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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
# SS_Sensi_plot<-function(
# Dir,
# model.summaries,
# current.year,
# mod.names,
# likelihood.out=c(1,1,1),
# Sensi.RE.out="Sensi_RE_out.DMP",
# CI=0.95,
# TRP.in=0.4,
# LRP.in=0.25,
# sensi_xlab="Sensitivity scenarios",
# ylims.in=c(-1,2,-1,2,-1,2,-1,2,-1,2,-1,2),
# plot.figs=c(1,1,1,1,1,1),
# sensi.type.breaks=NA,
# anno.x=NA,
# anno.y=NA,
# anno.lab=NA)
# {
# #num.likes<-sum(likelihood.out)*2+2
# num.likes<-dim(subset(model.summaries$likelihoods_by_fleet,model==1))[1] #determine how many likelihoods components
# if(missing(mod.names)){mod.names<-paste("model ",1:model.summaries$n)}
# if(likelihood.out[1]==1)
# {
# syrvlambda_index<-c(1:num.likes)[subset(model.summaries$likelihoods_by_fleet,model==1)$Label=="Surv_lambda"]
# survey.lambda<-data.frame(rownames(t(model.summaries$likelihoods_by_fleet))[-1:-2],t(model.summaries$likelihoods_by_fleet[seq(3,dim(model.summaries$likelihoods_by_fleet)[1], num.likes),][-1:-2]),"Survey_lambda")
# syrvlike_index<-c(1:num.likes)[subset(model.summaries$likelihoods_by_fleet,model==1)$Label=="Surv_like"]
# survey.like<-data.frame(rownames(t(model.summaries$likelihoods_by_fleet))[-1:-2],t(model.summaries$likelihoods_by_fleet[seq(syrvlike_index,dim(model.summaries$likelihoods_by_fleet)[1], num.likes),][-1:-2]),"Survey_likelihood")
# }
# else
# {
# survey.lambda<-survey.like<-data.frame(t(rep(NA,model.summaries$n+2)))
# }
# if(likelihood.out[2]==1)
# {
# Ltlambda_index<-c(1:num.likes)[subset(model.summaries$likelihoods_by_fleet,model==1)$Label=="Length_lambda"]
# Lt.lambda<-data.frame(rownames(t(model.summaries$likelihoods_by_fleet))[-1:-2],t(model.summaries$likelihoods_by_fleet[seq(Ltlambda_index,dim(model.summaries$likelihoods_by_fleet)[1], num.likes),][-1:-2]),"Lt_lambda")
# Ltlike_index<-c(1:num.likes)[subset(model.summaries$likelihoods_by_fleet,model==1)$Label=="Length_like"]
# Lt.like<-data.frame(rownames(t(model.summaries$likelihoods_by_fleet))[-1:-2],t(model.summaries$likelihoods_by_fleet[seq(Ltlike_index,dim(model.summaries$likelihoods_by_fleet)[1], num.likes),][-1:-2]),"Lt_likelihood")
# }
# else
# {
# Lt.lambda<-Lt.like<-data.frame(t(rep(NA,model.summaries$n+2)))
# }
# if(likelihood.out[3]==1)
# {
# Agelambda_index<-c(1:num.likes)[subset(model.summaries$likelihoods_by_fleet,model==1)$Label=="Age_lambda"]
# Age.lambda<-data.frame(rownames(t(model.summaries$likelihoods_by_fleet))[-1:-2],t(model.summaries$likelihoods_by_fleet[seq(Agelambda_index,dim(model.summaries$likelihoods_by_fleet)[1], num.likes),][-1:-2]),"Age_lambda")
# Agelike_index<-c(1:num.likes)[subset(model.summaries$likelihoods_by_fleet,model==1)$Label=="Age_like"]
# Age.like<-data.frame(rownames(t(model.summaries$likelihoods_by_fleet))[-1:-2],t(model.summaries$likelihoods_by_fleet[seq(Agelike_index,dim(model.summaries$likelihoods_by_fleet)[1], num.likes),][-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]
# )
# }
# 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]
# )
# }
# 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$n]))
# deltaAICs<-AICs-AICs[1]
# AIC.out<-data.frame(cbind(c("AIC","deltaAIC"),rbind.data.frame(AICs,deltaAICs),c("AIC")))
# colnames(AIC.out)<-colnames(survey.lambda)<-colnames(survey.like)<-colnames(Lt.lambda)<-colnames(Lt.like)<-colnames(Age.lambda)<-colnames(Age.like)<-colnames(parms)<-colnames(dev.quants.labs)<-c("Type",mod.names,"Label")
# Like.parm.quants<-rbind(AIC.out,survey.like,survey.lambda,Lt.like,Lt.lambda,Age.like,Age.lambda,parms,dev.quants.labs)
# Like.parm.quants.table.data<-as_grouped_data(Like.parm.quants,groups=c("Label"))
# #as_flextable(Like.parm.quants.table.data)
# write.csv(Like.parm.quants.table.data,paste0(Dir,"Likes_parms_devquants_table.csv"))
# #Calcualte Relative changes
# dev.quants.mat<-as.matrix(dev.quants)
# colnames(dev.quants.mat)<-1:dim(dev.quants.mat)[2]
# rownames(dev.quants.mat)<-c("SB0",paste0("SSB_",current.year),paste0("Bratio_",current.year),"MSY_SPR","F_SPR")
# #RE<-melt((as.matrix(dev.quants)-as.matrix(dev.quants)[,1])/as.matrix(dev.quants)[,1])
# RE<-melt((dev.quants.mat-dev.quants.mat[,1])/dev.quants.mat[,1])[-1:-5,]
# logRE<-melt(log(dev.quants.mat/dev.quants.mat[,1]))[-1:-5,]
# #Get values for plots
# Dev.quants.temp<-as.data.frame(cbind(rownames(dev.quants.mat),dev.quants.mat[,-1]))
# colnames(Dev.quants.temp)<-c("Metric",mod.names[-1])
# Dev.quants.ggplot<-data.frame(melt(Dev.quants.temp,id.vars=c("Metric")),RE[,2:3],logRE[,2:3])
# colnames(Dev.quants.ggplot)<-c("Metric","Model_name","Value","Model_num_plot","RE","Model_num_plot_log","logRE")
