diff --git a/content/NEFSC-yellowtail.qmd b/content/NEFSC-yellowtail.qmd index d95625f..46967ac 100644 --- a/content/NEFSC-yellowtail.qmd +++ b/content/NEFSC-yellowtail.qmd @@ -1,53 +1,500 @@ --- -title: Customize +title: NEFSC Case Study Southern New England-Mid Atlantic Yellowtail Flounder +format: + html: + code-fold: true --- -## Edit and add your pages +## The setup -Each page should start with -``` ---- -title: your title ---- -``` -and the first header will be the 2nd level, so `## `. Note, there are situations where you leave off -``` ---- -title: your title ---- +```{r} +#| warning: false + +library(tidyverse) +library(FIMS) +library(TMB) +library(TMBhelper) + +R_version <- version$version.string +TMB_version <- packageDescription("TMB")$Version +FIMS_commit <- substr(packageDescription("FIMS")$GithubSHA1, 1, 7) ``` -and start the qmd file with a level header `# `, but if using the default title yaml (in the `---` fence) is a good habit since it makes it easy for Quarto convert your qmd file to other formats (like into a presentation). -## Add a chunk of code describing your setup +- R version: `r R_version`\ +- TMB version: `r TMB_version`\ +- FIMS commit: `r FIMS_commit`\ +- Stock name: Southern New England-Mid Atlantic Yellowtail Flounder\ +- Region: NEFSC\ +- Analyst: Chris Legault\ + +## Simplifications to the original assessment + +* End year 2021 to 2019 due to missing 2020 survey +* 5 indices to 2 +* Filled a survey missing year for one index (see below) +* Varying weight at age to constant over time +* Different weight at age matrices for catch, SSB, Jan-1 to same for all 3 +* SSB calculated in April to Jan-1 +* Aggregate index in numbers to weight +* Fishery 2 selectivity blocks to 1 +* Catch and Index selectivity at age to logistic +* Index timing from April to Jan-1 +* Varying Index CV to constant over time +* Varying Catch ESS to constant over time +* SR unexploited scaler SSB to recruitment + + +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 that sets up and runs the model +```{r} +#| warning: false + +# clear memory +clear() + +# read the ASAP rdat files +rdat <- dget(file.path("data_files", "NEFSC_YT_SIMPLIFIED.RDAT")) # to be used in FIMS, lots of modifications from original +orig <- dget(file.path("data_files", "NEFSC_YT_ORIGINAL.RDAT")) # where started before modifications for use in FIMS + +# function to create equivalent of data_mile1, basic catch and survey data +# need to think about how to deal with multiple fleets and indices - only use 1 of each for now +get_asap_data <- function(rdat){ + res <- data.frame(type = character(), + name = character(), + age = integer(), + datestart = character(), + dateend = character(), + value = double(), + unit = character(), + uncertainty = double()) + + landings <- data.frame(type = "landings", + name = "fleet1", + age = NA, + datestart = paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-01-01"), + dateend = paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-12-31"), + value = as.numeric(rdat$catch.obs[1,]), + unit = "mt", + uncertainty = rdat$control.parms$catch.tot.cv[,1]) + + # loop over all indices + for (i in 1:rdat$parms$nindices){ + index <- data.frame(type = "index", + name = paste0("survey", i), + age = NA, + datestart = paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-01-01"), + dateend = paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-12-31"), + value = as.numeric(rdat$index.obs[[i]]), + unit = "", + uncertainty = rdat$index.cv[[i]]) + if (i == 1){ + allinds <- index + }else{ + allinds <- rbind(allinds, index) + } + } + + catchage <- data.frame(type = "age", + name = "fleet1", + age = rep(seq(1,rdat$parms$nages), rdat$parms$nyears), + datestart = rep(paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-01-01"), each=rdat$parms$nages), + dateend = rep(paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-12-31"), each=rdat$parms$nages), + value = as.numeric(t(rdat$catch.comp.mats$catch.fleet1.ob)), + unit = "", + uncertainty = rep(rdat$fleet.catch.Neff.init[1,], each=rdat$parms$nages)) + + # loop over all indices + for (i in 1:rdat$parms$nindices){ + indexage <- data.frame(type = "age", + name = paste0("survey", i), + age = rep(seq(1,rdat$parms$nages), rdat$parms$nyears), + datestart = rep(paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-01-01"), each=rdat$parms$nages), + dateend = rep(paste0(seq(rdat$parms$styr, rdat$parms$endyr), "-12-31"), each=rdat$parms$nages), + value = as.numeric(t(rdat$index.comp.mats[[i*2-1]])), + unit = "", + uncertainty = rep(rdat$index.Neff.init[i,], each=rdat$parms$nages)) + if (i == 1){ + allindsage <- indexage + }else{ + allindsage <- rbind(allindsage, indexage) + } + } + + res <- rbind(res, landings, allinds, catchage, allindsage) + return(res) +} + +mydat <- get_asap_data(rdat) + +myfimsframe <- FIMS::FIMSFrame(mydat) +#str(myfimsframe) + +# define the dimensions +nyears <- rdat$parms$nyears +years <- seq(rdat$parms$styr, rdat$parms$endyr) +nseasons <- 1 # ASAP only has one season +nages <- rdat$parms$nages +ages <- 1:nages # ASAP starts at age 1 + + +# set up FIMS data objects +age_frame <- FIMS::FIMSFrameAge(mydat) + +fishery_catch <- FIMS::m_landings(age_frame) +fishery_agecomp <- FIMS::m_agecomp(age_frame, "fleet1") +survey_index <- list() +survey_agecomp <- list() +for (i in 1:rdat$parms$nindices){ + survey_index[[i]] <- FIMS::m_index(age_frame, paste0("survey", i)) + survey_agecomp[[i]] <- FIMS::m_agecomp(age_frame, paste0("survey", i)) +} + +# eventually change to allow multiple fishing fleets similar to multiple indices - only using 1 fishing fleet for now +fishing_fleet_index <- methods::new(Index, nyears) +fishing_fleet_age_comp <- methods::new(AgeComp, nyears, nages) +fishing_fleet_index$index_data <- fishery_catch +fishing_fleet_age_comp$age_comp_data <- fishery_agecomp * rep(rdat$fleet.catch.Neff.init[1,], each=rdat$parms$nages) + + +# fleet selectivity +#methods::show(LogisticSelectivity) +fishing_fleet_selectivity <- methods::new(LogisticSelectivity) +fishing_fleet_selectivity$inflection_point$value <- rdat$sel.input.mats$fleet.sel.ini[nages+1,1] # hardwired to assume only 1 fleet and logistic selectivity used +fishing_fleet_selectivity$inflection_point$is_random_effect <- FALSE +fishing_fleet_selectivity$inflection_point$estimated <- TRUE +fishing_fleet_selectivity$slope$value <- rdat$sel.input.mats$fleet.sel.ini[nages+2,1] # hardwired to assume only 1 fleet and logistic selectivity used +fishing_fleet_selectivity$slope$is_random_effect <- FALSE +fishing_fleet_selectivity$slope$estimated <- TRUE + +# create fleet object +fishing_fleet <- methods::new(Fleet) +fishing_fleet$nages <- nages +fishing_fleet$nyears <- nyears +fishing_fleet$log_Fmort <- log(rep(rdat$initial.guesses$Fmult.year1.init[1], nyears)) # ASAP assumes Fmult devs = 0 +fishing_fleet$estimate_F <- TRUE +fishing_fleet$random_F <- FALSE +fishing_fleet$log_q <- log(rdat$initial.guesses$q.year1.init[1]) +fishing_fleet$estimate_q <- FALSE +fishing_fleet$random_q <- FALSE +fishing_fleet$log_obs_error$value <- log(sqrt(log(as.numeric(mean(rdat$control.parms$catch.tot.cv[,1], na.rm=TRUE)^2) + 1))) +fishing_fleet$log_obs_error$estimated <- FALSE +# Next two lines not currently used by FIMS +fishing_fleet$SetAgeCompLikelihood(1) +fishing_fleet$SetIndexLikelihood(1) +# Set Index, AgeComp, and Selectivity using the IDs from the modules defined above +fishing_fleet$SetObservedIndexData(fishing_fleet_index$get_id()) +fishing_fleet$SetObservedAgeCompData(fishing_fleet_age_comp$get_id()) +fishing_fleet$SetSelectivity(fishing_fleet_selectivity$get_id()) + +# survey module now can handle multiple indices +for (i in 1:rdat$parms$nindices){ + survey_fleet_index <- methods::new(Index, nyears) + survey_fleet_age_comp <- methods::new(AgeComp, nyears, nages) + survey_fleet_index$index_data <- survey_index[[i]] + survey_fleet_age_comp$age_comp_data <- survey_agecomp[[i]] * rep(rdat$index.Neff.init[i,], each=rdat$parms$nages) + + # survey selectivity + survey_fleet_selectivity <- new(LogisticSelectivity) + survey_fleet_selectivity$inflection_point$value <- rdat$sel.input.mats$index.sel.ini[(i+1)*(nages+2+4)+nages+1,1] # hardwired for this example + survey_fleet_selectivity$inflection_point$is_random_effect <- FALSE + survey_fleet_selectivity$inflection_point$estimated <- TRUE + survey_fleet_selectivity$inflection_point$value <- rdat$sel.input.mats$index.sel.ini[(i+1)*(nages+2+4)+nages+1,1] # hardwired for this example + survey_fleet_selectivity$slope$is_random_effect <- FALSE + survey_fleet_selectivity$slope$estimated <- TRUE + + survey_fleet <- methods::new(Fleet) + survey_fleet$is_survey <- TRUE + survey_fleet$nages <- nages + survey_fleet$nyears <- nyears + survey_fleet$estimate_F <- FALSE + survey_fleet$random_F <- FALSE + survey_fleet$log_q <- log(rdat$initial.guesses$q.year1.init[i]) + survey_fleet$estimate_q <- TRUE + survey_fleet$random_q <- FALSE + # sd = sqrt(log(cv^2 + 1)), sd is log transformed + survey_fleet$log_obs_error$value <- log(sqrt(log(as.numeric(mean(rdat$index.cv[[i]], na.rm=TRUE)^2 + 1)))) + survey_fleet$log_obs_error$estimated <- FALSE + survey_fleet$SetAgeCompLikelihood(i) + survey_fleet$SetIndexLikelihood(i) + survey_fleet$SetSelectivity(survey_fleet_selectivity$get_id()) + survey_fleet$SetObservedIndexData(survey_fleet_index$get_id()) + survey_fleet$SetObservedAgeCompData(survey_fleet_age_comp$get_id()) +} + +# Population module + +# recruitment +recruitment <- methods::new(BevertonHoltRecruitment) +#methods::show(BevertonHoltRecruitment) + +recruitment$log_sigma_recruit$value <- log(mean(rdat$control.parms$recruit.cv)) # typically enter same value for every year in ASAP +recruitment$log_rzero$value <- log(rdat$initial.guesses$SR.inits$SR.scaler.init) # ASAP can enter either R0 or SSB0, need to make sure use R0 in input file +recruitment$log_rzero$is_random_effect <- FALSE +recruitment$log_rzero$estimated <- TRUE +# note: do not set steepness exactly equal to 1, use 0.99 instead in ASAP run +recruitment$logit_steep$value <- -log(1.0 - rdat$initial.guesses$SR.inits$SR_steepness.init) + log(rdat$initial.guesses$SR.inits$SR_steepness.init - 0.2) +recruitment$logit_steep$is_random_effect <- FALSE +recruitment$logit_steep$estimated <- FALSE + +recruitment$estimate_log_devs <- TRUE +recruitment$log_devs <- rep(1.0, nyears) # set to no deviations (multiplier) to start, just like ASAP + +# growth +ewaa_growth <- methods::new(EWAAgrowth) +ewaa_growth$ages <- ages +# NOTE: FIMS currently cannot use matrix of WAA, so have to ensure constant WAA over time in ASAP file for now +ewaa_growth$weights <- rdat$WAA.mats$WAA.catch.all[1,] -Case study files should include: -* R version -* TMB version -* FIMS commit -* Name of your stock -* Name of your region -* Name of the analyst +# NOTE: FIMS assumes SSB calculated at the start of the year, so need to adjust ASAP to do so as well for now, need to make timing of SSB calculation part of FIMS later -## Add a bulleted list and script describing simplifications you had to make +# maturity +# NOTE: for now tricking FIMS into thinking age 0 is age 1, so need to adjust A50 for maturity because FIMS calculations use ages 0-5+ instead of 1-6 +maturity <- new(LogisticMaturity) +maturity$inflection_point$value <- 1.8 # hardwired for now, need to figure out a better way than this +maturity$inflection_point$is_random_effect <- FALSE +maturity$inflection_point$estimated <- FALSE +maturity$slope$value <- 4 # hardwired for now, need to figure out a better way than this +maturity$slope$is_random_effect <- FALSE +maturity$slope$estimated <- FALSE -How I simplified