to beta(a,b)
b.out <- (b-a)*b.std + a
return(b.out)
}
#current.year: Year to report output
#mod.names: List the names of the sensitivity runs
#likelihood.out=c(1,1,1): Note which likelihoods are in the model (surveys, lengths, ages)
#Sensi.RE.out="Sensi_RE_out.DMP": #Saved file of relative changes
#CI=0.95:Confidence interval box based on the reference model
#TRP.in=0.4:Target relative abundance value
#LRP.in=0.25: Limit relative abundance value
#sensi_xlab="Sensitivity scenarios" : X-axis label
#ylims.in=c(-1,2,-1,2,-1,2,-1,2,-1,2,-1,2): Y-axis label
#plot.figs=c(1,1,1,1,1,1): Which plots to make/save?
#sensi.type.breaks=NA: vertical breaks that can separate out types of sensitivities
#anno.x=NA: Vertical positioning of the sensitivity types labels
#anno.y=NA: Horizontal positioning of the sensitivity types labels
#anno.lab=NA: Sensitivity types labels
Sensi_plot_horiz <- function(model.summaries,
dir = "",
current.year,
mod.names,
likelihood.out = c(1, 1, 1),
Sensi.RE.out = "Sensi_RE_out.DMP",
CI = 0.95,
TRP.in = 0.25,
LRP.in = 0.125,
sensi_xlab = "Sensitivity scenarios",
ylims.in = c(-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1),
plot.figs = c(1, 1, 1, 1, 1, 1),
sensi.type.breaks = NA,
anno.x = NA,
anno.y = NA,
anno.lab = NA,
header.text=2,
horizontal = FALSE) {
# internal function
gg_color_hue <- function(n) {
hues <- seq(15, 375, length = n + 1)
grDevices::hcl(h = hues, l = 65, c = 100)[1:n]
}
#Create all likelihood names
mod.like.names<-unique(model.summaries$likelihoods_by_fleet$Label)
build.like.tab<-colnames(model.summaries$likelihoods_by_fleet)
Fleet.likes<-model.summaries$likelihoods_by_fleet
mod_sums.temp1<-Fleet.likes[1,]
mod_sums.temp1[,1:length(mod_sums.temp1)]<-NA
for(t in 1:model.summaries$n)
{
mod.labs.in<-subset(Fleet.likes,model==t)$Label
add.labs<-mod.like.names[!mod.like.names%in%mod.labs.in]
if(length(add.labs)>0)
{
mod.likes<-mod_sums.temp1%>% slice(rep(1:n(), each = length(add.labs)))
mod.likes[,1]<-t
mod.likes[,2]<-add.labs
Fleet.likes<-rbind(Fleet.likes,mod.likes)
}
}
# num.likes<-sum(likelihood.out)*2+2
num.likes <- dim(Fleet.likes)[1] # determine how many likelihoods components
if (missing(mod.names)) {
mod.names <- paste("model ", 1:model.summaries$n)
}
# grab the survey likelihoods
if (likelihood.out[1] == 1 & any(mod.like.names=="Surv_like")) {
syrvlambda_index <- c(1:num.likes)[Fleet.likes$Label == "Surv_lambda"]
survey.lambda <- data.frame(rownames(t(Fleet.likes))[-1:-2], t(Fleet.likes[syrvlambda_index, ][-1:-2]), "Survey_lambda")
syrvlike_index <- c(1:num.likes)[Fleet.likes$Label == "Surv_like"]
survey.like <- data.frame(rownames(t(Fleet.likes))[-1:-2], t(Fleet.likes[syrvlike_index, ][-1:-2]), "Survey_likelihood")
} else {
survey.lambda <- survey.like <- data.frame(t(rep(NA, model.summaries$n + 2)))
}
# length likelihoods
if (likelihood.out[2] == 1 & any(mod.like.names=="Length_like")) {
Ltlambda_index <- c(1:num.likes)[Fleet.likes$Label == "Length_lambda"]
Lt.lambda <- data.frame(rownames(t(Fleet.likes))[-1:-2], t(Fleet.likes[Ltlambda_index, ][-1:-2]), "Lt_lambda")
Ltlike_index <- c(1:num.likes)[Fleet.likes$Label == "Length_like"]
Lt.like <- data.frame(rownames(t(Fleet.likes))[-1:-2], t(Fleet.likes[Ltlike_index, ][-1:-2]), "Lt_likelihood")
} else {
Lt.lambda <- Lt.like <- data.frame(t(rep(NA, model.summaries$n + 2)))
}
# age likelihood
if (likelihood.out[3] == 1 & any(mod.like.names=="Age_like")) {
Agelambda_index <- c(1:num.likes)[Fleet.likes$Label == "Age_lambda"]
Age.lambda <- data.frame(rownames(t(Fleet.likes))[-1:-2], t(Fleet.likes[Agelambda_index, ][-1:-2]), "Age_lambda")
Agelike_index <- c(1:num.likes)[Fleet.likes$Label == "Age_like"]
Age.like <- data.frame(rownames(t(Fleet.likes))[-1:-2], t(Fleet.likes[Agelike_index, ][-1:-2]), "Age_likelihood")
} else {
Age.lambda <- Age.like <- data.frame(t(rep(NA, model.summaries$n + 2)))
}
parms <- model.summaries$pars
# rownames(parms)<-parms$Label
parms <- data.frame(parms$Label, parms[, 1:(dim(parms)[2] - 3)], "Parameters")
if (any(model.summaries$nsexes == 1)) {
dev.quants <- rbind(
model.summaries$quants[model.summaries$quants$Label == "SSB_Initial", 1:(dim(model.summaries$quants)[2] - 2)] / 2,
(model.summaries$quants[model.summaries$quants$Label == paste0("SSB_", current.year), 1:(dim(model.summaries$quants)[2] - 2)]) / 2,
model.summaries$quants[model.summaries$quants$Label == paste0("Bratio_", current.year), 1:(dim(model.summaries$quants)[2] - 2)],
model.summaries$quants[model.summaries$quants$Label == "Dead_Catch_SPR", 1:(dim(model.summaries$quants)[2] - 2)] / 2,
model.summaries$quants[model.summaries$quants$Label == "annF_SPR", 1:(dim(model.summaries$quants)[2] - 2)]
)
# Extract SDs for use in the ggplots
dev.quants.SD <- c(
model.summaries$quantsSD[model.summaries$quantsSD$Label == "SSB_Initial", 1] / 2,
