inst/scripts/99.snowcrab_size_structured.r
In jae0/bio.snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
# Snow crab size structured model (via stan --- retired)
require(bio.snowcrab) # loadfunctions("bio.snowcrab")
year.assessment = 2021
yrs = 1999:year.assessment
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=2526 )
loadfunctions("bio.snowcrab")
pS = snowcrab_parameters( project_class="size_structured", yrs=yrs,
selection = list( type = "number", biologicals=list( spec_bio=spec_bio ) )
)
M = snowcrab.db( p=pS, DS="set.complete" )
setDT(M)
# must check why we have dups here
i = which(duplicated(M[,1:10]))
M = M[-i,]
s_male = c("mi123.no", "mi4.no", "mi5.no", "mi6.no", "mi7.no", "mi8.no", "mi9.no", "mi10.no", "mi11.no", "mi12.no", "ma9.no", "ma10.no", "ma11.no", "ma12.no", "ma13.no" )
i_male = c( 3:12, 9:13 ) # instar
m_male = c( rep(0, 10), rep(1, 5) ) # maturity
# convert to numbers
males = M[ , c("uid", s_male ) ]
males[, s_male] = males[, s_male] * M$sa
m = data.table::melt( setDT(males), id.vars="uid", measure.vars=s_male, variable.factor=FALSE )
setnames(m, "value", "n")
setnames(m, "variable", "stage")
# plot(xtabs(n~stage, m)[s_male])
m$instar = i_male[ match( m$stage, s_male ) ]
m$maturity = m_male[ match( m$stage, s_male ) ]
tot = m[, .(ntot=sum(n)), by=.(uid, instar) ]
mat = m[ maturity==1, .(nmat=sum(n)), by=.(uid, instar)]
cnts = mat[ tot, on=.(uid, instar) ]
cnts$fraction_mature = cnts$nmat / cnts$ntot
cnts = cnts[ M[,c("uid", "yr")], on=.(uid) ]
mx = dcast( cnts[,.(ntot=sum(ntot, na.rm=TRUE)), by=.( instar, yr) ], instar ~ yr, value.var="ntot" )
mtm = dcast( cnts[,.(tm=sum(fraction_mature*ntot, na.rm=TRUE)), by=.( instar, yr) ], instar ~ yr, value.var="tm" )
mm = as.matrix(mtm / mx)[ , 2:26]
image( as.matrix( round( mm, 3) ) )
plot(rowMeans(mm))
s_female = c("fi1234.no", "fi5.no", "fi6.no", "fi7.no", "fi8.no", "fi9.no", "fi10.no", "fa7.no", "fa8.no", "fa9.no", "fa10.no" )
females = M[ , c("uid", s_female ) ]
females[, s_female] = females[, s_female] * M$sa
f = data.table::melt( setDT(females), id.vars="uid", measure.vars=s_female, variable.factor=FALSE )
setnames(f, "value", "n")
setnames(f, "variable", "stage")
plot(xtabs(n~stage, f)[s_female])
# some survey timestamps extend into January (e.g., 2020) force them to be part of the correct "survey year", i.e., "yr"
i = which(lubridate::month(M$timestamp)==1)
if (length(i) > 0) M$timestamp[i] = M$timestamp[i] - lubridate::duration(month=1)
M$tiyr = lubridate::decimal_date(M$timestamp)
M$dyear[ M$dyear > 1] = 0.99 # a survey year can run into the next year, cap the seasonal compenent at the calendar year for modellng
# reduce size
M = M[ which( M$lon > p$corners$lon[1] & M$lon < p$corners$lon[2] & M$lat > p$corners$lat[1] & M$lat < p$corners$lat[2] ), ]
# levelplot(z.mean~plon+plat, data=M, aspect="iso")
if (areal_units) {
# polygon structure:: create if not yet made
# for (au in c("cfanorth", "cfasouth", "cfa4x", "cfaall" )) plot(polygon_managementareas( species="snowcrab", au))
xydata = snowcrab.db( p=pN, DS="areal_units_input", redo=TRUE )
xydata = snowcrab.db( p=pN, DS="areal_units_input" )
sppoly = areal_units( p=pN, xydata=xydata[ which(xydata$yr %in% pN$yrs), ], redo=TRUE, verbose=TRUE ) # create constrained polygons with neighbourhood as an attribute
sppoly=areal_units( p=pN )
plot(sppoly["npts"])
additional_features = snowcrab_mapping_features(pN) # for mapping below
figure_area_based_extraction_from_carstm(DS="temperature", year.assessment ) # can only do done once we have an sppoly for snow crab
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly, redo=TRUE ) # will redo if not found
}
sppoly=areal_units( p=pN )
# ------------------------------------------------
# Part 2 -- spatiotemporal statistical model
if ( spatiotemporal_model ) {
# total numbers
sppoly = areal_units( p=pN )
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly ) # will redo if not found
io = which(M$tag=="observations")
ip = which(M$tag=="predictions")
# subset to positive definite data (for number and size)
isubset =1:nrow(M)
ipositive = unique( c( which( M$totno > 0), ip ) )
if (grepl("hurdle", pN$carstm_model_label)) {
isubset = ipositive
hist(M$data_offset[ipositive])
}
# number
fit = NULL; gc()
fit = carstm_model( p=pN, data=M[ isubset, ], sppoly=sppoly,
posterior_simulations_to_retain="predictions", improve.hyperparam.estimates=TRUE
)
# mean size
fit = NULL; gc()
