inst/IP/bycatchCSAS/Lobster/sdmTMBLobster27.r

In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes

#Make sure to run 0.RunMeFirst.R and 5.CreateMesh_DataforModellingLFA27.R
rm(aT,be,bspde)
u = readRDS(file='results/dataForLFA27.rds')

aT = u$data
bspde = u$grid
be = u$preds

 fit = sdmTMB(LegalWt~
 				s(lZ,k=5),
 				time='WOS',
 				data=aT,
 				mesh=bspde, 
 				family=tweedie(link='log'),
 				spatial='on',
 				spatialtemporal='ar1'
 				)

g = predict(fit,newdata=be,nsim=50)
g1 = fit$family$linkinv(g)

be$pred = apply(g1,1,median)
be$sd = apply(g1,1,sd)
be$lQ = apply(g1,1,quantile,0.25)
be$uQ = apply(g1,1,quantile,0.75)


saveRDS(list(fit,be),file='lobstersdmTMB27.rds')
if(reload){
	r = readRDS(file='lobstersdmTMB27.rds')
	fit=r[[1]]
	be=r[[2]]
}

gsf = st_as_sf(be,coords = c("X","Y"),crs=32620,remove=F)


rL = readRDS(file.path( project.datadirectory("bio.lobster"), "data","maps","LFAPolysSF.rds"))
			rL = rL[rL$LFA==27,]
			st_crs(rL) <- 4326
			crs_utm20 <- 32620
			rL <- suppressWarnings(suppressMessages(
  					st_crop(rL,
          			c(xmin = -62, ymin = 45.5, xmax = -58, ymax = 47.5))))
			rL <- st_transform(rL, crs_utm20)


png('Figures/ModelOutput/lobstersdmTMB27.png')
mm = c(0.0,max(gsf$pred))
ggplot(subset(gsf,WOS %in% 1:9)) +
			geom_sf(aes(fill=pred,color=pred)) + 
			facet_wrap(~WOS)+
			scale_fill_viridis_c(trans='sqrt',limits=mm) +
			scale_color_viridis_c(trans='sqrt',limits=mm) +
			geom_sf(data=rL,size=1,color='black',fill=NA)+
			
				theme( axis.ticks.x = element_blank(),
        		   axis.text.x = element_blank(),
				   axis.title.x = element_blank(),
				   axis.ticks.y = element_blank(),
        		   axis.text.y = element_blank(),
        		   axis.title.y = element_blank()
        		   ) +
 			coord_sf()
dev.off()

ag = aggregate(cbind(pred,lQ,uQ)~SID+PID+WOS,data=be,FUN=median)
#ag = aggregate(cbind(pred)~SID+PID+WOS,data=be,FUN=median)

ef = readRDS('results/BumpedUpEffortByGridNUM.rds')
ef = subset(ef,LFA %in% 27)
ef = aggregate(cbind(BTTH, BlTH,BuTH)~GRID_NUM+WOS+LFA,data=ef,FUN=mean)
names(ef)[c(1,3)] = c('SID','PID')

ff = merge(ag,ef)

ff$L = ff$pred*ff$BTTH
ff$Ll = ff$lQ*ff$BTTH
ff$Lu = ff$uQ*ff$BTTH

L = aggregate(cbind(L,Ll,Lu)~PID,data=ff,FUN=sum)

#residual plots from slips v predicted to show gaps

el = readRDS('results/LandingsByGridNUM.rds')
names(el)[c(1,3)] = c('SID','PID')
el = aggregate(WEIGHT_KG~SID+WOS+PID,data=el,FUN=mean)

fl = merge(ff,el)
fl$err = (fl$L-fl$WEIGHT_KG)/(fl$WEIGHT_KG)
fl$Rediduals=fl$L-fl$WEIGHT_KG

saveRDS(fl,file='results/LandingsPredictedActual27.rds')