(model.summaries$quantsSD[model.summaries$quantsSD$Label == paste0("SSB_", current.year), 1]) / 2,
model.summaries$quantsSD[model.summaries$quantsSD$Label == paste0("Bratio_", current.year), 1],
model.summaries$quantsSD[model.summaries$quantsSD$Label == "Dead_Catch_SPR", 1] / 2,
model.summaries$quantsSD[model.summaries$quantsSD$Label == "annF_SPR", 1]
)
} # end single-sex check
if (any(model.summaries$nsexes == 2)) {
dev.quants <- rbind(
model.summaries$quants[model.summaries$quants$Label == "SSB_Initial", 1:(dim(model.summaries$quants)[2] - 2)],
model.summaries$quants[model.summaries$quants$Label == paste0("SSB_", current.year), 1:(dim(model.summaries$quants)[2] - 2)],
model.summaries$quants[model.summaries$quants$Label == paste0("Bratio_", current.year), 1:(dim(model.summaries$quants)[2] - 2)],
model.summaries$quants[model.summaries$quants$Label == "Dead_Catch_SPR", 1:(dim(model.summaries$quants)[2] - 2)],
model.summaries$quants[model.summaries$quants$Label == "annF_SPR", 1:(dim(model.summaries$quants)[2] - 2)]
)
# Extract SDs for use in the ggplots
dev.quants.SD <- c(
model.summaries$quantsSD[model.summaries$quantsSD$Label == "SSB_Initial", 1],
(model.summaries$quantsSD[model.summaries$quantsSD$Label == paste0("SSB_", current.year), 1]),
model.summaries$quantsSD[model.summaries$quantsSD$Label == paste0("Bratio_", current.year), 1],
model.summaries$quantsSD[model.summaries$quantsSD$Label == "Dead_Catch_SPR", 1],
model.summaries$quantsSD[model.summaries$quantsSD$Label == "annF_SPR", 1]
)
} # end two-sex check
dev.quants.labs <- data.frame(c("SB0", paste0("SSB_", current.year),
paste0("Bratio_", current.year), "MSY_SPR", "F_SPR"),
dev.quants, "Derived quantities")
AICs <- 2 * model.summaries[["npars"]] + (2 * as.numeric(model.summaries[["likelihoods"]][1,
1:model.summaries[["n"]]]))
deltaAICs <- AICs - AICs[1]
AIC.out <- data.frame(cbind(c("AIC", "deltaAIC"), rbind.data.frame(AICs, deltaAICs), c("AIC")))
colnames(AIC.out) <- colnames(survey.lambda) <- colnames(survey.like) <- colnames(Lt.lambda) <- colnames(Lt.like) <- colnames(Age.lambda) <- colnames(Age.like) <- colnames(parms) <- colnames(dev.quants.labs) <-
c("Type",mod.names, "Label")
Like.parm.quants <- rbind(AIC.out, survey.like, survey.lambda,
Lt.like, Lt.lambda, Age.like, Age.lambda, parms, dev.quants.labs)
Like.parm.quants.table.data <- flextable::as_grouped_data(Like.parm.quants,
groups = c("Label"))
write.csv(Like.parm.quants.table.data, file.path(dir, "Likes_parms_devquants_table.csv"))
# dev.quants.labs <- data.frame(c("SB0", paste0("SSB_", current.year), paste0("Bratio_", current.year), "MSY_SPR", "F_SPR"), dev.quants, "Derived quantities")
# AICs <- 2 * model.summaries$npars + (2 * as.numeric(model.summaries$likelihoods[1, 1:model.summaries$n]))
# deltaAICs <- AICs - AICs[1]
# AIC.out <- data.frame(cbind(c("AIC", "deltaAIC"), rbind.data.frame(AICs, deltaAICs), c("AIC")))
# colnames(AIC.out) <- colnames(survey.lambda) <- colnames(survey.like) <- colnames(Lt.lambda) <- colnames(Lt.like) <- colnames(Age.lambda) <- colnames(Age.like) <- colnames(parms) <- colnames(dev.quants.labs) <- c("Type", mod.names, "Label")
# Like.parm.quants <- rbind(AIC.out, survey.like, survey.lambda, Lt.like, Lt.lambda, Age.like, Age.lambda, parms, dev.quants.labs)
# Like.parm.quants.table.data <- flextable::as_grouped_data(Like.parm.quants, groups = c("Label"))
# # as_flextable(Like.parm.quants.table.data)
# write.csv( Like.parm.quants.table.data, file.path(dir, "Likes_parms_devquants_table.csv")
# )
# Calcualte Relative changes
dev.quants.mat <- as.matrix(dev.quants)
colnames(dev.quants.mat) <- 1:dim(dev.quants.mat)[2]
rownames(dev.quants.mat) <- c("SB0", paste0("SSB_", current.year), paste0("Bratio_", current.year), "MSY_SPR", "F_SPR")
# RE<-reshape2::melt((as.matrix(dev.quants)-as.matrix(dev.quants)[,1])/as.matrix(dev.quants)[,1])
RE <- reshape2::melt((dev.quants.mat - dev.quants.mat[, 1]) / dev.quants.mat[, 1])[-1:-5, ]
logRE <- reshape2::melt(log(dev.quants.mat / dev.quants.mat[, 1]))[-1:-5, ]
# Get values for plots
Dev.quants.temp <- as.data.frame(cbind(rownames(dev.quants.mat), dev.quants.mat[, -1]))
colnames(Dev.quants.temp) <- c("Metric", mod.names[-1])
Dev.quants.ggplot <- data.frame(reshape2::melt(Dev.quants.temp, id.vars = c("Metric")), RE[, 2:3], logRE[, 2:3])
colnames(Dev.quants.ggplot) <- c("Metric", "Model_name", "Value", "Model_num_plot", "RE", "Model_num_plot_log", "logRE")
Dev.quants.ggplot$Metric <- factor(Dev.quants.ggplot$Metric, levels = unique(Dev.quants.ggplot$Metric))
save(Dev.quants.ggplot, file = file.path(dir, Sensi.RE.out))
# Calculate RE values for reference model boxes
CI_DQs_RE <- ((dev.quants[, 1] + dev.quants.SD * qnorm(CI)) - dev.quants[, 1]) / dev.quants[, 1]
TRP <- (TRP.in - dev.quants[3, 1]) / dev.quants[3, 1]
LRP <- (LRP.in - dev.quants[3, 1]) / dev.quants[3, 1]
logCI_DQs_RE <- log((dev.quants[, 1] + dev.quants.SD * qnorm(CI)) / dev.quants[, 1])