fit = carstm_model( p=pW, data=M[ isubset, ], sppoly = sppoly,
posterior_simulations_to_retain="predictions", improve.hyperparam.estimates=TRUE,
control.inla = list( strategy="laplace", int.strategy="eb" )
)
# model pa using all data
fit = NULL; gc()
fit = carstm_model( p=pH, data=M, sppoly=sppoly,
posterior_simulations_to_retain="predictions", improve.hyperparam.estimates=TRUE,
# control.family=list(control.link=list(model="logit")), # default
control.inla = list( strategy="laplace", int.strategy="eb" )
)
if ( number ) {
p = pN
outputdir = file.path( p$modeldir, p$carstm_model_label, "predicted.numerical.densities" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
fn_root_prefix = "Predicted_numerical_abundance"
title= paste( snowcrab_filter_class, "Predicted numerical density (no./m^2) - persistent spatial effect" )
}
if ( meansize) {
p = pW
outputdir = file.path( p$modeldir, p$carstm_model_label, "predicted.meansize" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
fn_root_prefix = "Predicted_meansize"
title= paste( snowcrab_filter_class, "Predicted meansize (kg) - persistent spatial effect" )
}
if ( presence_absence ) {
p = pH
outputdir = file.path( p$modeldir, p$carstm_model_label, "predicted.presence_absence" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
fn_root_prefix = "Predicted_presence_absence"
title= paste( snowcrab_filter_class, "Predicted habitat probability - persistent spatial effect")
}
if (0) {
# extract results
fit = carstm_model( p=p, DS="modelled_fit", sppoly = sppoly ) # extract currently saved model fit
fit$summary$dic$dic
fit$summary$dic$p.eff
plot(fit)
plot(fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
plot( fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
plot( fit$marginals.hyperpar$"Phi for space_time", type="l") # posterior distribution of phi nonspatial dominates
plot( fit$marginals.hyperpar$"Precision for space_time", type="l")
plot( fit$marginals.hyperpar$"Precision for setno", type="l")
fit = NULL
}
oeffdir = file.path(p$data_root, p$carstm_model_label, "figures")
fn_root_prefix = "Predicted_numerical_abundance"
carstm_plot_marginaleffects( p, oeffdir, fn_root_prefix )
# maps of some of the results
outputdir = file.path(p$data_root, p$carstm_model_label, "maps" )
carstm_plot_map( p, outputdir, fn_root_prefix , additional_features, toplot="random_spatial", probs=c(0.025, 0.975) )
carstm_plot_map( p, outputdir, fn_root_prefix , additional_features, toplot="predictions", probs=c(0.1, 0.9))
} # end spatiotemporal model
# ----------------------
assimilate_numbers_and_size = TRUE
if (assimilate_numbers_and_size ) {
# wgts_max = 1.1 # kg, hard upper limit
# N_max = NULL
# # quantile( M$totno[ipositive]/M$data_offset[ipositive], probs=0.95, na.rm=TRUE )
# posterior sims
sims = carstm_posterior_simulations( pN=pN, pW=pW, pH=pH, pa_threshold=0.05, qmax=0.99 )
sims = sims / 10^6 # 10^6 kg -> kt;; kt/km^2
SM = aggregate_simulations(
sims=sims,
sppoly=sppoly,
fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ),
yrs=pN$yrs,
method="sum",
redo=TRUE
)
RES= SM$RES
# RES = aggregate_simulations( fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ) )$RES
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "aggregated_biomass_timeseries" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
( fn = file.path( outputdir, "cfa_all.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfaall ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfaall_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfaall_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_south.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfasouth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfasouth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfasouth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_north.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfanorth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfanorth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfanorth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_4x.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfa4x ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfa4x_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfa4x_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
# map it ..mean density
sppoly = areal_units( p=pN ) # to reload
vn = paste("biomass", "predicted", sep=".")