# Dev.quants.ggplot$Metric<-factor(Dev.quants.ggplot$Metric,levels=unique(Dev.quants.ggplot$Metric))
# save(Dev.quants.ggplot,file=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("","")
# }
# #Begin plots
# pt.dodge=0.3
# if(plot.figs[1]==1)
# {
# #RE plot
# ggplot(Dev.quants.ggplot,aes(Model_num_plot,RE))+
# geom_point(aes(shape=Metric,color=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$Model_name))+
# #scale_y_continuous(limits=ylims.in[1:2])+
# coord_cartesian(ylim=ylims.in[1:2])+
# theme(axis.text.x = element_text(angle=45,hjust=1,vjust=1),
# legend.text.align = 0,
# panel.grid.minor = element_blank())+
# scale_shape_manual(values=c(15:18,12),
# name ="",
# labels = c(expression(SO[0]),
# as.expression(bquote('SO'[.(current.year)])),
# bquote(frac(SO[.(current.year)],SO[0])),
# expression(MSY[SPR]),
# expression(F[SPR])))+
# scale_color_manual(values=four.colors[1:5],
# name ="",
# labels = c(expression(SO[0]),
# as.expression(bquote('SO'[.(current.year)])),
# bquote(frac(SO[.(current.year)],SO[0])),
# expression(MSY[SPR]),
# expression(F[SPR])))+
# labs(x = sensi_xlab,y = "Relative change")+
# annotate("text",x=anno.x,y=anno.y,label=anno.lab)+
# annotate("text",x=c((model.summaries$n+2),(model.summaries$n+2)),y=c(TRP+0.03,LRP-0.03),label=c("TRP","LRP"),size=c(3,3),color=c("darkgreen","darkred"))+
# geom_vline(xintercept =c(sensi.type.breaks),lty=lty.in)
# ggsave(paste0(Dir,"Sensi_REplot_all.png"))
# #log plot
# ggplot(Dev.quants.ggplot,aes(Model_num_plot,logRE))+
# geom_point(aes(shape=Metric,color=Metric),position=position_dodge(pt.dodge))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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(SO[0]),
# as.expression(bquote('SO'[.(current.year)])),
# bquote(frac(SO[.(current.year)],SO[0])),
# expression(MSY[SPR]),
# expression(F[SPR])))+
# scale_color_manual(values=four.colors[1:5],
# name ="",
# labels = c(expression(SO[0]),
# as.expression(bquote('SO'[.(current.year)])),
# bquote(frac(SO[.(current.year)],SO[0])),
# expression(MSY[SPR]),
# expression(F[SPR])))+
# labs(x = sensi_xlab,y = "Log 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(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(paste0(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(Model_num_plot,RE))+
# geom_point(aes(shape=Metric,color=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$n))+
# #scale_y_continuous(limits=ylims.in[1:2])+
# coord_cartesian(ylim=ylims.in[1:2])+
# theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# panel.grid.minor = element_blank())+
# scale_shape_manual(values=c(16,17),
# name ="",
# labels = c(expression(SO[0]),
# as.expression(bquote('SO'[.(current.year)]))))+
# scale_color_manual(values=four.colors[1:2],
# name ="",
# labels = c(expression(SO[0]),
# as.expression(bquote('SO'[.(current.year)]))))+
# theme(legend.text.align = 0)+
# labs(x = " ",y = " ")+
# annotate("text",x=anno.x,y=anno.y,label=anno.lab)+
# geom_hline(yintercept =0,lwd=0.5,color="gray")+
# geom_vline(xintercept =c(sensi.type.breaks),lty=lty.in)
# Dev.quants.ggplot.Dep<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[3])
# p2<-ggplot(Dev.quants.ggplot.Dep,aes(Model_num_plot,RE))+
# geom_point(aes(color=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(SO[.(current.year)],SO[0]))))+
# 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(Model_num_plot,RE,group=Metric))+
# geom_point(aes(shape=Metric,color=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.text=element_text(size=rel(1)))+
# scale_shape_manual(values=c(16,17),
# name ="",
# labels = expression(MSY,F[SPR]))+
# scale_color_manual(values=four.colors[4:5],
# name ="",
# labels = expression(MSY,F[SPR]))+
# 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<-ggarrange(p1,p2,p3,nrow=3,ncol=1,align="v",heights=c(5,5,8))
# ggsave(paste0(Dir,"Sensi_REplot_SB_Dep_F_MSY.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(Model_num_plot,logRE))+
# geom_point(aes(shape=Metric,color=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$n))+
# #scale_y_continuous(limits=ylims.in[1:2])+
# coord_cartesian(ylim=ylims.in[1:2])+
# theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# panel.grid.minor = element_blank())+
# scale_shape_manual(values=c(16,17),
# name ="",
# labels = c(expression(SO[0]),
# as.expression(bquote('SO'[.(current.year)]))))+
# scale_color_manual(values=four.colors[1:2],
# name ="",
# labels = c(expression(SO[0]),
# as.expression(bquote('SO'[.