logTRP <- log(TRP.in / dev.quants[3, 1])
logLRP <- log(LRP.in / dev.quants[3, 1])
# Plot Relative changes
four.colors <- gg_color_hue(5)
lty.in <- 2
if (any(is.na(sensi.type.breaks))) {
lty.in <- 0
sensi.type.breaks <- c(1, 1)
}
if (any(is.na(anno.x))) {
anno.x <- c(1, 1)
}
if (any(is.na(anno.y))) {
anno.y <- c(1, 1)
}
if (any(is.na(anno.lab))) {
anno.lab <- c("", "")
}
SPRtarg.lab<-paste0('SPR',model.summaries[["sprtargs"]][1]*100,"%")
#if (horizontal == TRUE) {
# RE plot
ggplot(Dev.quants.ggplot, aes(RE, .data$Model_num_plot)) +
geom_point(aes(shape = .data$Metric, color = .data$Metric), size = 3) +
geom_rect(aes(ymin = 1, ymax = model.summaries$n + 1, xmin = -CI_DQs_RE[1], xmax = CI_DQs_RE[1]), fill = NA, color = four.colors[1]) +
geom_rect(aes(ymin = 1, ymax = model.summaries$n + 1, xmin = -CI_DQs_RE[2], xmax = CI_DQs_RE[2]), fill = NA, color = four.colors[2]) +
geom_rect(aes(ymin = 1, ymax = model.summaries$n + 1, xmin = -CI_DQs_RE[3], xmax = CI_DQs_RE[3]), fill = NA, color = four.colors[3]) +
geom_rect(aes(ymin = 1, ymax = model.summaries$n + 1, xmin = -CI_DQs_RE[4], xmax = CI_DQs_RE[4]), fill = NA, color = four.colors[4]) +
geom_rect(aes(ymin = 1, ymax = model.summaries$n + 1, xmin = -CI_DQs_RE[5], xmax = CI_DQs_RE[5]), fill = NA, color = four.colors[5]) +
geom_vline(xintercept = c(TRP + 0.03, LRP - 0.03, 0), lty = c(2, 2, 1), color = c("darkgreen", "darkred", "gray")) +
scale_y_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot$Model_name)) +
# scale_y_continuous(limits=ylims.in[1:2])+
coord_cartesian(xlim = ylims.in[1:2]) +
theme(
axis.text.y = element_text(size = 14, color = 1),
axis.text.x = element_text(size = 14, color = 1),
legend.text.align = 0,
axis.title.x = element_text(size = 14),
legend.text = element_text(size = 15),
legend.title = element_text(size = 12),
panel.grid.minor = element_blank()
) +
scale_shape_manual(
values = c(15:18, 12),
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)])),
bquote(frac(SB[.(current.year)], SB[0])),
bquote(Yield[.(SPRtarg.lab)]),
bquote(F[.(SPRtarg.lab)])
)
) +
scale_color_manual(
values = four.colors[1:5],
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)])),
bquote(frac(SB[.(current.year)], SB[0])),
bquote(Yield[.(SPRtarg.lab)]),
bquote(F[.(SPRtarg.lab)])
)
) +
labs(y = "", x = "Relative change") +
annotate("text", y = anno.x, x = anno.y, label = anno.lab, size = 5, col = 'grey10') +
annotate("text", x = c(0, 0), y = c(TRP + 0.03, LRP - 0.03), label = c("TRP", "LRP"), size = c(2, 2), color = c("darkgreen", "darkred")) +
#annotate("text", y = c((model.summaries$n + 2), (model.summaries$n + 2)),
#x = c(TRP + 0.08, LRP - 0.08), label = c("TRP", "LRP"), size = c(5, 5), color = c("darkgreen", "darkred")) +
geom_hline(yintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_REplot_all_horizontal.png"), width = 12, height = 7)
#}
#if (horizontal == FALSE){
pt.dodge <- 0.3
if (plot.figs[1] == 1) {
# RE plot
ggplot(Dev.quants.ggplot, aes(.data$Model_num_plot, RE)) +
geom_point(aes(shape = .data$Metric, color = .data$Metric), position = position_dodge(pt.dodge)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[1], ymax = CI_DQs_RE[1]), fill = NA, color = four.colors[1]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[2], ymax = CI_DQs_RE[2]), fill = NA, color = four.colors[2]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[3], ymax = CI_DQs_RE[3]), fill = NA, color = four.colors[3]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[4], ymax = CI_DQs_RE[4]), fill = NA, color = four.colors[4]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[5], ymax = CI_DQs_RE[5]), fill = NA, color = four.colors[5]) +
geom_hline(yintercept = c(TRP, LRP, 0), lty = c(2, 2, 1), color = c("darkgreen", "darkred", "gray")) +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot$Model_name)) +
# scale_y_continuous(limits=ylims.in[1:2])+
coord_cartesian(ylim = ylims.in[1:2]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.text.align = 0,
panel.grid.minor = element_blank()
) +
scale_shape_manual(
values = c(15:18, 12),
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)])),
bquote(frac(SB[.(current.year)], SB[0])),
bquote(Yield[.(SPRtarg.lab)]),
bquote(F[.(SPRtarg.lab)])
)
) +
scale_color_manual(
values = four.colors[1:5],
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)])),
bquote(frac(SB[.(current.year)], SB[0])),
bquote(Yield[.(SPRtarg.lab)]),
bquote(F[.(SPRtarg.lab)])
)
) +
labs(x = sensi_xlab, y = "Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
annotate("text", x = c((model.summaries$n + 2), (model.summaries$n + 2)), y = c(TRP + 0.03, LRP - 0.03), label = c("TRP", "LRP"), size = c(3, 3), color = c("darkgreen", "darkred")) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_REplot_all.png"))