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "predicted_biomass_densitites" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
B = apply( sims, c(1,2), mean )
brks = pretty( log10( quantile( B[], probs=c(0.05, 0.95) )* 10^6) )
additional_features = snowcrab_mapping_featuresresresres(pN) # for mapping below
for (i in 1:length(pN$yrs) ){
y = as.character( pN$yrs[i] )
sppoly[,vn] = log10( B[,y]* 10^6 )
outfilename = file.path( outputdir , paste( "biomass", y, "png", sep=".") )
plt = carstm_map(
sppoly=sppoly,
vn=vn,
breaks=brks,
additional_features=additional_features,
title=paste( "log_10( Predicted biomass density; kg/km^2 )", y ),
palette="-RdYlBu",
plot_elements=c( "compass", "scale_bar", "legend" ),
outfilename=outfilename
)
plt
}
} # end assimilate size and numbers
##########
# this part is only relevent for R0
fishery_model = FALSE
if (fishery_model) {
# you need a stan installation on your system as well (outside of R), and the R-interface "cmdstanr":
# install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
require(cmdstanr)
loadfunctions("bio.snowcrab")
# choose:
pN$fishery_model_label = "stan_surplus_production_2022_model_qc_uniform"
# pN$fishery_model_label = "stan_surplus_production_2022_model_variation1_wider_qc_uniform"
# pN$fishery_model_label = "stan_surplus_production_2022_model_variation1_wider_qc_normal"
# pN$fishery_model_label = "stan_surplus_production_2022_model_qc_cauchy_wider"
# pN$fishery_model_label = "stan_surplus_production_2022_model_qc_beta" # qc_beta postive definite
# pN$fishery_model_label = "stan_surplus_production_2022_model_qc_cauchy"
# pN$fishery_model_label = "stan_surplus_production_2019_model"
pN$fishery_model = fishery_model( DS = "logistic_parameters", p=pN, tag=pN$fishery_model_label )
to_look = c("K", "r", "q", "qc", "log_lik" )
if ( model_version=="framework_2019" ) {
# this is to create results for reviewers in 2022 that wanted a comparison with previous methods .. can be deleted in future
# bring in unscaled abundance index
a = fishery_model( DS="data_aggregated_timeseries", p=pN )
a$IOA[ !is.finite(a$IOA) ] = 0
pN$fishery_model$fmdata$IOA = a$IOA
to_look = c("K", "r", "q", "log_lik" )
}
# str( pN$fishery_model)
pN$fishery_model$stancode = stan_initialize( stan_code=fishery_model( p=pN, DS=pN$fishery_model_label ) )
pN$fishery_model$stancode$compile()
fit = pN$fishery_model$stancode$sample(
data=pN$fishery_model$fmdata,
iter_warmup = 4000,
iter_sampling = 4000,
seed = 45678,
chains = 3,
parallel_chains = 3, # The maximum number of MCMC chains to run in parallel.
max_treedepth = 18,
adapt_delta = 0.99,
refresh = 1000
)
fit$summary(to_look)
require(loo)
waic(fit$draws("log_lik"))
loo(fit$draws("log_lik"))
# save fit and get draws
res = fishery_model( p=pN, DS="logistic_model", tag=pN$fishery_model_label, fit=fit ) # from here down are params for cmdstanr::sample()
if (0) {
# reload saved fit and results
load(pN$fishery_model$fnres)
fit = aegis::read_write_fast(pN$fishery_model$fnfit)
}
# frequency density of key parameters
fishery_model( DS="plot", vname="K", res=res )
fishery_model( DS="plot", vname="r", res=res )
fishery_model( DS="plot", vname="q", res=res, xrange=c(0.5, 2.5))
fishery_model( DS="plot", vname="qc", res=res, xrange=c(-1, 1))
fishery_model( DS="plot", vname="FMSY", res=res )
# timeseries
fishery_model( DS="plot", type="timeseries", vname="biomass", res=res )
fishery_model( DS="plot", type="timeseries", vname="fishingmortality", res=res)
# Harvest control rules
fishery_model( DS="plot", type="hcr", vname="default", res=res )
# Summary table of mean values for inclusion in document
( qs = apply( res$mcmc$K[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) ) # carrying capactiy
( qs = apply( res$mcmc$FMSY[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) ) # FMSY
biomass = as.data.table( fit$summary("B") )
np = year.assessment+c(1:pN$fishery_model$fmdata$M)
biomass$yr = rep( c(pN$yrs, np ), 3)
nt = pN$fishery_model$fmdata$N +pN$fishery_model$fmdata$M
biomass$region = c( rep("cfanorth", nt), rep("cfasouth", nt), rep("cfa4x", nt) )
(biomass)
NN = res$pN$fishery_model$fmdata$N
# densities of biomass estimates for the year.assessment
( qs = apply( res$mcmc$B[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of biomass estimates for the previous year
( qs = apply( res$mcmc$B[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of F in assessment year
