From 0c536e6310d0f303cc9c88438b73beeda372bc13 Mon Sep 17 00:00:00 2001 From: Cole-Monnahan-NOAA Date: Wed, 7 Feb 2024 16:38:56 -0800 Subject: [PATCH 1/5] First pass of GOA pollock case study -- runs but does not render --- content/AFSC-GOA-pollock.qmd | 263 ++++++++++++ .../data_files/pk_pollock_SSB_posteriors.RDS | Bin 0 -> 404295 bytes content/data_files/pkfit0.RDS | Bin 0 -> 2717677 bytes content/data_files/pkfitfinal.RDS | Bin 0 -> 1570902 bytes content/data_files/pkinput.RDS | Bin 0 -> 16252 bytes content/data_files/pkinput0.RDS | Bin 0 -> 20871 bytes content/figures/AFSC_PK_age_comp_fits_1.png | Bin 0 -> 535291 bytes content/figures/AFSC_PK_age_comp_fits_2.png | Bin 0 -> 390057 bytes content/figures/AFSC_PK_index_fits.png | Bin 0 -> 61382 bytes content/figures/AFSC_PK_like_profile_R0.png | Bin 0 -> 110314 bytes content/figures/AFSC_PK_ts_comparison.png | Bin 0 -> 317745 bytes .../AFSC_PK_ts_comparison_relerror.png | Bin 0 -> 150892 bytes content/figures/ssb_posterior.png | Bin 0 -> 531978 bytes content/pk_prepare_FIMS_inputs.R | 396 ++++++++++++++++++ 14 files changed, 659 insertions(+) create mode 100644 content/AFSC-GOA-pollock.qmd create mode 100644 content/data_files/pk_pollock_SSB_posteriors.RDS create mode 100644 content/data_files/pkfit0.RDS create mode 100644 content/data_files/pkfitfinal.RDS create mode 100644 content/data_files/pkinput.RDS create mode 100644 content/data_files/pkinput0.RDS create mode 100644 content/figures/AFSC_PK_age_comp_fits_1.png create mode 100644 content/figures/AFSC_PK_age_comp_fits_2.png create mode 100644 content/figures/AFSC_PK_index_fits.png create mode 100644 content/figures/AFSC_PK_like_profile_R0.png create mode 100644 content/figures/AFSC_PK_ts_comparison.png create mode 100644 content/figures/AFSC_PK_ts_comparison_relerror.png create mode 100644 content/figures/ssb_posterior.png create mode 100644 content/pk_prepare_FIMS_inputs.R diff --git a/content/AFSC-GOA-pollock.qmd b/content/AFSC-GOA-pollock.qmd new file mode 100644 index 0000000..87b3584 --- /dev/null +++ b/content/AFSC-GOA-pollock.qmd @@ -0,0 +1,263 @@ +--- +title: AFSC Case Study Gulf of Alaska Walleye Pollock +format: + html: + code-fold: true +--- + +## The setup + +```{r} +#| warning: false +library(tidyverse) +library(FIMS) +library(TMB) +library(TMBhelper) +library(GOApollock) +theme_set(theme_bw()) + + +R_version <- version$version.string +TMB_version <- packageDescription("TMB")$Version +FIMS_commit <- substr(packageDescription("FIMS")$GithubSHA1, 1, 7) +``` + +- R version: `r R_version`\ +- TMB version: `r TMB_version`\ +- FIMS commit: `r FIMS_commit`\ +- Stock name: Gulf of Alaska (GOA) Walleye Pollock\ +- Region: AFSC\ +- Analyst: Cole Monnahan\ + +## Simplifications to the original assessment + +* Drop survey 1 +* To do + + +How I simplified my assessment: + +To fill the missing year of survey data, I first ran the model in ASAP with the missing data treated as missing. I then filled the missing data with the expected value of the survey, both in aggregate and for catch at age in place of the missing data. + +The varying