(current.year)]))))+
# theme(legend.text.align = 0)+
# labs(x = " ",y = " ")+
# annotate("text",x=anno.x,y=anno.y,label=anno.lab)+
# geom_hline(yintercept =0,lwd=0.5,color="gray")+
# geom_vline(xintercept =c(sensi.type.breaks),lty=lty.in)
# Dev.quants.ggplot.Dep<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[3])
# p2<-ggplot(Dev.quants.ggplot.Dep,aes(Model_num_plot,logRE))+
# geom_point(aes(color=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(SO[.(current.year)],SO[0]))))+
# 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(Model_num_plot,logRE,group=Metric))+
# geom_point(aes(shape=Metric,color=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.text=element_text(size=7.5))+
# scale_shape_manual(values=c(16,17),
# name ="",
# labels = expression(MSY[SPR],F[SPR]))+
# scale_color_manual(values=four.colors[4:5],
# name ="",
# labels = expression(MSY[SPR],F[SPR]))+
# 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<-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(paste0(Dir,"Sensi_logREplot_SB_Dep_F_MSY.png"),p4)
# }
# if(plot.figs[2]==1)
# {
# #RE plot
# Dev.quants.ggplot.SB0<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[1])
# ggplot(Dev.quants.ggplot.SB0,aes(Model_num_plot,RE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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(SO[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(paste0(Dir,"Sensi_REplot_SO_0.png"))
# #Log plot
# Dev.quants.ggplot.SB0<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[1])
# ggplot(Dev.quants.ggplot.SB0,aes(Model_num_plot,logRE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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(SO[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(paste0(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$Metric)[2])
# ggplot(Dev.quants.ggplot.SBt,aes(Model_num_plot,RE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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('SO'[.(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(paste0(Dir,"Sensi_REplot_SOcurrent.png"))
# #Log plots
# Dev.quants.ggplot.SBt<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[2])
# ggplot(Dev.quants.ggplot.SBt,aes(Model_num_plot,logRE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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('SO'[.(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(paste0(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$Metric)[3])
# ggplot(Dev.quants.ggplot.Dep,aes(Model_num_plot,RE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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(SO[.(current.year)],SO[0]))))+
# 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(paste0(Dir,"Sensi_REplot_status.png"))
# #Log plots
# Dev.quants.ggplot.Dep<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[3])
# ggplot(Dev.quants.ggplot.Dep,aes(Model_num_plot,logRE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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(SO[.(current.year)],SO[0]))))+
# 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"))+
# 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(paste0(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$Metric)[4])
# ggplot(Dev.quants.ggplot.MSY,aes(Model_num_plot,RE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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 = expression(MSY[SPR]))+
# 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(paste0(Dir,"Sensi_REplot_MSY.png"))
# #Log plots
# Dev.quants.ggplot.MSY<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[4])
# ggplot(Dev.quants.ggplot.MSY,aes(Model_num_plot,logRE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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 = expression(MSY[SPR]))+
# 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(paste0(Dir,"Sensi_logREplot_MSY.png"))
# }
# if(plot.figs[6]==1)
# {
# #RE plots
# Dev.quants.ggplot.FMSY<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[5])
# ggplot(Dev.quants.ggplot.FMSY,aes(Model_num_plot,RE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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 = expression(F[SPR]))+
# 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(paste0(Dir,"Sensi_REplot_FMSY.png"))
# #RE plots
# Dev.quants.ggplot.FMSY<-subset(Dev.quants.ggplot,Metric == unique(Dev.quants.ggplot$Metric)[5])
# ggplot(Dev.quants.ggplot.FMSY,aes(Model_num_plot,logRE))+
# geom_point(aes(color=Metric))+
# geom_rect(aes(xmin=1,xmax=model.summaries$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 = expression(F[SPR]))+
# 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(paste0(Dir,"Sensi_logREplot_FMSY.png"))
# }
# }