( qs = apply( res$mcmc$F[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN,], 2, mean ) )
# densities of F in previous year
( qs = apply( res$mcmc$F[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN-1,], 2, mean ) )
if (0) {
# obsolete:
fishery_model( DS="plot", vname="bosd", res=res )
fishery_model( DS="plot", vname="bpsd", res=res )
fishery_model( DS="plot", type="hcr", vname="simple", res=res )
# biomass.summary.table()
fit = fishery_model( p=pN, DS="fit", tag=pN$fishery_model_label ) # to load samples (results)
fit$summary(c("K", "r", "q", "qc"))
print( fit, max_rows=30 )
fit$cmdstan_diagnose()
fit$cmdstan_summary()
# testing other samplers and optimizsers ... faster , good for debugging
# (penalized) maximum likelihood estimate (MLE)
fit_mle = pN$fishery_model$stancode$optimize(data =pN$fishery_model$fmdata, seed = 123)
fit_mle$summary( to_look )
u = stan_extract( as_draws_df(fit_mle$draws() ) )
# mcmc_hist(fit$draws("K")) + vline_at(fit_mle$mle(), size = 1.5)
# # Variational Bayes
fit_vb = pN$fishery_model$stancode$variational( data =pN$fishery_model$fmdata, seed = 123, output_samples = 4000)
fit_vb$summary(to_look)
fit_vb$cmdstan_diagnose()
fit_vb$cmdstan_summary()
u = stan_extract( as_draws_df(fit_vb$draws() ) )
# bayesplot_grid(
# mcmc_hist(fit$draws("K"), binwidth = 0.025),
# mcmc_hist(fit_vb$draws("K"), binwidth = 0.025),
# titles = c("Posterior distribution from MCMC", "Approximate posterior from VB")
# )
# color_scheme_set("gray")
# mcmc_dens(fit$draws("K"), facet_args = list(nrow = 3, labeller = ggplot2::label_parsed ) ) + facet_text(size = 14 )
# mcmc_hist( fit$draws("K"))
# obtain mcmc samples from vb solution
res_vb = fishery_model(
DS="logistic_model",
p=pN,
tag=pN$fishery_model_label,
fit = fit_vb
)
names(res_vb$mcmc)
# other diagnostics
# fishery_model( DS="plot", type="diagnostic.errors", res=res )
# fishery_model( DS="plot", type="diagnostic.phase", res=res )
NN = res$pN$fishery_model$fmdata$N
# bosd
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$bosd[,i] ), main="")
( qs = apply( res$mcmc$bosd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# bpsd
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$bpsd[,i] ), main="")
( qs = apply( res$mcmc$bpsd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# rem_sd
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$rem_sd[,i] ), main="")
( qs = apply( res$mcmc$rem_sd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# qc
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$qc[,i] ), main="")
( qs = apply( res$mcmc$qc[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# b0
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$b0[,i] ), main="")
( qs = apply( res$mcmc$b0[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# K
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$K[,i] ), main="")
( qs = apply( res$mcmc$K[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# R
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$r[,i] ), main="")
( qs = apply( res$mcmc$r[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# q
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$q[,i] ), main="")
( qs = apply( res$mcmc$q[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# FMSY
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$FMSY[,i] ), main="")
( qs = apply( res$mcmc$FMSY[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of biomass estimates for the year.assessment
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$B[,NN,i] ), main="")
( qs = apply( res$mcmc$B[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of biomass estimates for the previous year
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density( res$mcmc$B[,NN-1,i] ), main="")
( qs = apply( res$mcmc$B[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of F in assessment year
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density( res$mcmc$F[,NN,i] ), xlim=c(0.01, 0.6), main="")
( qs = apply( res$mcmc$F[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN,], 2, mean ) )
# densities of F in previous year
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density( res$mcmc$F[,NN-1,i] ), xlim=c(0.01, 0.6), main="")
( qs = apply( res$mcmc$F[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN-1,], 2, mean ) )
# F for table ---
summary( res$mcmc$F, median)
} # end skip
} # end fishery model
# end
jae0/bio.snowcrab documentation built on Nov. 6, 2024, 10:10 p.m.