# log plot
ggplot(Dev.quants.ggplot, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(shape = .data$Metric, color = .data$Metric), position = position_dodge(pt.dodge)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[1], ymax = logCI_DQs_RE[1]), fill = NA, color = four.colors[1]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[2], ymax = logCI_DQs_RE[2]), fill = NA, color = four.colors[2]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[3], ymax = logCI_DQs_RE[3]), fill = NA, color = four.colors[3]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[4], ymax = logCI_DQs_RE[4]), fill = NA, color = four.colors[4]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[5], ymax = logCI_DQs_RE[5]), fill = NA, color = four.colors[5]) +
geom_hline(yintercept = c(logTRP, logLRP, 0), lty = c(2, 2, 1), color = c("darkgreen", "darkred", "gray")) +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot$Model_name)) +
# scale_y_continuous(limits=ylims.in[1:2])+
coord_cartesian(ylim = ylims.in[1:2]) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), legend.text.align = 0, panel.grid.minor = element_blank()) +
scale_shape_manual(
values = c(15:18, 12),
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)])),
bquote(frac(SB[.(current.year)], SB[0])),
bquote(Yield[.(SPRtarg.lab)]),
bquote(F[.(SPRtarg.lab)])
)
) +
scale_color_manual(
values = four.colors[1:5],
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)])),
bquote(frac(SB[.(current.year)], SB[0])),
bquote(Yield[.(SPRtarg.lab)]),
bquote(F[.(SPRtarg.lab)])
)
) +
labs(x = sensi_xlab, y = "Log relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
annotate("text", x = c(0, 0), y = c(logTRP + 0.03, logLRP - 0.03), label = c("TRP", "LRP"), size = c(2, 2), color = c("darkgreen", "darkred")) +
#annotate("text", x = c((model.summaries$n + 2), (model.summaries$n + 2)), y = c(logTRP + 0.03, logLRP - 0.03), label = c("TRP", "LRP"), size = c(3, 3), color = c("darkgreen", "darkred")) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_logREplot_all.png"))
}
if (plot.figs[1] == 1) {
# RE plots
Dev.quants.ggplot.SBs <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[1] | Metric == unique(Dev.quants.ggplot$Metric)[2])
p1 <- ggplot(Dev.quants.ggplot.SBs, aes(.data$Model_num_plot, RE)) +
geom_point(aes(shape = .data$Metric, color = .data$Metric), position = position_dodge(pt.dodge)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[1], ymax = CI_DQs_RE[1]), fill = NA, color = four.colors[1]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[2], ymax = CI_DQs_RE[2]), fill = NA, color = four.colors[2]) +
scale_x_continuous(breaks = 2:(model.summaries$n)) +
# scale_y_continuous(limits=ylims.in[1:2])+
coord_cartesian(ylim = ylims.in[1:2]) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
panel.grid.minor = element_blank()
) +
scale_shape_manual(
values = c(16, 17),
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)]))
)
) +
scale_color_manual(
values = four.colors[1:2],
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)]))
)
) +
theme(legend.text.align = 0) +
labs(x = " ", y = " ") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab, size=header.text) +
geom_hline(yintercept = 0, lwd = 0.5, color = "gray") +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
Dev.quants.ggplot.Dep <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[3])
p2 <- ggplot(Dev.quants.ggplot.Dep, aes(.data$Model_num_plot, RE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[3], ymax = CI_DQs_RE[3]), fill = NA, color = four.colors[3]) +
scale_x_continuous(breaks = 2:(model.summaries$n)) +
# scale_y_continuous(limits=ylims.in[7:8])+
coord_cartesian(ylim = ylims.in[7:8]) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
panel.grid.minor = element_blank()
) +
theme(legend.text.align = 0) +
labs(x = " ", y = "Relative change") +
scale_colour_manual(
values = four.colors[3],
name = "",
labels = as.expression(bquote(frac(SB[.(current.year)], SB[0])))
) +
annotate("text", x = c(0, 0), y = c(logTRP + 0.03, logLRP - 0.03), label = c("TRP", "LRP"), size = c(2, 2), color = c("darkgreen", "darkred")) +
#annotate("text", x = c((model.summaries$n + 1), (model.summaries$n + 1)), y = c(TRP + 0.1, LRP - 0.1), label = c("TRP", "LRP"), size = c(3, 3), color = c("darkgreen", "darkred")) +
geom_hline(yintercept = c(TRP, LRP, 0), lty = c(3, 3, 1), lwd = c(0.5, 0.5, 0.5), color = c("darkgreen", "darkred", "gray")) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
Dev.quants.ggplot.MSY_FMSY <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[4] | Metric == unique(Dev.quants.ggplot$Metric)[5])
p3 <- ggplot(Dev.quants.ggplot.MSY_FMSY, aes(.data$Model_num_plot, RE, group = .data$Metric)) +
