In DLMtool/DLMtool: Data-Limited Methods Toolkit

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

dd <- params$Pars$M_ageArray %>% dim()
nsim <- dd[1]
maxage <- dd[2]

nsamp <- length(params$its)

if (Pars$SRrel[1] == 1) SR <- "Beverton-Holt"
if (Pars$SRrel[1] == 2) SR <- "Ricker"

Sampled Parameters

Histograms of r nsim simulations of steepness (h), recruitment process error (Perr) and auto-correlation (AC) for the r SR stock-recruitment relationship, with vertical colored lines indicating r nsamp randomly drawn values used in other plots:

par(mfrow=c(1,3))
hist2(Pars$hs, col=params$plotPars$col, axes=params$plotPars$axes, main="Steepness (h)", breaks=params$plotPars$breaks)
abline(v=Pars$hs[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1)  

hist2(Pars$procsd, col=params$plotPars$col, axes=params$plotPars$axes, main="Recruitment process error (Perr)", breaks=params$plotPars$breaks)
abline(v=Pars$procsd[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1) 

hist2(Pars$AC, col=params$plotPars$col, axes=params$plotPars$axes, main="Auto-correlation (AC)", breaks=params$plotPars$breaks)
abline(v=Pars$AC[params$its], col=1:nsamp, lwd=params$plotPars$lwd)
axis(side=1)

Time-Series

years <- c(seq(-nyears+1, 0, by=1), seq(1, proyears,1))

matplot(years, t(Pars$Perr_y[params$its,(maxage):(maxage+length(years)-1)]), type="l", bty="l", main="Rec Devs by Year", 
        lwd=params$plotPars$lwd, lty=1, ylab="Rec Devs", xlab="Years")
abline(h=1, col="lightgray", lty=2)
abline(v=0, col="lightgray", lty=2)