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# Snow crab size structured model (via stan --- retired)
require(bio.snowcrab) # loadfunctions("bio.snowcrab")
year.assessment = 2021
yrs = 1999:year.assessment
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=2526 )
loadfunctions("bio.snowcrab")
pS = snowcrab_parameters( project_class="size_structured", yrs=yrs,
selection = list( type = "number", biologicals=list( spec_bio=spec_bio ) )
)
M = snowcrab.db( p=pS, DS="set.complete" )
setDT(M)
# must check why we have dups here
i = which(duplicated(M[,1:10]))
M = M[-i,]
s_male = c("mi123.no", "mi4.no", "mi5.no", "mi6.no", "mi7.no", "mi8.no", "mi9.no", "mi10.no", "mi11.no", "mi12.no", "ma9.no", "ma10.no", "ma11.no", "ma12.no", "ma13.no" )
i_male = c( 3:12, 9:13 ) # instar
m_male = c( rep(0, 10), rep(1, 5) ) # maturity
# convert to numbers
males = M[ , c("uid", s_male ) ]
males[, s_male] = males[, s_male] * M$sa
m = data.table::melt( setDT(males), id.vars="uid", measure.vars=s_male, variable.factor=FALSE )
setnames(m, "value", "n")
setnames(m, "variable", "stage")
# plot(xtabs(n~stage, m)[s_male])
m$instar = i_male[ match( m$stage, s_male ) ]
m$maturity = m_male[ match( m$stage, s_male ) ]
tot = m[, .(ntot=sum(n)), by=.(uid, instar) ]
mat = m[ maturity==1, .(nmat=sum(n)), by=.(uid, instar)]
cnts = mat[ tot, on=.(uid, instar) ]
cnts$fraction_mature = cnts$nmat / cnts$ntot
cnts = cnts[ M[,c("uid", "yr")], on=.(uid) ]
mx = dcast( cnts[,.(ntot=sum(ntot, na.rm=TRUE)), by=.( instar, yr) ], instar ~ yr, value.var="ntot" )
mtm = dcast( cnts[,.(tm=sum(fraction_mature*ntot, na.rm=TRUE)), by=.( instar, yr) ], instar ~ yr, value.var="tm" )
mm = as.matrix(mtm / mx)[ , 2:26]
image( as.matrix( round( mm, 3) ) )
plot(rowMeans(mm))
s_female = c("fi1234.no", "fi5.no", "fi6.no", "fi7.no", "fi8.no", "fi9.no", "fi10.no", "fa7.no", "fa8.no", "fa9.no", "fa10.no" )
females = M[ , c("uid", s_female ) ]
females[, s_female] = females[, s_female] * M$sa
f = data.table::melt( setDT(females), id.vars="uid", measure.vars=s_female, variable.factor=FALSE )
setnames(f, "value", "n")
setnames(f, "variable", "stage")
plot(xtabs(n~stage, f)[s_female])
# some survey timestamps extend into January (e.g., 2020) force them to be part of the correct "survey year", i.e., "yr"
i = which(lubridate::month(M$timestamp)==1)
if (length(i) > 0) M$timestamp[i] = M$timestamp[i] - lubridate::duration(month=1)
M$tiyr = lubridate::decimal_date(M$timestamp)
M$dyear[ M$dyear > 1] = 0.99 # a survey year can run into the next year, cap the seasonal compenent at the calendar year for modellng
# reduce size
M = M[ which( M$lon > p$corners$lon[1] & M$lon < p$corners$lon[2] & M$lat > p$corners$lat[1] & M$lat < p$corners$lat[2] ), ]
# levelplot(z.mean~plon+plat, data=M, aspect="iso")
if (areal_units) {
# polygon structure:: create if not yet made
# for (au in c("cfanorth", "cfasouth", "cfa4x", "cfaall" )) plot(polygon_managementareas( species="snowcrab", au))
xydata = snowcrab.db( p=pN, DS="areal_units_input", redo=TRUE )
xydata = snowcrab.db( p=pN, DS="areal_units_input" )
sppoly = areal_units( p=pN, xydata=xydata[ which(xydata$yr %in% pN$yrs), ], redo=TRUE, verbose=TRUE ) # create constrained polygons with neighbourhood as an attribute
sppoly=areal_units( p=pN )
plot(sppoly["npts"])
additional_features = snowcrab_mapping_features(pN) # for mapping below
figure_area_based_extraction_from_carstm(DS="temperature", year.assessment ) # can only do done once we have an sppoly for snow crab
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly, redo=TRUE ) # will redo if not found
}
sppoly=areal_units( p=pN )
# ------------------------------------------------
# Part 2 -- spatiotemporal statistical model
if ( spatiotemporal_model ) {
# total numbers
sppoly = areal_units( p=pN )
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly ) # will redo if not found
io = which(M$tag=="observations")
ip = which(M$tag=="predictions")
# subset to positive definite data (for number and size)
isubset =1:nrow(M)
ipositive = unique( c( which( M$totno > 0), ip ) )
if (grepl("hurdle", pN$carstm_model_label)) {
isubset = ipositive
hist(M$data_offset[ipositive])
}
# number
fit = NULL; gc()
fit = carstm_model( p=pN, data=M[ isubset, ], sppoly=sppoly,
posterior_simulations_to_retain="predictions", improve.hyperparam.estimates=TRUE
)
# mean size
fit = NULL; gc()