geom_point(aes(shape = .data$Metric, color = .data$Metric), position = position_dodge(pt.dodge)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[4], ymax = CI_DQs_RE[4]), fill = NA, color = four.colors[4]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[5], ymax = CI_DQs_RE[5]), fill = NA, color = four.colors[5]) +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot$Model_name)) +
# scale_y_continuous(limits=ylims.in[9:10])+
coord_cartesian(ylim = ylims.in[9:10]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.text.align = 0,
panel.grid.minor = element_blank(),
legend.margin=margin(0,0,0,0),
legend.box.spacing = unit(0, "pt")
) +
# legend.text=element_text(size=rel(1)))+
scale_shape_manual(
values = c(16, 17),
name = "",
labels = c(bquote(Yield[.(SPRtarg.lab)]), bquote(F[.(SPRtarg.lab)]))
) +
scale_color_manual(
values = four.colors[4:5],
name = "",
labels = c(bquote(Yield[.(SPRtarg.lab)]), bquote(F[.(SPRtarg.lab)]))
) +
labs(x = sensi_xlab, y = "") +
guides(fill = FALSE) +
# annotate("text",x=anno.x,y=anno.y,label=anno.lab)+
geom_hline(yintercept = 0, lwd = 0.5, color = "gray") +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
# p4<-grid.arrange(p1,p2,p3,heights=c(5,5,8))
p4 <- ggpubr::ggarrange(p1, p2, p3, nrow = 3, ncol = 1, align = "v", heights = c(5, 5, 8))
ggsave(file.path(dir, "Sensi_REplot_SB_Dep_F_Yield.png"), p4)
# Log plots
Dev.quants.ggplot.SBs <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[1] | Metric == unique(Dev.quants.ggplot$Metric)[2])
p1 <- ggplot(Dev.quants.ggplot.SBs, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(shape = .data$Metric, color = .data$Metric), position = position_dodge(pt.dodge)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[1], ymax = logCI_DQs_RE[1]), fill = NA, color = four.colors[1]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[2], ymax = logCI_DQs_RE[2]), fill = NA, color = four.colors[2]) +
scale_x_continuous(breaks = 2:(model.summaries$n)) +
# scale_y_continuous(limits=ylims.in[1:2])+
coord_cartesian(ylim = ylims.in[1:2]) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
panel.grid.minor = element_blank()
) +
scale_shape_manual(
values = c(16, 17),
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)]))
)
) +
scale_color_manual(
values = four.colors[1:2],
name = "",
labels = c(
expression(SB[0]),
as.expression(bquote("SB"[.(current.year)]))
)
) +
theme(legend.text.align = 0) +
labs(x = " ", y = " ") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab, size=header.text) +
geom_hline(yintercept = 0, lwd = 0.5, color = "gray") +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
Dev.quants.ggplot.Dep <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[3])
p2 <- ggplot(Dev.quants.ggplot.Dep, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[3], ymax = logCI_DQs_RE[3]), fill = NA, color = four.colors[3]) +
scale_x_continuous(breaks = 2:(model.summaries$n)) +
# scale_y_continuous(limits=ylims.in[7:8])+
coord_cartesian(ylim = ylims.in[7:8]) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
panel.grid.minor = element_blank()
) +
theme(legend.text.align = 0) +
labs(x = " ", y = "Log relative change") +
scale_colour_manual(
values = four.colors[3],
name = "",
labels = as.expression(bquote(frac(SB[.(current.year)], SB[0])))
) +
annotate("text", x = c(0, 0), y = c(logTRP + 0.03, logLRP - 0.03), label = c("TRP", "LRP"), size = c(2, 2), color = c("darkgreen", "darkred")) +
#annotate("text", x = c((model.summaries$n + 1), (model.summaries$n + 1)), y = c(logTRP + 0.08, logLRP - 0.08), label = c("TRP", "LRP"), size = c(3, 3), color = c("darkgreen", "darkred")) +
geom_hline(yintercept = c(logTRP, logLRP, 0), lty = c(3, 3, 1), lwd = c(0.5, 0.5, 0.5), color = c("darkgreen", "darkred", "gray")) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
Dev.quants.ggplot.MSY_FMSY <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[4] | Metric == unique(Dev.quants.ggplot$Metric)[5])
p3 <- ggplot(Dev.quants.ggplot.MSY_FMSY, aes(.data$Model_num_plot, logRE, group = .data$Metric)) +
geom_point(aes(shape = .data$Metric, color = .data$Metric), position = position_dodge(pt.dodge)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[4], ymax = logCI_DQs_RE[4]), fill = NA, color = four.colors[4]) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[5], ymax = logCI_DQs_RE[5]), fill = NA, color = four.colors[5]) +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot$Model_name)) +
# scale_y_continuous(limits=ylims.in[9:10])+
coord_cartesian(ylim = ylims.in[9:10]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.text.align = 0,
panel.grid.minor = element_blank(),
legend.margin=margin(0,0,0,0),
legend.box.spacing = unit(0, "pt")
) +
# legend.text=element_text(size=7.5))+
scale_shape_manual(
values = c(16, 17),
name = "",
labels = c(bquote(Yield[.(SPRtarg.lab)]), bquote(F[.(SPRtarg.lab)]))
) +
scale_color_manual(
values = four.colors[4:5],
name = "",
labels = c(bquote(Yield[.(SPRtarg.lab)]), bquote(F[.(SPRtarg.lab)]))
) +
labs(x = sensi_xlab, y = "") +
guides(fill = FALSE) +
