create.projection.summary = function( p, pFB, rescale.results=F ) {
outdata = NULL
for (rr in c(1:p$nregions)) {
td = pFB[ which(pFB$region==p$regions[rr]) , ]
td0 = td[ which(as.character(td$vars)=="fb.prefishery"),] ; td0$fbpre = td0$val; td0$val=NULL; td0$vars=NULL
td1 = td[ which(as.character(td$vars)=="error"),] ; td1$error = td1$val; td1$val=NULL; td1$vars=NULL
td2 = td[ which(as.character(td$vars)=="landings"),] ; td2$landings = td2$val; td2$val=NULL; td2$vars=NULL
td = merge( td0, td1, by=c("yr","region","ER") )
td = merge( td, td2, by=c("yr","region","ER") )
td$ER = as.factor(td$ER)
scale.factor = 1000 # convert to X 1000 t
if ( rescale.results ) scale.factor = td[ which( td$yr==p$start.projection.year ), "fbpre"] [1]
td$fbpre = td$fbpre / scale.factor
td$error = td$error / scale.factor
td$landings = td$landings / scale.factor
td = td[ order(td$region, td$ER, td$yr) , ]
td$ub = td$fbpre + td$error # already 2SD
td$lb = td$fbpre - td$error
i.bigerror = which( td$error >= td$fbpre)
td$lb[ i.bigerror ] = 0
td$ub[ i.bigerror ] = 0
td$region = p$regions[rr]
outdata = rbind(outdata, td)
}
return(outdata)
}
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