fit = carstm_model( p=pW, data=M[ isubset, ], sppoly = sppoly,
posterior_simulations_to_retain="predictions", improve.hyperparam.estimates=TRUE,
control.inla = list( strategy="laplace", int.strategy="eb" )
)
# model pa using all data
fit = NULL; gc()
fit = carstm_model( p=pH, data=M, sppoly=sppoly,
posterior_simulations_to_retain="predictions", improve.hyperparam.estimates=TRUE,
# control.family=list(control.link=list(model="logit")), # default
control.inla = list( strategy="laplace", int.strategy="eb" )
)
if ( number ) {
p = pN
outputdir = file.path( p$modeldir, p$carstm_model_label, "predicted.numerical.densities" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
fn_root_prefix = "Predicted_numerical_abundance"
title= paste( snowcrab_filter_class, "Predicted numerical density (no./m^2) - persistent spatial effect" )
}
if ( meansize) {
p = pW
outputdir = file.path( p$modeldir, p$carstm_model_label, "predicted.meansize" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
fn_root_prefix = "Predicted_meansize"
title= paste( snowcrab_filter_class, "Predicted meansize (kg) - persistent spatial effect" )
}
if ( presence_absence ) {
p = pH
outputdir = file.path( p$modeldir, p$carstm_model_label, "predicted.presence_absence" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
fn_root_prefix = "Predicted_presence_absence"
title= paste( snowcrab_filter_class, "Predicted habitat probability - persistent spatial effect")
}
if (0) {
# extract results
fit = carstm_model( p=p, DS="modelled_fit", sppoly = sppoly ) # extract currently saved model fit
fit$summary$dic$dic
fit$summary$dic$p.eff
plot(fit)
plot(fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
plot( fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )
plot( fit$marginals.hyperpar$"Phi for space_time", type="l") # posterior distribution of phi nonspatial dominates
plot( fit$marginals.hyperpar$"Precision for space_time", type="l")
plot( fit$marginals.hyperpar$"Precision for setno", type="l")
fit = NULL
}
oeffdir = file.path(p$data_root, p$carstm_model_label, "figures")
fn_root_prefix = "Predicted_numerical_abundance"
carstm_plot_marginaleffects( p, oeffdir, fn_root_prefix )
# maps of some of the results
outputdir = file.path(p$data_root, p$carstm_model_label, "maps" )
carstm_plot_map( p, outputdir, fn_root_prefix , additional_features, toplot="random_spatial", probs=c(0.025, 0.975) )
carstm_plot_map( p, outputdir, fn_root_prefix , additional_features, toplot="predictions", probs=c(0.1, 0.9))
} # end spatiotemporal model
# ----------------------
assimilate_numbers_and_size = TRUE
if (assimilate_numbers_and_size ) {
# wgts_max = 1.1 # kg, hard upper limit
# N_max = NULL
# # quantile( M$totno[ipositive]/M$data_offset[ipositive], probs=0.95, na.rm=TRUE )
# posterior sims
sims = carstm_posterior_simulations( pN=pN, pW=pW, pH=pH, pa_threshold=0.05, qmax=0.99 )
sims = sims / 10^6 # 10^6 kg -> kt;; kt/km^2
SM = aggregate_simulations(
sims=sims,
sppoly=sppoly,
fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ),
yrs=pN$yrs,
method="sum",
redo=TRUE
)
RES= SM$RES
# RES = aggregate_simulations( fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ) )$RES
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "aggregated_biomass_timeseries" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
( fn = file.path( outputdir, "cfa_all.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfaall ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfaall_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfaall_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_south.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfasouth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfasouth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfasouth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_north.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfanorth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfanorth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfanorth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_4x.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfa4x ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfa4x_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfa4x_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
# map it ..mean density
sppoly = areal_units( p=pN ) # to reload
vn = paste("biomass", "predicted", sep=".")