weights at age, index CVs, and ESS values were replaced by the time series mean in all years. + +The catch weight at age matrix was used as the weight at age for all sources. + +## Script to prepare data for building FIMS object +```{r} +#| warning: false + +# clear memory +clear() + +## define the dimensions and global variables +years <- 1970:2023 +nyears <- length(years) +nseasons <- 1 +nages <- 10 +ages <- 1:nages +## nfleets <- 1 +## This will fit the models bridging to FIMS (simplifying) +## source("fit_bridge_models.R") +## compare changes to model +pkfitfinal <- readRDS("data_files/pkfitfinal.RDS") +pkfit0 <- readRDS("data_files/pkfit0.RDS") +parfinal <- pkfitfinal$obj$env$parList() +pkinput0 <- readRDS('data_files/pkinput0.RDS') +fimsdat <- pkdat0 <- pkinput0$dat +pkinput <- readRDS('data_files/pkinput.RDS') +``` + +## Run FIMS model +```{r} +#| warning: false +#| +## set up FIMS data objects +clear() +clear_logs() +estimate_fish_selex <- TRUE +estimate_survey_selex <- TRUE +estimate_q2 <- TRUE +estimate_q3 <- TRUE +estimate_q6 <- TRUE +estimate_F <- TRUE +estimate_recdevs <- TRUE +source("pk_prepare_FIMS_inputs.R") +## make FIMS model +success <- CreateTMBModel() +parameters <- list(p = get_fixed()) +obj <- MakeADFun(data = list(), parameters, DLL = "FIMS", silent = TRUE) +## report values for the two models +rep0 <- pkfitfinal$rep +rep1 <- obj$report() # FIMS initial values +## try fitting the model +opt <- TMBhelper::fit_tmb(obj, getsd=FALSE, newtonsteps=0, control=list(trace=100)) +## opt <- with(obj, nlminb(start=par, objective=fn, gradient=gr)) +max(abs(obj$gr())) # from Cole, can use TMBhelper::fit_tmb to get val to <1e-10 +rep2 <- obj$report() ## FIMS after estimation + +## Output plotting +out1 <- get_long_outputs(rep1, rep0) %>% + mutate(platform=ifelse(platform=='FIMS', 'FIMS init', 'TMB')) +out2 <- get_long_outputs(rep2, rep0) %>% filter(platform=='FIMS') %>% + mutate(platform='FIMS opt') +out <- rbind(out1,out2) +g <- ggplot(out, aes(year, value, color=platform)) + geom_line() + + facet_wrap('name', scales='free') + ylim(0,NA) + + labs(x=NULL, y=NULL) +ggsave('figures/AFSC_PK_ts_comparison.png', g, width=9, height=5, units='in') +g <- ggplot(filter(out, platform!='TMB'), aes(year, relerror, color=platform)) + geom_line() + + facet_wrap('name', scales='free') + + labs(x=NULL, y='Relative difference') + coord_cartesian(ylim=c(-.5,.5)) +ggsave('figures/AFSC_PK_ts_comparison_relerror.png', g, width=9, height=5, units='in') + +## Quick check on age comp fits +p1 <- get_acomp_fits(rep0, rep1, rep2, fleet=1, years=pkdat0$fshyrs) +g <- ggplot(p1, aes(age, paa, color=platform)) + facet_wrap('year') + geom_line() +ggsave('figures/AFSC_PK_age_comp_fits_1.png', g, width=9, height=8, units='in') +p2 <- get_acomp_fits(rep0, rep1, rep2, fleet=2, years=pkdat0$srv_acyrs2) +g <- ggplot(p2, aes(age, paa, color=platform)) + facet_wrap('year') + geom_line() +ggsave('figures/AFSC_PK_age_comp_fits_2.png', g, width=9, height=8, units='in') +## p3 <- get_acomp_fits(rep0, rep1, rep2, fleet=3, years=pkdat0$srv_acyrs3) +## g <- ggplot(p3, aes(age, paa, color=platform)) + facet_wrap('year') + geom_line() +## p6 <- get_acomp_fits(rep0, rep1, rep2, fleet=4, years=pkdat0$srv_acyrs6) +## g <- ggplot(p6, aes(age, paa, color=platform)) + facet_wrap('year') + geom_line() + +## index fits +addsegs <- function(yrs, obs, CV){ + getlwr <- function(obs, CV) qlnorm(p=.025, meanlog=log(obs), sdlog=sqrt(log(1+CV^2))) + getupr <- function(obs, CV) qlnorm(p=.975, meanlog=log(obs), sdlog=sqrt(log(1+CV^2))) + segments(yrs, y0=getlwr(obs,CV), y1=getupr(obs,CV)) + points(yrs, obs, pch=22, bg='white') +} +png('figures/AFSC_PK_index_fits.png', res=300, width=6, height=7, units='in') +par(mfrow=c(3,1), mar=c(3,3,.5,.5), mgp=c(1.5,.5,0), tck=-0.02) +plot(years, rep0$Eindxsurv2, type='l', + ylim=c(0,2), lwd=5.5, + xlab=NA, ylab='Biomass (million t)') +x1 <- out1 %>% filter(name=='Index2' & platform=='FIMS init') +x2 <- out2 %>% filter(name=='Index2' & platform=='FIMS opt') +lines(years,x1$value, col=2, lwd=1.5) +lines(years,x2$value, col=3, lwd=1.5) +addsegs(yrs=pkdat0$srvyrs2, obs=pkdat0$indxsurv2, CV=pkdat0$indxsurv_log_sd2) +legend('topright', legend=c('TMB', 'FIMS init', 'FIMS opt'), lty=1, col=1:3) +mtext('Survey 2', line=-1.5) +plot(years, rep0$Eindxsurv3, type='l', + ylim=c(0,.6), lwd=5.5, + xlab=NA, ylab='Biomass (million t)') +x1 <- out1 %>% filter(name=='Index3' & platform=='FIMS init') +x2 <- out2 %>% filter(name=='Index3' & platform=='FIMS opt') +lines(years,x1$value, col=2, lwd=1.5) +lines(years,x2$value, col=3, lwd=1.5) +addsegs(yrs=pkdat0$srvyrs3, obs=pkdat0$indxsurv3, CV=pkdat0$indxsurv_log_sd3) +mtext('Survey 3', line=-1.5) +legend('topright', legend=c('TMB', 'FIMS init', 'FIMS opt'), lty=1, col=1:3) +plot(years, rep0$Eindxsurv6, type='l', + ylim=c(0,2.6), lwd=5.5, + xlab=NA, ylab='Biomass (million t)') +x1 <- out1 %>% filter(name=='Index6' & platform=='FIMS init') +x2 <- out2 %>% filter(name=='Index6' & platform=='FIMS opt') +lines(years,x1$value, col=2, lwd=1.5) +lines(years,x2$value, col=3, lwd=1.5) +addsegs(yrs=pkdat0$srvyrs6, obs=pkdat0$indxsurv6, CV=pkdat0$indxsurv_log_sd6) +mtext('Survey 6', line=-1.5) +legend('topright', legend=c('TMB', 'FIMS init', 'FIMS opt'), lty=1, col=1:3) +dev.off() + + + +``` +## Comparison figures for basic model +![Time Series](figures/AFSC_PK_ts_comparison.png){width=7in} +![Time Series (relative error)](figures/AFSC_PK_ts_comparison_relerror.png){width=7in} +![Indices](figures/AFSC_PK_index_fits.png){width=7in} +![Time Series](figures/AFSC_PK_ts_comparison.png){width=7in} +![Fishery Age Composition Fits](figures/AFSC_PK_age_comp_fits_1.png){width=7in} +![Survey 2 Age Composition Fits](figures/AFSC_PK_age_comp_fits_2.png){width=7in} + +## Extra analyses +Two extra analyses are demonstrated. First is a likelihood profile over lnR0, showing component contributions and testing for data conflict. The second is to run the model through the 'Stan' software using the 'tmbstan' R package. This samples from the posterior and the posterior distribution for spawning stock biomass is shown. Given it's long run time the results are saved to a file and read in for post-hoc