# annotate("text",x=anno.x,y=anno.y,label=anno.lab)+
geom_hline(yintercept = 0, lwd = 0.5, color = "gray") +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
p4 <- ggpubr::ggarrange(p1, p2, p3, nrow = 3, ncol = 1, align = "v", heights = c(5, 5, 8))
# p4<-grid.arrange(p1,p2,p3,heights=c(5,5,8))
ggsave(file.path(dir, "Sensi_logREplot_SB_Dep_F_Yield.png"), p4)
}
if (plot.figs[2] == 1) {
# RE plot
Dev.quants.ggplot.SB0 <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[1])
ggplot(Dev.quants.ggplot.SB0, aes(.data$Model_num_plot, RE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[1], ymax = CI_DQs_RE[1]), fill = NA, color = four.colors[1]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.SB0$Model_name)) +
# scale_y_continuous(limits=ylims.in[3:4])+
coord_cartesian(ylim = ylims.in[3:4]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.text.align = 0,
panel.grid.minor = element_blank()
) +
scale_colour_manual(
values = four.colors[1],
name = "",
labels = expression(SB[0])
) +
labs(x = sensi_xlab, y = "Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_REplot_SO_0.png"))
# Log plot
Dev.quants.ggplot.SB0 <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[1])
ggplot(Dev.quants.ggplot.SB0, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[1], ymax = logCI_DQs_RE[1]), fill = NA, color = four.colors[1]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.SB0$Model_name)) +
# scale_y_continuous(limits=ylims.in[3:4])+
coord_cartesian(ylim = ylims.in[3:4]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.text.align = 0,
panel.grid.minor = element_blank()
) +
scale_colour_manual(
values = four.colors[1],
name = "",
labels = expression(SB[0])
) +
labs(x = sensi_xlab, y = "Log Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_logREplot_SO_0.png"))
}
if (plot.figs[3] == 1) {
# RE plots
Dev.quants.ggplot.SBt <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[2])
ggplot(Dev.quants.ggplot.SBt, aes(.data$Model_num_plot, RE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[2], ymax = CI_DQs_RE[2]), fill = NA, color = four.colors[2]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), minor_breaks = NULL, labels = unique(Dev.quants.ggplot.SBt$Model_name)) +
# scale_y_continuous(limits=ylims.in[5:6])+
coord_cartesian(ylim = ylims.in[5:6]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
# panel.grid.minor = element_blank(),
legend.text.align = 0
) +
scale_colour_manual(
values = four.colors[2],
name = "",
labels = as.expression(bquote("SB"[.(current.year)]))
) +
labs(x = sensi_xlab, y = "Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_REplot_SOcurrent.png"))
# Log plots
Dev.quants.ggplot.SBt <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[2])
ggplot(Dev.quants.ggplot.SBt, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[2], ymax = logCI_DQs_RE[2]), fill = NA, color = four.colors[2]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), minor_breaks = NULL, labels = unique(Dev.quants.ggplot.SBt$Model_name)) +
# scale_y_continuous(limits=ylims.in[5:6])+
coord_cartesian(ylim = ylims.in[5:6]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
# panel.grid.minor = element_blank(),
legend.text.align = 0
) +
scale_colour_manual(
values = four.colors[2],
name = "",
labels = as.expression(bquote("SB"[.(current.year)]))
) +
labs(x = sensi_xlab, y = "Log Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_logREplot_SOcurrent.png"))
}
if (plot.figs[4] == 1) {
# RE plots
Dev.quants.ggplot.Dep <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[3])
ggplot(Dev.quants.ggplot.Dep, aes(.data$Model_num_plot, RE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[3], ymax = CI_DQs_RE[3]), fill = NA, color = four.colors[3]) +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.Dep$Model_name)) +
# scale_y_continuous(limits=ylims.in[7:8])+
coord_cartesian(ylim = ylims.in[7:8]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.text.align = 0,
panel.grid.minor = element_blank()
) +
labs(x = " ", y = "Relative change") +
scale_colour_manual(
values = four.colors[3],
name = "",
labels = as.expression(bquote(frac(SB[.(current.year)], SB[0])))
) +
annotate("text", x = c(0, 0), y = c(logTRP + 0.03, logLRP - 0.03), label = c("TRP", "LRP"), size = c(2, 2), color = c("darkgreen", "darkred")) +
#annotate("text", x = c((model.summaries$n + 2), (model.summaries$n + 2)), y = c(TRP + 0.03, LRP - 0.03), label = c("TRP", "LRP"), size = c(3, 3), color = c("darkgreen", "darkred")) +
labs(x = sensi_xlab, y = "Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_hline(yintercept = c(TRP, LRP, 0), lty = c(3, 3, 1), lwd = c(0.5, 0.5, 0.5), color = c("darkgreen", "darkred", "gray")) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_REplot_status.png"))
# Log plots
Dev.quants.ggplot.Dep <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[3])