my assessment -* Remove seasonality -* etc. +# population +population <- new(Population) +population$log_M <- log(as.numeric(t(rdat$M.age))) +population$estimate_M <- FALSE +population$log_init_naa <- log(rdat$N.age[1,]) # log(rdat$initial.guesses$NAA.year1.init) +population$estimate_init_naa <- FALSE # TRUE , NOTE: fixing at ASAP estimates to test SSB calculations +population$nages <- nages +population$ages <- ages +population$nfleets <- rdat$parms$nfleets + rdat$parms$nindices # fleets plus surveys +population$nseasons <- nseasons +population$nyears <- nyears +population$prop_female <- 1.0 # ASAP assumption -## Add your script that sets up and runs the model +population$SetMaturity(maturity$get_id()) +population$SetGrowth(ewaa_growth$get_id()) +population$SetRecruitment(recruitment$get_id()) -## Add your comparison figures +# make FIMS model +sucess <- CreateTMBModel() +parameters <- list(p = get_fixed()) +obj <- MakeADFun(data = list(), parameters, DLL = "FIMS", silent = TRUE) -## Add comparison tables + +# fitting the model +opt <- nlminb(start=obj$par, objective=obj$fn, gradient=obj$gr, + control = list(eval.max = 8000, iter.max = 800)) +# method = "BFGS", +# control = list(maxit=1000000, reltol = 1e-15)) +#print(opt) + + +#max(abs(obj$gr())) # from Cole, can use TMBhelper::fit_tmb to get val to <1e-10 + +#opt <- TMBhelper::fit_tmb(obj, newtonsteps=3, quiet = TRUE) # don't understand why quiet flag does not work in Quarto + +#max(abs(obj$gr())) + +sdr <- TMB::sdreport(obj) +sdr_fixed <- summary(sdr, "fixed") +report <- obj$report() + +### Plotting + +mycols <- c("FIMS" = "blue", "ASAP" = "red", "ASAP_orig" = "darkgreen") + +for (i in 1:rdat$parms$nindices){ + index_results <- data.frame( + survey = i, + year = years, + observed = rdat$index.obs[[i]], + FIMS = report$exp_index[[rdat$parms$nfleet+i]], + ASAP = rdat$index.pred[[i]] + ) + if (i==1){ + allinds_results <- index_results + }else{ + allinds_results <- rbind(allinds_results, index_results) + } +} +#print(allinds_results) + +comp_index <- ggplot(allinds_results, aes(x = year, y = observed)) + + geom_point() + + geom_line(aes(x = year, y = FIMS), color = "blue") + + geom_line(aes(x = year, y = ASAP), color = "red") + + facet_wrap(~survey, scales = "free_y", nrow = 2) + + xlab("Year") + + ylab("Index") + + ggtitle("Blue=FIMS, Red=ASAP") + + theme_bw() +#print(comp_index) + +catch_results <- data.frame( + observed = fishing_fleet_index$index_data, + FIMS = report$exp_index[[1]], + ASAP = as.numeric(rdat$catch.pred[1,]) +) +#print(catch_results) + +comp_catch <- ggplot(catch_results, aes(x = years, y = observed)) + + geom_point() + + xlab("Year") + + ylab("Catch (mt)") + + geom_line(aes(x = years, y = FIMS), color = "blue") + + geom_line(aes(x = years, y = ASAP), color = "red") + + ggtitle("Blue=FIMS, Red=ASAP") + + theme_bw() +#print(comp_catch) + +pop_results <- data.frame( + Year = c(years, max(years)+1, years, years, years, years, max(years)+1, years), + Metric = c(rep("SSB", 2*nyears+1), rep("F_mort", 2*nyears), rep("Recruitment", 2*nyears+1)), + Model = c(rep("FIMS", nyears+1), rep("ASAP", nyears), rep(c("FIMS", "ASAP"), each=nyears), + rep("FIMS", nyears+1), rep("ASAP", nyears)), + Value = c(report$ssb[[1]], rdat$SSB, report$F_mort[[1]], rdat$F.report, report$recruitment[[1]], as.numeric(rdat$N.age[,1])) +) +#print(pop_results) + +# ggplot(filter(pop_results, Year <=2019), aes(x=Year, y=Value, color=Model)) + +# geom_line() + +# facet_wrap(~Metric, ncol=1, scales = "free_y") + +# theme_bw() + +# scale_color_manual(values = mycols) + +orig_years <- seq(orig$parms$styr, orig$parms$endyr) +orig_pop_results <- data.frame( + Year = rep(orig_years, 3), + Metric = rep(c("SSB", "F_mort", "Recruitment"), each = length(orig_years)), + Model = "ASAP_orig", + Value = c(orig$SSB, orig$F.report, as.numeric(orig$N.age[,1])) +) + +pop_results_3 <- rbind(pop_results, orig_pop_results) +#print(pop_results_3) + +# ggplot(filter(pop_results_3, Year <=2019), aes(x=Year, y=Value, color=Model)) + +# geom_line() + +# facet_wrap(~Metric, ncol=1, scales = "free_y") + +# theme_bw() + +# scale_color_manual(values = mycols) + +comp_FRSSB3 <- ggplot(pop_results_3, aes(x=Year, y=Value, color=Model)) + + geom_line() + + facet_wrap(~Metric, ncol=1, scales = "free_y") + + theme_bw() + + scale_color_manual(values = mycols) +#print(comp_FRSSB3) + +FIMS_naa_results <- data.frame( + Year = rep(c(years, max(years)+1), each = nages), + Age = rep(ages, nyears+1), + Metric = "NAA", + Model = "FIMS", + Value = report$naa[[1]] +) + +ASAP_naa_results <- data.frame( + Year = rep(years, each = nages), + Age = rep(ages, nyears), + Metric = "NAA", + Model = "ASAP", + Value = as.numeric(t(rdat$N.age)) +) + +orig_naa_results <- data.frame( + Year = rep(orig_years, each = nages), + Age = rep(ages, length(orig_years)), + Metric = "NAA", + Model = "ASAP_orig", + Value = as.numeric(t(orig$N.age)) +) +naa_results <- rbind(FIMS_naa_results, ASAP_naa_results, orig_naa_results) +#print(naa_results) + +# ggplot(filter(naa_results, Year <= 2019), aes(x=Year, y=Value, color=Model)) + +# geom_line() + +# facet_wrap(~Age, ncol=1, scales = "free_y") + +# ylab("NAA") + +# theme_bw() + +# scale_color_manual(values = mycols) + +comp_naa2 <- ggplot(filter(naa_results, Year <= 2019, Model %in% c("ASAP", "FIMS")), aes(x=Year, y=Value, color=Model)) + + geom_line() + + facet_wrap(~Age, ncol=1, scales = "free_y") + + ylab("NAA") + + theme_bw() + + scale_color_manual(values = mycols) +#print(comp_naa2) + +# ggplot(filter(naa_results, Year == 1973, Model %in% c("ASAP", "FIMS")), aes(x=Age, y=Value, color=Model)) + +# geom_line() + +# ylab("NAA in Year 1") + +# theme_bw() + +# scale_color_manual(values = mycols) + + +saveplots <- TRUE +if(saveplots){ + ggsave(filename = "figures/NEFSC_YT_compare_index.png", plot = comp_index, width = 4, height = 4, units = "in") + ggsave(filename = "figures/NEFSC_YT_compare_catch.png", plot = comp_catch, width = 4, height = 4, units = "in") + ggsave(filename = "figures/NEFSC_YT_compare_FRSSB3.png", plot = comp_FRSSB3, width = 5, height = 6.5, units = "in") + ggsave(filename = "figures/NEFSC_YT_compare_NAA2.png", plot = comp_naa2, width = 5, height = 6.5, units = "in") +} + +``` +## Comparison figures +![Catch](figures/NEFSC_YT_compare_catch.png){width=4in} +![Indices](figures/NEFSC_YT_compare_index.png){width=4in} +![FRSSB](figures/NEFSC_YT_compare_FRSSB3.png){width=4in} +![NAA](figures/NEFSC_YT_compare_NAA2.png){width=4in} + +## Comparison table + +The likelihood components from FIMS and ASAP for the same data are shown in the table below. Note that the ASAP file had to turn on the use likelihood constants option to enable this comparison (this option should not be used when recruitment deviations are estimated). + +```{r} +jnlltab <- data.frame(Component=c("Total","Index","Age Comp", "Rec"), + FIMS = c(report$jnll, report$index_nll, report$age_comp_nll, report$rec_nll), + ASAP = c(rdat$like$lk.total, + (rdat$like$lk.catch.total + rdat$like$lk.index.fit.total), + (rdat$like$lk.catch.age.comp + rdat$like$lk.index.age.comp), + rdat$like$lk.Recruit.devs)) +print(jnlltab) +``` ## What was your experience using FIMS? What could we do to improve usability? +Relatively easy to use by following the vignette. Creating wrappers for data input would help so that each element did not need to be assigned directly. + ## List any issues that you ran into or found Please [open an issue](https://github.com/NOAA-FIMS/FIMS/issues/new/choose) if you found something new. +* SSB calculations in FIMS assume 0.5 multiplier, which differs from ASAP [Issue #521](https://github.com/NOAA-FIMS/FIMS/issues/521). +* Output all derived values (this is mostly done) +* Fix recruitment estimation [Issue #364](https://github.com/NOAA-FIMS/FIMS/issues/364) +* Handle missing data, especially surveys [Issue #502](https://github.com/NOAA-FIMS/FIMS/issues/502) +* Weights at age that change over time +* Separate weights at age for catch, SSB, Jan-1 population, indices, etc. +* Fishery selectivity blocks or random effects +* Allow time-varying CVs and ESS (or alternative functions) +* Option for Index in numbers +* Timing of Index and SSB calculations within the year +* One-step-ahead residuals +* Reference points, projections, pushbutton retro + ## What features are most important to add based on this case study? -## Add your pages to the project +* Missing values, would allow inclusion of the other 3 indices (too many missing years to fill for this example) -* Add the files to `_quarto.yml` +```{r} +# Clear C++ objects from memory +clear() +``` diff --git a/content/data_files/NEFSC_YT_ORIGINAL.RDAT b/content/data_files/NEFSC_YT_ORIGINAL.RDAT new file mode 100644 index 0000000..3f155f6 --- /dev/null +++ b/content/data_files/NEFSC_YT_ORIGINAL.RDAT @@ -0,0 +1,2066 @@ +### This file written with ADMB2R version 1.15 +### Read into R or S with x=dget('c:\users\chris.legault\documents\working\assessments\management track 2022 fall\snemayt\asap\run23\asap3.rdat') + +structure(list( + +info = structure(list( +date = "Wednesday, 07 Sep 2022 at 11:25:41", +program = "ASAP3"), +.Names = c("date", "program")) + +, parms = structure(list( +styr = 1973, +endyr = 2021, +nyears = 49, +nages = 6, +nfleets = 1, +nselblocks = 2, +navailindices = 5, +nindices = 5), +.Names = c("styr", "endyr", "nyears", "nages", "nfleets", "nselblocks", "navailindices", "nindices")) + +, options = structure(list( +isfecund = 0.000000e+000, +frac.yr.spawn = 4.167000e-001, +do.projections = 0, +ignore.guesses = 0.000000e+000, +Freport.agemin = 4, +Freport.agemax = 5, +Freport.wtopt = 1, +use.likelihood.constants = 0, +Fmult.max.value = 5.000000e+000, +N.year1.flag = 1, +do.mcmc = 0), +.Names = c("isfecund", "frac.yr.spawn", "do.projections", "ignore.guesses", "Freport.agemin", "Freport.agemax", "Freport.wtopt", "use.likelihood.constants", "Fmult.max.value", "N.year1.flag", "do.mcmc")) + +, like = structure(list( +lk.total = 6.510761e+003, +lk.catch.total = -1.068722e+002, +lk.discard.total = 0.000000e+000, +lk.index.fit.total = 6.678083e+001, +lk.catch.age.comp = 3.731990e+003, +lk.discards.age.comp = 0.000000e+000, +lk.index.age.comp = 2.818863e+003, +lk.sel.param.total = 0.000000e+000, +lk.index.sel.param.total = 0.000000e+000, +lk.q.year1 = 0.000000e+000, +lk.q.devs = 0.000000e+000, +lk.Fmult.year1.total = 0.000000e+000, +lk.Fmult.devs.total = 0.000000e+000, +lk.N.year1 = 0.000000e+000, +lk.Recruit.devs = 0.000000e+000, +lk.SR.steepness = 0.000000e+000, +lk.SR.scaler = 0.000000e+000, +lk.Fmult.Max.penalty = 0.000000e+000, +lk.F.penalty = 0.000000e+000), +.Names = c("lk.total", "lk.catch.total", "lk.discard.total", "lk.index.fit.total", "lk.catch.age.comp", "lk.discards.age.comp", "lk.index.age.comp", "lk.sel.param.total", "lk.index.sel.param.total", "lk.q.year1", "lk.q.devs", "lk.Fmult.year1.total", "lk.Fmult.devs.total", "lk.N.year1", "lk.Recruit.devs", "lk.SR.steepness", "lk.SR.scaler", "lk.Fmult.Max.penalty", "lk.F.penalty")) + +, like.additional = structure(list( +nfleets = 1, +nindices = 5, +nselparms = 12, +nindexselparms = 30, +lk.index.fit.ind01 = -5.811848e+000, +lk.index.fit.ind02 = 6.742690e+000, +lk.index.fit.ind03 = 2.467790e+001, +lk.index.fit.ind04 = 1.108335e+000, +lk.index.fit.ind05 = 4.006375e+001, +lk.q.year1.ind01 = 0.000000e+000, +lk.q.year1.ind02 = 0.000000e+000, +lk.q.year1.ind03 = -0.000000e+000, +lk.q.year1.ind04 = 0.000000e+000, +lk.q.year1.ind05 = 0.000000e+000, +lk.q.devs.ind01 = 0.000000e+000, +lk.q.devs.ind02 = 0.000000e+000, +lk.q.devs.ind03 = 0.000000e+000, +lk.q.devs.ind04 = 0.000000e+000, +lk.q.devs.ind05 = 0.000000e+000, +lk.sel.param.01 = 0.000000e+000, +lk.sel.param.02 = -0.000000e+000, +lk.sel.param.03 = -0.000000e+000, +lk.sel.param.07 = -0.000000e+000, +lk.sel.param.08 = -0.000000e+000, +lk.sel.param.09 = -0.000000e+000, +lk.indexsel.param.01 = 0.000000e+000, +lk.indexsel.param.02 = 0.000000e+000, +lk.indexsel.param.03 = -0.000000e+000, +lk.indexsel.param.07 = 0.000000e+000, +lk.indexsel.param.08 = 0.000000e+000, +lk.indexsel.param.09 = -0.000000e+000, +lk.indexsel.param.13 = -0.000000e+000, +lk.indexsel.param.14 = -0.000000e+000, +lk.indexsel.param.15 = -0.000000e+000), +.Names = c("nfleets", "nindices", "nselparms", "nindexselparms", "lk.index.fit.ind01", "lk.index.fit.ind02", "lk.index.fit.ind03", "lk.index.fit.ind04", "lk.index.fit.ind05", "lk.q.year1.ind01", "lk.q.year1.ind02", "lk.q.year1.ind03", "lk.q.year1.ind04", "lk.q.year1.ind05", "lk.q.devs.ind01", "lk.q.devs.ind02", "lk.q.devs.ind03", "lk.q.devs.ind04", "lk.q.devs.ind05", "lk.sel.param.01", "lk.sel.param.02", "lk.sel.param.03", "lk.sel.param.07", "lk.sel.param.08", "lk.sel.param.09", + "lk.indexsel.param.01", "lk.indexsel.param.02", "lk.indexsel.param.03", "lk.indexsel.param.07", "lk.indexsel.param.08", "lk.indexsel.param.09", "lk.indexsel.param.13", "lk.indexsel.param.14", "lk.indexsel.param.15")) + +, initial.guesses = structure(list( + +SR.inits = structure(list( +is.SR.scaler.R = 0.000000e+000, +SR.scaler.init = 1.500000e+005, +SR_steepness.init = 1.000000e+000), +.Names = c("is.SR.scaler.R", "SR.scaler.init", "SR_steepness.init")) + +, NAA.year1.init = structure(c( +4.249100e+004, 1.812800e+004, 2.932200e+004, 1.683400e+004, 1.102700e+004, 1.326400e+004), +.Names = NULL) + +, Fmult.year1.init = structure(c( +7.000000e-001), +.Names = NULL) + +, q.year1.init = structure(c( +2.000000e-001, 2.000000e-001, 2.000000e-001, 1.125000e-007, 1.125000e-007), +.Names = NULL) + +, release.mort = structure(c( +1.000000e+000), +.Names = NULL) + +, index.use.flag = structure(c( +1, 1, 1, 1, 1), +.Names = NULL) + +), .Names = c("SR.inits", "NAA.year1.init", "Fmult.year1.init", "q.year1.init", "release.mort", "index.use.flag")) + +, control.parms = structure(list( + +phases = structure(list( +phase.Fmult.year1 = 1, +phase.Fmult.devs = 2, +phase.recruit.devs = 3, +phase.N.year1.devs = 4, +phase.q.year1 = 1, +phase.q.devs = -7, +phase.SR.scaler = 1, +phase.steepness = -3), +.Names = c("phase.Fmult.year1", "phase.Fmult.devs", "phase.recruit.devs", "phase.N.year1.devs", "phase.q.year1", "phase.q.devs", "phase.SR.scaler", "phase.steepness")) + +, singles = structure(list( +lambda.N.year1.devs = 0.000000e+000, +N.year1.cv = 9.000000e-001, +lambda.recruit.devs = 0.000000e+000, +lambda.steepness = 0.000000e+000, +steepness.cv = 9.000000e-001, +lambda.SR.scaler = 0.000000e+000, +SR.scaler.cv = 9.000000e-001), +.Names = c("lambda.N.year1.devs", "N.year1.cv", "lambda.recruit.devs", "lambda.steepness", "steepness.cv", "lambda.SR.scaler", "SR.scaler.cv")) + +, mcmc = structure(list( +mcmc.nyear.opt = 0, +mcmc.n.boot = 0, +mcmc.n.thin = 0, +mcmc.seed = 0, +fillR.opt = 2, +Ravg.start = 0, +Ravg.end = 0), +.Names = c("mcmc.nyear.opt", "mcmc.n.boot", "mcmc.n.thin", "mcmc.seed", "fillR.opt", "Ravg.start", "Ravg.end")) + +, recruit.cv = structure(c( +6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, +6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001), +.Names = NULL) + +, lambda.ind = structure(c( +1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000), +.Names = NULL) + +, lambda.catch.tot = structure(c( +1.000000e+000), +.Names = NULL) + +, catch.tot.cv = structure(c( +1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, +1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001), +.Dim = c(49,1), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1"))) + +, lambda.Discard.tot = structure(c( +0.000000e+000), +.Names = NULL) + +, discard.tot.cv = structure(c( +1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, +1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002), +.Dim = c(49,1), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1"))) + +, lambda.Fmult.year1 = structure(c( +0.000000e+000), +.Names = NULL) + +, Fmult.year1.cv = structure(c( +9.000000e-001), +.Names = NULL) + +, lambda.Fmult.devs = structure(c( +0.000000e+000), +.Names = NULL) + +, Fmult.devs.cv = structure(c( +9.000000e-001), +.Names = NULL) + +, lambda.q.year1 = structure(c( +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000), +.Names = NULL) + +, q.year1.cv = structure(c( +9.000000e-001, 9.000000e-001, 9.000000e-001, 9.000000e-001, 9.000000e-001), +.Names = NULL) + +, lambda.q.devs = structure(c( +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000), +.Names = NULL) + +, q.devs.cv = structure(c( +9.000000e-001, 9.000000e-001, 9.000000e-001, 9.000000e-001, 9.000000e-001), +.Names = NULL) + +, directed.fleet = structure(c( +1), +.Names = NULL) + +, WAA.point.bio = structure(c( +1, 1, 1, 1, 2, 3), +.Names = NULL) + +, index.units.aggregate = structure(c( +2.000000e+000, 2.000000e+000, 2.000000e+000, 1.000000e+000, 1.000000e+000), +.Names = NULL) + +, index.units.proportions = structure(c( +2.000000e+000, 2.000000e+000, 2.000000e+000, 1.000000e+000, 1.000000e+000), +.Names = NULL) + +, index.WAA.point = structure(c( +3, 3, 3, 2, 2), +.Names = NULL) + +, index.month = structure(c( +3.000000e+000, 4.000000e+000, 1.000000e+001, 5.000000e+000, 5.000000e+000), +.Names = NULL) + +, index.sel.start.age = structure(c( +1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000), +.Names = NULL) + +, index.sel.end.age = structure(c( +6.000000e+000, 6.000000e+000, 6.000000e+000, 6.000000e+000, 6.000000e+000), +.Names = NULL) + +, index.sel.choice = structure(c( +-1.000000e+000, -1.000000e+000, -1.000000e+000, -1.000000e+000, -1.000000e+000), +.Names = NULL) + +, index.age.comp.flag = structure(c( +1, 