plotting. + +``` {r} +## Try a likelihood profile +i <- 68 # this will break if model is changed at all +map <- parameters +map$p[i] <- NA # map off R0 specified below +map$p <- as.factor(map$p) +xseq <- as.numeric(c(opt$par[i], seq(22,24, len=30))) +res <- list() +for(j in 1:length(xseq)){ + print(j) + parameters$p[i] <- xseq[j] + obj2 <- MakeADFun(data = list(), parameters, DLL = "FIMS", silent = TRUE, map=map) + opt2 <- TMBhelper::fit_tmb(obj2, getsd=FALSE, newtonsteps=0, control=list(trace=0)) + out <- obj2$report() + res[[j]] <- data.frame(j=j, lnR0=xseq[j], total=out$jnll, index=out$index_nll, + age=out$age_comp_nll,recruit=out$rec_nll, maxgrad=max(abs(obj$gr()))) +} +res <- bind_rows(res) %>% + pivot_longer( cols=c(total, index, age, recruit)) %>% + group_by(name) %>% mutate(deltaNLL=value-min(value)) +g <- ggplot(res, aes(lnR0, deltaNLL, color=name)) + geom_line() +g <- g+ geom_point(data=filter(res, deltaNLL==0), size=2) + + labs(y='Delta NLL', color='NLL component') +ggsave('figures/AFSC_PK_like_profile_R0.png', g, width=7, height=3.5) + + + +## ## Try Bayesian +## library(tmbstan) +## library(shinystan) +## ## Some paraemters wandering off to Inf so fix those (need +## ## priors). Needs a ton of work but is proof of concept. Major +## ## problem is parallel fails. +## map <- parameters +## ## parameters$p[65:66] +## map$p[65:66] <- NA # map off R0 specified below +## map$p <- as.factor(map$p) +## obj3 <- MakeADFun(data = list(), parameters, DLL = "FIMS", silent=TRUE, map=map) +## opt3 <- TMBhelper::fit_tmb(obj3, getsd=FALSE, newtonsteps=0, control=list(trace=0)) +## ## Fails when trying to do this in parallel unfortunately +## fit <- tmbstan(obj3, chains=1, cores=1, open_progress=FALSE, +## init='last.par.best', control=list(max_treedepth=10)) +## launch_shinystan(fit) +## df <- as.data.frame(fit) +## df <- df[,-ncol(df)] # drop lp__ +## ## for each posterior draw, report to get SSB +## postreps <- list() +## for(ii in 1:nrow(df)){ +## if(ii %% 10==0) print(ii) +## postreps[[ii]] <- obj3$rep(df[ii,]) +## } +## ssbpost <- lapply(postreps, function(x) data.frame(year=years, ssb=x$ssb[[1]][-55]))%>% +## bind_rows %>% mutate(rep=rep(1:nrow(df), each=54)) +## saveRDS(ssbpost, file='pk_SSB_posteriors.RDS') +ssbpost <- readRDS('data_files/pk_pollock_SSB_posteriors.RDS') +g <- ggplot(ssbpost, aes(year, ssb/1e9, group=rep)) + geom_line(alpha=.1) + + ylim(0,NA) + labs(x=NULL, y='SSB (M t)', title='Posterior demo (unconverged!)') +ggsave('figures/ssb_posterior.png', g, width=7, height=4, units='in') + +``` + +## Comparison table + +The likelihood components from the TMB model do not include constants and thus are not directly comparable. To be fixed later. + +## What was your experience using FIMS? What could we do to improve usability? + +TO DO Relatively easy to use by following the example done by Chris Legault. + +## List any issues that you ran into or found + +* Output more derived quantities like selectivity, maturity, etc. +* NLL components are not separated by fleet and need to be. 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