In Blue-Matter/MSEtool: Management Strategy Evaluation Toolkit

Implementation Parameters

knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
Pars <- params$Pars
nsim <- Pars$TAESD %>% length()

# ImpList <- list()
# ImpList$TACbias <- array(Pars$Imp$TACFrac, c(nsim, nyears + proyears))  
# ImpList$TACsd <- array(rlnorm((nyears + proyears) * nsim, 
#                      mconv(1, rep(Pars$Imp$TACSD, (nyears + proyears))), 
#                      sdconv(1, rep(Pars$Imp$TACSD, nyears + proyears))), 
#               c(nsim, nyears + proyears))  
# 
# ImpList$TAEbias <- array(Pars$Imp$TAEFrac, c(nsim, nyears + proyears))  
# ImpList$TAEsd <- array(rlnorm((nyears + proyears) * nsim, 
#                      mconv(1, rep(Pars$Imp$TAESD, (nyears + proyears))), 
#                      sdconv(1, rep(Pars$Imp$TAESD, nyears + proyears))), 
#               c(nsim, nyears + proyears)) 
# 
# ImpList$SizeLimFrac <- array(Pars$Imp$SizeLimFrac, c(nsim, nyears + proyears))  
# ImpList$SizeLimSD <- array(rlnorm((nyears + proyears) * nsim, 
#                      mconv(1, rep(Pars$Imp$SizeLimSD, (nyears + proyears))), 
#                      sdconv(1, rep(Pars$Imp$SizeLimSD, nyears + proyears))), 
#               c(nsim, nyears + proyears))

if (params$tabs) {
  cat('### TAC Implementation {.tabset .tabset-fade .tabset-pills}' )
} else {
  cat('### TAC Implementation')
}

Sampled Parameters

Histograms of r nsim simulations of inter-annual variability in TAC implementation error (TACSD) and persistent bias in TAC implementation (TACFrac), with vertical colored lines indicating r nsamp randomly drawn values used in other plots:

par(mfrow=c(1,2))
hist2(Pars$Imp$TACSD, main="TACSD", col=params$plotPars$col, axes=params$plotPars$axes,
      breaks=params$plotPars$breaks, cex.main=params$plotPars$cex.main)
abline(v=Pars$Imp$TACSD[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1) 

hist2(Pars$Imp$TACFrac, main="TACFrac", col=params$plotPars$col, axes=params$plotPars$axes,
      breaks=params$plotPars$breaks, cex.main=params$plotPars$cex.main)
abline(v=Pars$Imp$TACFrac[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1)

Time-Series

Time-series plots of r nsim samples of TAC implementation error by year:

par(mfrow=c(1,1), oma=c(3,3,1,1), mar=c(1,1,1,1))
years <- seq(nyears+1, to=nyears+proyears,1)

ylim <- c(0, max(Pars$Imp$TAC_y[params$its, ]))
matplot(years, t(Pars$Imp$TAC_y[params$its, ]),
        type="l", lty=1, bty="l", main="TAC discrepancy by Year", 
        lwd=params$plotPars$lwd, ylab="Observed/Real", xlab="Years", las=1, xpd=NA,
        ylim=ylim)
abline(v=0, col="darkgray", lty=2)
abline(h=1, col="darkgray", lty=2)

if (params$tabs) {
  cat('### TAE Implementation {.tabset .tabset-fade .tabset-pills}' )
} else {
  cat('### TAE Implementation')
}

Sampled Parameters

Histograms of r nsim simulations of inter-annual variability in TAE implementation error (TAESD) and persistent bias in TAC implementation (TAEFrac), with vertical colored lines indicating r nsamp randomly drawn values used in other plots:

par(mfrow=c(1,2))
hist2(Pars$Imp$TAESD, main="TAESD", col=params$plotPars$col, axes=params$plotPars$axes,
      breaks=params$plotPars$breaks, cex.main=params$plotPars$cex.main)
abline(v=Pars$Imp$TAESD[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1) 

hist2(Pars$Imp$TAEFrac, main="TAEFrac", col=params$plotPars$col, axes=params$plotPars$axes,
      breaks=params$plotPars$breaks, cex.main=params$plotPars$cex.main)
abline(v=Pars$Imp$TAEFrac[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1)

Time-Series

Time-series plots of r nsim samples of TAE implementation error by year:

par(mfrow=c(1,1), oma=c(3,3,1,1), mar=c(1,1,1,1))
years <- seq(nyears+1, to=nyears+proyears,1)

ylim <- c(0, max(Pars$Imp$E_y[params$its, ]))
matplot(years, t(Pars$Imp$E_y[params$its, ]),
        type="l", lty=1, bty="l", main="TAE discrepancy by Year", 
        lwd=params$plotPars$lwd, ylab="Observed/Real", xlab="Years", las=1, xpd=NA,
        ylim=ylim)
abline(v=0, col="darkgray", lty=2)
abline(h=1, col="darkgray", lty=2)

if (params$tabs) {
  cat('### Size Limit Implementation {.tabset .tabset-fade .tabset-pills}' )
} else {
  cat('### Size Limit Implementation')
}

Sampled Parameters

Histograms of r nsim simulations of inter-annual variability in size limit implementation error (SizeLimSD) and persistent bias in size limit implementation (SizeLimFrac), with vertical colored lines indicating r nsamp randomly drawn values used in other plots:

par(mfrow=c(1,2))
hist2(Pars$Imp$SizeLimSD, main="SizeLimSD", col=params$plotPars$col, axes=params$plotPars$axes,
      breaks=params$plotPars$breaks, cex.main=params$plotPars$cex.main)
abline(v=Pars$Imp$SizeLimSD[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1) 

hist2(Pars$Imp$SizeLimFrac, main="SizeLimFrac", col=params$plotPars$col, axes=params$plotPars$axes,
      breaks=params$plotPars$breaks, cex.main=params$plotPars$cex.main)
abline(v=Pars$Imp$SizeLimFrac[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1)

Time-Series

Time-series plots of r nsim samples of Size Limit implementation error by year:

par(mfrow=c(1,1), oma=c(3,3,1,1), mar=c(1,1,1,1))
years <- seq(nyears+1, to=nyears+proyears,1)

ylim <- c(0, max(Pars$Imp$SizeLim_y[params$its, ]))
matplot(years, t(Pars$Imp$SizeLim_y[params$its, ]),
        type="l", lty=1, bty="l", main="Size Limit discrepancy by Year", 
        lwd=params$plotPars$lwd, ylab="Observed/Real", xlab="Years", las=1, xpd=NA,
        ylim=ylim)
abline(v=0, col="darkgray", lty=2)
abline(h=1, col="darkgray", lty=2)