1, 1, 0, 0), +.Names = NULL) + +), .Names = c("phases", "singles", "mcmc", "recruit.cv", "lambda.ind", "lambda.catch.tot", "catch.tot.cv", "lambda.Discard.tot", "discard.tot.cv", "lambda.Fmult.year1", "Fmult.year1.cv", "lambda.Fmult.devs", "Fmult.devs.cv", "lambda.q.year1", "q.year1.cv", "lambda.q.devs", "q.devs.cv", "directed.fleet", "WAA.point.bio", "index.units.aggregate", "index.units.proportions", "index.WAA.point", "index.month", "index.sel.start.age", "index.sel.end.age", + "index.sel.choice", "index.age.comp.flag")) + +, sel.input.mats = structure(list( + +fleet.sel.ini = structure(c( +5.000000e-001, 7.500000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 1.000000e-002, 2.500000e-001, 7.500000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +5.000000e+000, 5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e+000, 5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002), +.Dim = c(24,4), +.Dimnames = list( +c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24"), +c("1", "2", "3", "4"))) + +, index.sel.ini = structure(c( +5.000000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e-001, +1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.200000e-003, 6.836000e-001, 9.854000e-001, 9.970000e-001, 9.963000e-001, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.200000e-003, 6.836000e-001, +9.854000e-001, 9.970000e-001, 9.963000e-001, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +5.000000e+000, 5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e+000, 5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e+000, +5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, -5.000000e+000, -5.000000e+000, +-5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 5.000000e-001, +5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 5.000000e-001, 5.000000e-001, +5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002), +.Dim = c(60,4), +.Dimnames = list( +c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", + "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", + "51", "52", "53", "54", "55", "56", "57", "58", "59", "60"), +c("1", "2", "3", "4"))) + +), .Names = c("fleet.sel.ini", "index.sel.ini")) + +, WAA.mats = structure(list( + +WAA.catch.fleet1 = structure(c( +2.100000e-001, 2.030000e-001, 2.180000e-001, 2.280000e-001, 2.150000e-001, 2.340000e-001, 1.890000e-001, 2.060000e-001, 1.400000e-001, 2.260000e-001, 1.750000e-001, 1.820000e-001, 1.830000e-001, 1.860000e-001, 2.470000e-001, 2.700000e-001, 6.100000e-002, 2.040000e-001, 9.000000e-002, 1.100000e-001, 1.220000e-001, 7.800000e-002, 7.600000e-002, 1.020000e-001, 1.390000e-001, +1.600000e-001, 1.310000e-001, 1.850000e-001, 1.450000e-001, 1.640000e-001, 9.500000e-002, 1.360000e-001, 1.020000e-001, 1.100000e-001, 1.110000e-001, 1.510000e-001, 1.050000e-001, 9.900000e-002, 1.300000e-001, 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4.660000e-001, +4.940000e-001, 5.570000e-001, 5.610000e-001, 5.670000e-001, 5.740000e-001, 5.720000e-001, 5.400000e-001, 5.110000e-001, 4.750000e-001, 4.720000e-001, 4.610000e-001, 5.020000e-001, 4.700000e-001, 4.910000e-001, 4.750000e-001, 4.530000e-001, 4.590000e-001, 4.760000e-001, 4.340000e-001, 4.890000e-001, 6.070000e-001, 4.040000e-001, 4.820000e-001, 4.820000e-001, +3.820000e-001, 4.390000e-001, 4.350000e-001, 4.990000e-001, 5.130000e-001, 6.620000e-001, 5.900000e-001, 6.820000e-001, 5.960000e-001, 6.600000e-001, 6.680000e-001, 4.870000e-001, 5.040000e-001, 5.870000e-001, 5.200000e-001, 6.110000e-001, 7.350000e-001, 6.130000e-001, 6.640000e-001, 6.310000e-001, 6.400000e-001, 4.800000e-001, 5.790000e-001, 6.040000e-001, 5.840000e-001, +5.670000e-001, 6.290000e-001, 6.670000e-001, 6.840000e-001, 7.640000e-001, 7.220000e-001, 5.810000e-001, 6.340000e-001, 6.070000e-001, 6.280000e-001, 5.740000e-001, 6.010000e-001, 5.730000e-001, 5.720000e-001, 5.540000e-001, 5.650000e-001, 5.650000e-001, 5.480000e-001, 5.210000e-001, 5.090000e-001, 6.090000e-001, 4.370000e-001, 5.248000e-001, 5.248000e-001, +4.280000e-001, 4.570000e-001, 4.810000e-001, 5.570000e-001, 5.420000e-001, 6.560000e-001, 6.620000e-001, 9.250000e-001, 6.500000e-001, 8.330000e-001, 8.190000e-001, 6.560000e-001, 6.080000e-001, 6.420000e-001, 6.310000e-001, 8.210000e-001, 9.570000e-001, 8.840000e-001, 7.980000e-001, 9.180000e-001, 1.040000e+000, 6.220000e-001, 7.580000e-001, 7.400000e-001, 7.680000e-001, +7.260000e-001, 1.695000e+000, 7.520000e-001, 8.240000e-001, 7.510000e-001, 9.450000e-001, 7.990000e-001, 7.950000e-001, 7.830000e-001, 8.340000e-001, 1.077000e+000, 7.530000e-001, 7.020000e-001, 7.170000e-001, 7.090000e-001, 6.830000e-001, 6.890000e-001, 6.850000e-001, 6.290000e-001, 6.810000e-001, 6.700000e-001, 7.910000e-001, 6.912000e-001, 6.912000e-001), +.Dim = c(49,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, WAA.discard.fleet1 = structure(c( +2.100000e-001, 2.030000e-001, 2.180000e-001, 2.280000e-001, 2.150000e-001, 2.340000e-001, 1.890000e-001, 2.060000e-001, 1.400000e-001, 2.260000e-001, 1.750000e-001, 1.820000e-001, 1.830000e-001, 1.860000e-001, 2.470000e-001, 2.700000e-001, 6.100000e-002, 2.040000e-001, 9.000000e-002, 1.100000e-001, 1.220000e-001, 7.800000e-002, 7.600000e-002, 1.020000e-001, 1.390000e-001, +1.600000e-001, 1.310000e-001, 1.850000e-001, 1.450000e-001, 1.640000e-001, 9.500000e-002, 1.360000e-001, 1.020000e-001, 1.100000e-001, 1.110000e-001, 1.510000e-001, 1.050000e-001, 9.900000e-002, 1.300000e-001, 1.200000e-001, 1.540000e-001, 1.560000e-001, 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5.090000e-001, 6.090000e-001, 4.370000e-001, 5.248000e-001, 5.248000e-001, +4.280000e-001, 4.570000e-001, 4.810000e-001, 5.570000e-001, 5.420000e-001, 6.560000e-001, 6.620000e-001, 9.250000e-001, 6.500000e-001, 8.330000e-001, 8.190000e-001, 6.560000e-001, 6.080000e-001, 6.420000e-001, 