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "predicted_biomass_densitites" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
B = apply( sims, c(1,2), mean )
brks = pretty( log10( quantile( B[], probs=c(0.05, 0.95) )* 10^6) )
additional_features = snowcrab_mapping_featuresresresres(pN) # for mapping below
for (i in 1:length(pN$yrs) ){
y = as.character( pN$yrs[i] )
sppoly[,vn] = log10( B[,y]* 10^6 )
outfilename = file.path( outputdir , paste( "biomass", y, "png", sep=".") )
plt = carstm_map(
sppoly=sppoly,
vn=vn,
breaks=brks,
additional_features=additional_features,
title=paste( "log_10( Predicted biomass density; kg/km^2 )", y ),
palette="-RdYlBu",
plot_elements=c( "compass", "scale_bar", "legend" ),
outfilename=outfilename
)
plt
}
} # end assimilate size and numbers
##########
# this part is only relevent for R0
fishery_model = FALSE
if (fishery_model) {
# you need a stan installation on your system as well (outside of R), and the R-interface "cmdstanr":
# install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
require(cmdstanr)
loadfunctions("bio.snowcrab")
# choose:
pN$fishery_model_label = "stan_surplus_production_2022_model_qc_uniform"
# pN$fishery_model_label = "stan_surplus_production_2022_model_variation1_wider_qc_uniform"
# pN$fishery_model_label = "stan_surplus_production_2022_model_variation1_wider_qc_normal"
# pN$fishery_model_label = "stan_surplus_production_2022_model_qc_cauchy_wider"
# pN$fishery_model_label = "stan_surplus_production_2022_model_qc_beta" # qc_beta postive definite
# pN$fishery_model_label = "stan_surplus_production_2022_model_qc_cauchy"
# pN$fishery_model_label = "stan_surplus_production_2019_model"
pN$fishery_model = fishery_model( DS = "logistic_parameters", p=pN, tag=pN$fishery_model_label )
to_look = c("K", "r", "q", "qc", "log_lik" )
if ( model_version=="framework_2019" ) {
# this is to create results for reviewers in 2022 that wanted a comparison with previous methods .. can be deleted in future
# bring in unscaled abundance index
a = fishery_model( DS="data_aggregated_timeseries", p=pN )
a$IOA[ !is.finite(a$IOA) ] = 0
pN$fishery_model$fmdata$IOA = a$IOA
to_look = c("K", "r", "q", "log_lik" )
}
# str( pN$fishery_model)
pN$fishery_model$stancode = stan_initialize( stan_code=fishery_model( p=pN, DS=pN$fishery_model_label ) )
pN$fishery_model$stancode$compile()
fit = pN$fishery_model$stancode$sample(
data=pN$fishery_model$fmdata,
iter_warmup = 4000,
iter_sampling = 4000,
seed = 45678,
chains = 3,
parallel_chains = 3, # The maximum number of MCMC chains to run in parallel.
max_treedepth = 18,
adapt_delta = 0.99,
refresh = 1000
)
fit$summary(to_look)
require(loo)
waic(fit$draws("log_lik"))
loo(fit$draws("log_lik"))
# save fit and get draws
res = fishery_model( p=pN, DS="logistic_model", tag=pN$fishery_model_label, fit=fit ) # from here down are params for cmdstanr::sample()
if (0) {
# reload saved fit and results
load(pN$fishery_model$fnres)
fit = aegis::read_write_fast(pN$fishery_model$fnfit)
}
# frequency density of key parameters
fishery_model( DS="plot", vname="K", res=res )
fishery_model( DS="plot", vname="r", res=res )
fishery_model( DS="plot", vname="q", res=res, xrange=c(0.5, 2.5))
fishery_model( DS="plot", vname="qc", res=res, xrange=c(-1, 1))
fishery_model( DS="plot", vname="FMSY", res=res )
# timeseries
fishery_model( DS="plot", type="timeseries", vname="biomass", res=res )
fishery_model( DS="plot", type="timeseries", vname="fishingmortality", res=res)
# Harvest control rules
fishery_model( DS="plot", type="hcr", vname="default", res=res )
# Summary table of mean values for inclusion in document
( qs = apply( res$mcmc$K[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) ) # carrying capactiy
( qs = apply( res$mcmc$FMSY[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) ) # FMSY
biomass = as.data.table( fit$summary("B") )
np = year.assessment+c(1:pN$fishery_model$fmdata$M)
biomass$yr = rep( c(pN$yrs, np ), 3)
nt = pN$fishery_model$fmdata$N +pN$fishery_model$fmdata$M
biomass$region = c( rep("cfanorth", nt), rep("cfasouth", nt), rep("cfa4x", nt) )
(biomass)
NN = res$pN$fishery_model$fmdata$N
# densities of biomass estimates for the year.assessment
( qs = apply( res$mcmc$B[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of biomass estimates for the previous year
( qs = apply( res$mcmc$B[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of F in assessment year
( qs = apply( res$mcmc$F[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN,], 2, mean ) )