ggplot(Dev.quants.ggplot.Dep, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[3], ymax = logCI_DQs_RE[3]), fill = NA, color = four.colors[3]) +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.Dep$Model_name)) +
# scale_y_continuous(limits=ylims.in[7:8])+
coord_cartesian(ylim = ylims.in[7:8]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.text.align = 0,
panel.grid.minor = element_blank()
) +
labs(x = " ", y = "Relative change") +
scale_colour_manual(
values = four.colors[3],
name = "",
labels = as.expression(bquote(frac(SB[.(current.year)], SB[0])))
) +
annotate("text", x = c(0, 0), y = c(logTRP + 0.03, logLRP - 0.03), label = c("TRP", "LRP"), size = c(2, 2), color = c("darkgreen", "darkred")) +
#annotate("text", x = c((model.summaries$n + 2), (model.summaries$n + 2)), y = c(logTRP + 0.03, logLRP - 0.03), label = c("TRP", "LRP"), size = c(2, 2), color = c("darkgreen", "darkred")) +
labs(x = sensi_xlab, y = "Log Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_hline(yintercept = c(logTRP, logLRP, 0), lty = c(3, 3, 1), lwd = c(0.5, 0.5, 0.5), color = c("darkgreen", "darkred", "gray")) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_logREplot_status.png"))
}
if (plot.figs[5] == 1) {
# RE plots
Dev.quants.ggplot.MSY <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[4])
ggplot(Dev.quants.ggplot.MSY, aes(.data$Model_num_plot, RE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[4], ymax = CI_DQs_RE[4]), fill = NA, color = four.colors[4]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.MSY$Model_name)) +
# scale_y_continuous(limits=ylims.in[9:10])+
coord_cartesian(ylim = ylims.in[9:10]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
panel.grid.minor = element_blank()
) +
scale_color_manual(
values = four.colors[4],
name = "",
labels = as.expression(bquote(Yield[.(SPRtarg.lab)]))
) +
labs(x = sensi_xlab, y = "Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_REplot_Yield.png"))
# Log plots
Dev.quants.ggplot.MSY <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[4])
ggplot(Dev.quants.ggplot.MSY, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[4], ymax = logCI_DQs_RE[4]), fill = NA, color = four.colors[4]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.MSY$Model_name)) +
# scale_y_continuous(limits=ylims.in[9:10])+
coord_cartesian(ylim = ylims.in[9:10]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
panel.grid.minor = element_blank()
) +
scale_color_manual(
values = four.colors[4],
name = "",
labels = as.expression(bquote(Yield[.(SPRtarg.lab)]))
) +
labs(x = sensi_xlab, y = "Log Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_logREplot_Yield.png"))
}
if (plot.figs[6] == 1) {
# RE plots
Dev.quants.ggplot.FMSY <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[5])
ggplot(Dev.quants.ggplot.FMSY, aes(.data$Model_num_plot, RE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -CI_DQs_RE[5], ymax = CI_DQs_RE[5]), fill = NA, color = four.colors[5]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.FMSY$Model_name)) +
# scale_y_continuous(limits=ylims.in[11:12])+
coord_cartesian(ylim = ylims.in[11:12]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
panel.grid.minor = element_blank()
) +
scale_color_manual(
values = four.colors[5],
name = "",
labels = as.expression(bquote(F[.(SPRtarg.lab)]))
) +
labs(x = sensi_xlab, y = "Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_REplot_FSPR.png"))
# RE plots
Dev.quants.ggplot.FMSY <- subset(Dev.quants.ggplot, Metric == unique(Dev.quants.ggplot$Metric)[5])
ggplot(Dev.quants.ggplot.FMSY, aes(.data$Model_num_plot, logRE)) +
geom_point(aes(color = .data$Metric)) +
geom_rect(aes(xmin = 1, xmax = model.summaries$n + 1, ymin = -logCI_DQs_RE[5], ymax = logCI_DQs_RE[5]), fill = NA, color = four.colors[5]) +
geom_hline(yintercept = 0, lty = 1, color = "gray") +
scale_x_continuous(breaks = 2:(model.summaries$n), labels = unique(Dev.quants.ggplot.FMSY$Model_name)) +
# scale_y_continuous(limits=ylims.in[11:12])+
coord_cartesian(ylim = ylims.in[11:12]) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
panel.grid.minor = element_blank()
) +
scale_color_manual(
values = four.colors[5],
name = "",
labels = as.expression(bquote(F[.(SPRtarg.lab)]))
) +
labs(x = sensi_xlab, y = "Log Relative change") +
annotate("text", x = anno.x, y = anno.y, label = anno.lab) +
geom_vline(xintercept = c(sensi.type.breaks), lty = lty.in)
ggsave(file.path(dir, "Sensi_logREplot_FSPR.png"))
}
#}
}
profile_multi<-function (dir, oldctlfile = "control.ss_new", masterctlfile = lifecycle::deprecated(),
newctlfile = "control_modified.ss", linenum = NULL, string = NULL,
profilevec = NULL, usepar = FALSE, globalpar = FALSE, parlinenum = NULL,
parstring = NULL, saveoutput = TRUE, overwrite = TRUE, whichruns = NULL,
prior_check = TRUE, read_like = TRUE, exe = "ss", verbose = TRUE,
...)