6.310000e-001, 8.210000e-001, 9.570000e-001, 8.840000e-001, 7.980000e-001, 9.180000e-001, 1.040000e+000, 6.220000e-001, 7.580000e-001, 7.400000e-001, 7.680000e-001, +7.260000e-001, 1.695000e+000, 7.520000e-001, 8.240000e-001, 7.510000e-001, 9.450000e-001, 7.990000e-001, 7.950000e-001, 7.830000e-001, 8.340000e-001, 1.077000e+000, 7.530000e-001, 7.020000e-001, 7.170000e-001, 7.090000e-001, 6.830000e-001, 6.890000e-001, 6.850000e-001, 6.290000e-001, 6.810000e-001, 6.700000e-001, 7.910000e-001, 6.912000e-001, 6.912000e-001), +.Dim = c(49,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, WAA.catch.all = structure(c( +2.100000e-001, 2.030000e-001, 2.180000e-001, 2.280000e-001, 2.150000e-001, 2.340000e-001, 1.890000e-001, 2.060000e-001, 1.400000e-001, 2.260000e-001, 1.750000e-001, 1.820000e-001, 1.830000e-001, 1.860000e-001, 2.470000e-001, 2.700000e-001, 6.100000e-002, 2.040000e-001, 9.000000e-002, 1.100000e-001, 1.220000e-001, 7.800000e-002, 7.600000e-002, 1.020000e-001, 1.390000e-001, +1.600000e-001, 1.310000e-001, 1.850000e-001, 1.450000e-001, 1.640000e-001, 9.500000e-002, 1.360000e-001, 1.020000e-001, 1.100000e-001, 1.110000e-001, 1.510000e-001, 1.050000e-001, 9.900000e-002, 1.300000e-001, 1.200000e-001, 1.540000e-001, 1.560000e-001, 4.600000e-002, 1.210000e-001, 1.120000e-001, 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5.248000e-001, 5.248000e-001, +4.280000e-001, 4.570000e-001, 4.810000e-001, 5.570000e-001, 5.420000e-001, 6.560000e-001, 6.620000e-001, 9.250000e-001, 6.500000e-001, 8.330000e-001, 8.190000e-001, 6.560000e-001, 6.080000e-001, 6.420000e-001, 6.310000e-001, 8.210000e-001, 9.570000e-001, 8.840000e-001, 7.980000e-001, 9.180000e-001, 1.040000e+000, 6.220000e-001, 7.580000e-001, 7.400000e-001, 7.680000e-001, +7.260000e-001, 1.695000e+000, 7.520000e-001, 8.240000e-001, 7.510000e-001, 9.450000e-001, 7.990000e-001, 7.950000e-001, 7.830000e-001, 8.340000e-001, 1.077000e+000, 7.530000e-001, 7.020000e-001, 7.170000e-001, 7.090000e-001, 6.830000e-001, 6.890000e-001, 6.850000e-001, 6.290000e-001, 6.810000e-001, 6.700000e-001, 7.910000e-001, 6.912000e-001, 6.912000e-001), +.Dim = c(49,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, WAA.discard.all = structure(c( +2.100000e-001, 2.030000e-001, 2.180000e-001, 2.280000e-001, 2.150000e-001, 2.340000e-001, 1.890000e-001, 2.060000e-001, 1.400000e-001, 2.260000e-001, 1.750000e-001, 1.820000e-001, 1.830000e-001, 1.860000e-001, 2.470000e-001, 2.700000e-001, 6.100000e-002, 2.040000e-001, 9.000000e-002, 1.100000e-001, 1.220000e-001, 7.800000e-002, 7.600000e-002, 1.020000e-001, 1.390000e-001, +1.600000e-001, 1.310000e-001, 1.850000e-001, 1.450000e-001, 1.640000e-001, 9.500000e-002, 1.360000e-001, 1.020000e-001, 1.100000e-001, 1.110000e-001, 1.510000e-001, 1.050000e-001, 9.900000e-002, 1.300000e-001, 1.200000e-001, 1.540000e-001, 1.560000e-001, 4.600000e-002, 1.210000e-001, 1.120000e-001, 1.390000e-001, 1.170000e-001, 1.070000e-001, 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4.570000e-001, 4.810000e-001, 5.570000e-001, 5.420000e-001, 6.560000e-001, 6.620000e-001, 9.250000e-001, 6.500000e-001, 8.330000e-001, 8.190000e-001, 6.560000e-001, 6.080000e-001, 6.420000e-001, 6.310000e-001, 8.210000e-001, 9.570000e-001, 8.840000e-001, 7.980000e-001, 9.180000e-001, 1.040000e+000, 6.220000e-001, 7.580000e-001, 7.400000e-001, 7.680000e-001, +7.260000e-001, 1.695000e+000, 7.520000e-001, 8.240000e-001, 7.510000e-001, 9.450000e-001, 7.990000e-001, 7.950000e-001, 7.830000e-001, 8.340000e-001, 1.077000e+000, 7.530000e-001, 7.020000e-001, 7.170000e-001, 7.090000e-001, 6.830000e-001, 6.890000e-001, 6.850000e-001, 6.290000e-001, 6.810000e-001, 6.700000e-001, 7.910000e-001, 6.912000e-001, 6.912000e-001), +.Dim = c(49,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, WAA.ssb = structure(c( +1.970000e-001, 1.910000e-001, 2.060000e-001, 2.200000e-001, 2.040000e-001, 2.240000e-001, 1.770000e-001, 1.980000e-001, 1.260000e-001, 2.210000e-001, 1.660000e-001, 1.720000e-001, 1.700000e-001, 1.750000e-001, 2.400000e-001, 2.800000e-001, 4.800000e-002, 2.020000e-001, 7.400000e-002, 9.200000e-002, 1.090000e-001, 6.600000e-002, 5.900000e-002, 8.800000e-002, 1.230000e-001, +1.430000e-001, 1.130000e-001, 1.660000e-001, 1.260000e-001, 1.470000e-001, 7.900000e-002, 1.200000e-001, 8.800000e-002, 9.500000e-002, 9.600000e-002, 1.360000e-001, 8.900000e-002, 8.300000e-002, 1.200000e-001, 1.059000e-001, 1.346000e-001, 1.370000e-001, 3.430000e-002, 1.099000e-001, 1.018000e-001, 1.357000e-001, 9.840000e-002, 9.142000e-002, 9.142000e-002, +2.880000e-001, 2.890000e-001, 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5.420000e-001, 6.560000e-001, 6.620000e-001, 9.250000e-001, 6.500000e-001, 8.330000e-001, 8.190000e-001, 6.560000e-001, 6.080000e-001, 6.420000e-001, 6.310000e-001, 8.210000e-001, 9.570000e-001, 8.840000e-001, 7.980000e-001, 9.180000e-001, 1.040000e+000, 6.220000e-001, 7.580000e-001, 7.400000e-001, 7.680000e-001, +7.260000e-001, 1.695000e+000, 7.520000e-001, 8.240000e-001, 7.510000e-001, 9.450000e-001, 7.990000e-001, 7.950000e-001, 7.830000e-001, 8.340000e-001, 1.077000e+000, 7.530000e-001, 7.020000e-001, 7.170000e-001, 7.090000e-001, 6.830000e-001, 6.890000e-001, 6.850000e-001, 6.290000e-001, 6.810000e-001, 6.700000e-001, 7.910000e-001, 6.912000e-001, 6.912000e-001), +.Dim = c(49,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, WAA.jan1 = structure(c( +1.730000e-001, 1.700000e-001, 1.850000e-001, 2.050000e-001, 1.850000e-001, 2.060000e-001, 1.550000e-001, 1.830000e-001, 1.020000e-001, 2.100000e-001, 1.500000e-001, 1.520000e-001, 1.470000e-001, 1.550000e-001, 2.270000e-001, 3.020000e-001, 3.000000e-002, 1.990000e-001, 4.900000e-002, 6.500000e-002, 8.600000e-002, 4.700000e-002, 3.600000e-002, 6.500000e-002, 9.700000e-002, +1.150000e-001, 8.400000e-002, 1.330000e-001, 