# densities of F in previous year
( qs = apply( res$mcmc$F[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN-1,], 2, mean ) )
if (0) {
# obsolete:
fishery_model( DS="plot", vname="bosd", res=res )
fishery_model( DS="plot", vname="bpsd", res=res )
fishery_model( DS="plot", type="hcr", vname="simple", res=res )
# biomass.summary.table()
fit = fishery_model( p=pN, DS="fit", tag=pN$fishery_model_label ) # to load samples (results)
fit$summary(c("K", "r", "q", "qc"))
print( fit, max_rows=30 )
fit$cmdstan_diagnose()
fit$cmdstan_summary()
# testing other samplers and optimizsers ... faster , good for debugging
# (penalized) maximum likelihood estimate (MLE)
fit_mle = pN$fishery_model$stancode$optimize(data =pN$fishery_model$fmdata, seed = 123)
fit_mle$summary( to_look )
u = stan_extract( as_draws_df(fit_mle$draws() ) )
# mcmc_hist(fit$draws("K")) + vline_at(fit_mle$mle(), size = 1.5)
# # Variational Bayes
fit_vb = pN$fishery_model$stancode$variational( data =pN$fishery_model$fmdata, seed = 123, output_samples = 4000)
fit_vb$summary(to_look)
fit_vb$cmdstan_diagnose()
fit_vb$cmdstan_summary()
u = stan_extract( as_draws_df(fit_vb$draws() ) )
# bayesplot_grid(
# mcmc_hist(fit$draws("K"), binwidth = 0.025),
# mcmc_hist(fit_vb$draws("K"), binwidth = 0.025),
# titles = c("Posterior distribution from MCMC", "Approximate posterior from VB")
# )
# color_scheme_set("gray")
# mcmc_dens(fit$draws("K"), facet_args = list(nrow = 3, labeller = ggplot2::label_parsed ) ) + facet_text(size = 14 )
# mcmc_hist( fit$draws("K"))
# obtain mcmc samples from vb solution
res_vb = fishery_model(
DS="logistic_model",
p=pN,
tag=pN$fishery_model_label,
fit = fit_vb
)
names(res_vb$mcmc)
# other diagnostics
# fishery_model( DS="plot", type="diagnostic.errors", res=res )
# fishery_model( DS="plot", type="diagnostic.phase", res=res )
NN = res$pN$fishery_model$fmdata$N
# bosd
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$bosd[,i] ), main="")
( qs = apply( res$mcmc$bosd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# bpsd
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$bpsd[,i] ), main="")
( qs = apply( res$mcmc$bpsd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# rem_sd
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$rem_sd[,i] ), main="")
( qs = apply( res$mcmc$rem_sd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# qc
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$qc[,i] ), main="")
( qs = apply( res$mcmc$qc[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# b0
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$b0[,i] ), main="")
( qs = apply( res$mcmc$b0[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# K
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$K[,i] ), main="")
( qs = apply( res$mcmc$K[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# R
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$r[,i] ), main="")
( qs = apply( res$mcmc$r[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# q
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$q[,i] ), main="")
( qs = apply( res$mcmc$q[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# FMSY
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$FMSY[,i] ), main="")
( qs = apply( res$mcmc$FMSY[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of biomass estimates for the year.assessment
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density(res$mcmc$B[,NN,i] ), main="")
( qs = apply( res$mcmc$B[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of biomass estimates for the previous year
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density( res$mcmc$B[,NN-1,i] ), main="")
( qs = apply( res$mcmc$B[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
# densities of F in assessment year
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density( res$mcmc$F[,NN,i] ), xlim=c(0.01, 0.6), main="")
( qs = apply( res$mcmc$F[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN,], 2, mean ) )
# densities of F in previous year
plot.new()
layout( matrix(c(1,2,3), 3, 1 ))
par(mar = c(4.4, 4.4, 0.65, 0.75))
for (i in 1:3) plot(density( res$mcmc$F[,NN-1,i] ), xlim=c(0.01, 0.6), main="")
( qs = apply( res$mcmc$F[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )
( qs = apply( res$mcmc$F[,NN-1,], 2, mean ) )
# F for table ---
summary( res$mcmc$F, median)
} # end skip
} # end fishery model
# end
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