{
orig_wd <- getwd()
on.exit(setwd(orig_wd))
if (lifecycle::is_present(masterctlfile)) {
lifecycle::deprecate_warn(when = "1.46.0", what = "profile(masterctlfile)",
with = "profile(oldctlfile)")
oldctlfile <- masterctlfile
}
check_exe(exe = exe, dir = dir, verbose = verbose)
if (is.null(linenum) & is.null(string)) {
stop("You should input either 'linenum' or 'string' (but not both)")
}
if (!is.null(linenum) & !is.null(string)) {
stop("You should input either 'linenum' or 'string' (but not both)")
}
if (usepar) {
if (is.null(parlinenum) & is.null(parstring)) {
stop("Using par file. You should input either 'parlinenum' or ",
"'parstring' (but not both)")
}
if (!is.null(parlinenum) & !is.null(parstring)) {
stop("Using par file. You should input either 'parlinenum' or ",
"'parstring' (but not both)")
}
}
if (!is.null(linenum)) {
npars <- length(linenum)
}
if (!is.null(string)) {
npars <- length(string)
}
if (usepar) {
if (!is.null(parlinenum)) {
npars <- length(parlinenum)
}
if (!is.null(parstring)) {
npars <- length(parstring)
}
}
if (is.na(npars) || npars < 1) {
stop("Problem with the number of parameters to profile over. npars = ",
npars)
}
if (is.null(profilevec)) {
stop("Missing input 'profilevec'")
}
if (npars == 1) {
n <- length(profilevec)
}
else {
if ((!is.data.frame(profilevec) & !is.matrix(profilevec)) ||
ncol(profilevec) != npars) {
stop("'profilevec' should be a data.frame or a matrix with ",
npars, " columns")
}
n <- length(profilevec[[1]])
if (any(unlist(lapply(profilevec, FUN = length)) !=
n)) {
stop("Each element in the 'profilevec' list should have length ",
n)
}
if (verbose) {
if (!is.null(string)) {
profilevec_df <- data.frame(profilevec)
names(profilevec_df) <- string
message("Profiling over ", npars, " parameters\n",
paste0(profilevec_df, collapse = "\n"))
}
}
}
if (is.null(whichruns)) {
whichruns <- 1:n
}
else {
if (!all(whichruns %in% 1:n)) {
stop("input whichruns should be NULL or a subset of 1:",
n, "\n", sep = "")
}
}
if (verbose) {
message("Doing runs: ", paste(whichruns, collapse = ", "),
",\n out of n = ", n)
}
converged <- rep(NA, n)
totallike <- rep(NA, n)
liketable <- NULL
if (verbose) {
message("Changing working directory to ", dir, ",\n",
" but will be changed back on exit from function.")
}
setwd(dir)
stdfile <- file.path(dir, "ss.std")
starter.file <- dir()[tolower(dir()) == "starter.ss"]
if (length(starter.file) == 0) {
stop("starter.ss not found in", dir)
}
starter <- SS_readstarter(starter.file, verbose = FALSE)
if (starter[["ctlfile"]] != newctlfile) {
stop("starter file should be changed to change\n", "'",
starter[["ctlfile"]], "' to '", newctlfile, "'")
}
if (prior_check & starter[["prior_like"]] == 0) {
stop("for likelihood profile, you should change the starter file value of\n",
" 'Include prior likelihood for non-estimated parameters'\n",
" from 0 to 1 and re-run the estimation.\n")
}
if (usepar & starter[["init_values_src"]] == 0) {
stop("With setting 'usepar=TRUE', change the starter file value",
" for initial value source from 0 (ctl file) to 1 (par file).\n")
}
if (!usepar & starter[["init_values_src"]] == 1) {
stop("Change the starter file value for initial value source",
" from 1 (par file) to 0 (par file) or change to",
" profile(..., usepar = TRUE).")
}
if (usepar) {
file.copy("ss.par", "parfile_original_backup.sso")
}
for (i in whichruns) {
newrepfile <- paste("Report", i, ".sso", sep = "")
if (!overwrite & file.exists(newrepfile)) {
message("skipping profile i=", i, "/", n, " because overwrite=FALSE\n",
" and file exists: ", newrepfile)
}
else {
message("running profile i=", i, "/", n)
if (npars == 1) {
newvals <- profilevec[i]
}
else {
newvals <- as.numeric(profilevec[i, ])
}
SS_changepars(dir = NULL, ctlfile = oldctlfile,
newctlfile = newctlfile, linenums = linenum,
strings = string, newvals = newvals, estimate = FALSE,
verbose = TRUE, repeat.vals = TRUE)
ctltable_new <- SS_parlines(ctlfile = newctlfile)
if (!any(ctltable_new[["PHASE"]] == 1)) {
warning("At least one parameter needs to be estimated in phase 1.\n",
"Edit control file to add a parameter\n",
"which isn't being profiled over to phase 1.")
}
if (usepar) {
if (globalpar) {
par <- readLines("parfile_original_backup.sso")
}
else {
par <- readLines("ss.par")
}
for (ipar in 1:npars) {
if (!is.null(parstring)) {
parlinenum <- grep(parstring[ipar], par,
fixed = TRUE) + 1
}
if (length(parlinenum) == 0) {
stop("Problem with input parstring = '",
parstring[ipar], "'")
}
parline <- par[parlinenum[ipar]]
parval <- as.numeric(parline)
if (is.na(parval)) {
stop("Problem with parlinenum or parstring for par file.\n",
"line as read: ", parline)
}
par[parlinenum[ipar]] <- ifelse(npars > 1,
profilevec[i, ipar], profilevec[i])
}
note <- c(paste("# New par file created by profile() with the value on line number",
linenum), paste("# changed from", parval,
"to", profilevec[i]))
par <- c(par, "#", note)