9.600000e-002, 1.190000e-001, 5.400000e-002, 9.200000e-002, 6.500000e-002, 7.100000e-002, 7.200000e-002, 1.110000e-001, 6.400000e-002, 5.900000e-002, 1.020000e-001, 8.250000e-002, 1.029000e-001, 1.056000e-001, 1.900000e-002, 9.060000e-002, 8.400000e-002, 1.294000e-001, 8.270000e-002, 7.860000e-002, 7.860000e-002, +2.710000e-001, 2.540000e-001, 2.420000e-001, 2.570000e-001, 2.540000e-001, 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"2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, index.WAA.ind04 = structure(c( +1.970000e-001, 1.910000e-001, 2.060000e-001, 2.200000e-001, 2.040000e-001, 2.240000e-001, 1.770000e-001, 1.980000e-001, 1.260000e-001, 2.210000e-001, 1.660000e-001, 1.720000e-001, 1.700000e-001, 1.750000e-001, 2.400000e-001, 2.800000e-001, 4.800000e-002, 2.020000e-001, 7.400000e-002, 9.200000e-002, 1.090000e-001, 6.600000e-002, 5.900000e-002, 8.800000e-002, 1.230000e-001, +1.430000e-001, 1.130000e-001, 1.660000e-001, 1.260000e-001, 1.470000e-001, 7.900000e-002, 1.200000e-001, 8.800000e-002, 9.500000e-002, 9.600000e-002, 1.360000e-001, 8.900000e-002, 8.300000e-002, 1.200000e-001, 1.059000e-001, 1.346000e-001, 1.370000e-001, 3.430000e-002, 1.099000e-001, 1.018000e-001, 1.357000e-001, 9.840000e-002, 9.142000e-002, 9.142000e-002, +2.880000e-001, 2.890000e-001, 2.730000e-001, 2.870000e-001, 2.730000e-001, 2.780000e-001, 2.890000e-001, 2.630000e-001, 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"2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, index.WAA.ind05 = structure(c( +1.970000e-001, 1.910000e-001, 2.060000e-001, 2.200000e-001, 2.040000e-001, 2.240000e-001, 1.770000e-001, 1.980000e-001, 1.260000e-001, 2.210000e-001, 1.660000e-001, 1.720000e-001, 1.700000e-001, 1.750000e-001, 2.400000e-001, 2.800000e-001, 4.800000e-002, 2.020000e-001, 7.400000e-002, 9.200000e-002, 1.090000e-001, 6.600000e-002, 5.900000e-002, 8.800000e-002, 1.230000e-001, +1.430000e-001, 1.130000e-001, 1.660000e-001, 1.260000e-001, 1.470000e-001, 7.900000e-002, 1.200000e-001, 8.800000e-002, 9.500000e-002, 9.600000e-002, 1.360000e-001, 8.900000e-002, 8.300000e-002, 1.200000e-001, 1.059000e-001, 1.346000e-001, 1.370000e-001, 3.430000e-002, 1.099000e-001, 1.018000e-001, 1.357000e-001, 9.840000e-002, 9.142000e-002, 9.142000e-002, +2.880000e-001, 2.890000e-001, 2.730000e-001, 2.870000e-001, 2.730000e-001, 2.780000e-001, 2.890000e-001, 2.630000e-001, 2.520000e-001, 2.370000e-001, 2.550000e-001, 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"2", "3", "4", "5", "6"))) + +, N.age = structure(c( +3.409928e+004, 1.370219e+004, 4.138821e+004, 1.874739e+004, 7.001298e+004, 7.270003e+004, 5.476856e+004, 6.462522e+004, 1.684146e+005, 8.506767e+004, 2.125099e+004, 2.736950e+004, 3.196354e+004, 9.704596e+003, 2.101610e+004, 1.952071e+005, 4.210177e+004, 1.105862e+004, 3.404140e+003, 3.068497e+003, 4.251859e+003, 8.027088e+003, 6.004239e+003, 4.540282e+003, 1.089048e+004, +6.176444e+003, 9.956763e+003, 5.129462e+003, 4.662978e+003, 1.742473e+003, 1.891322e+003, 2.412469e+003, 7.704881e+003, 5.616614e+003, 2.294939e+003, 3.611080e+003, 3.353126e+003, 2.852156e+003, 6.089375e+003, 1.670548e+003, 1.103677e+003, 1.785485e+002, 8.807558e+001, 6.585128e+001, 1.788250e+002, 2.257910e+002, 1.154797e+003, 4.490695e+002, 4.395983e+003, +2.220294e+004, 2.169607e+004, 8.139732e+003, 2.501683e+004, 1.190137e+004, 4.339852e+004, 4.530866e+004, 3.422632e+004, 4.101083e+004, 1.083299e+005, 5.399161e+004, 1.304967e+004, 1.617495e+004, 1.947239e+004, 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discard.pred = structure(c( +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000), +.Dim = c(1,49), +.Dimnames = list( +c("1"), +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", 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+0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000), +.Dim = c(1,49), +.Dimnames = list( +c("1"), +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"))) + +, catch.comp.mats = structure(list( + +catch.fleet1.ob = structure(c( +5.456915e-003, 1.693968e-002, 4.444014e-001, 2.097826e-002, 3.097643e-001, 3.123255e-001, 6.535488e-003, 4.236780e-002, 1.845119e-003, 3.115818e-003, 3.557809e-002, 1.323012e-002, 1.328365e-001, 2.908061e-002, 1.758537e-001, 6.218812e-001, 9.907232e-004, 3.881273e-003, 2.446166e-002, 6.363097e-002, 6.845408e-003, 8.352997e-002, 1.025641e-003, 2.077922e-003, 6.062767e-003, 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5.111111e-001, 4.117647e-001, 8.333333e-002, 0.000000e+000, 0.000000e+000), +.Dim = c(49,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, catch.fleet1.pr = structure(c( +3.132939e-002, 2.796207e-002, 2.039458e-001, 7.153719e-002, 2.475671e-001, 1.573691e-001, 9.540230e-002, 1.104244e-001, 2.150976e-001, 6.778245e-002, 2.210112e-002, 7.350392e-002, 1.497529e-001, 4.730941e-002, 1.455346e-001, 5.462708e-001, 2.371422e-002, 5.547739e-003, 4.101205e-003, 1.082679e-002, 2.875891e-002, 6.914554e-002, 3.862323e-002, 2.059721e-002, 5.443622e-002, +3.268972e-002, 4.872634e-002, 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1.039010e-001, 3.773140e-002, 2.276381e-002), +.Dim = c(49,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"), +c("1", "2", "3", "4", "5", "6"))) + +, discard.fleet1.ob = structure(c( +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 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5.576120e-001, 5.317092e-001, 4.998005e-001, 6.759917e-001, 8.016667e-001), +.Names = NULL) + +, ind03 = structure(c( +5.542743e-001, 5.438054e-001, 5.934923e-001, 4.867032e-001, 4.767772e-001, 4.338595e-001, 3.696100e-001, 5.021231e-001, 4.101330e-001, 5.328431e-001, 4.651650e-001, 4.431296e-001, 4.793916e-001, 4.663560e-001, 5.502092e-001, 4.147130e-001, 4.680557e-001, 4.871222e-001, 4.454600e-001, 5.756494e-001, 5.066738e-001, 5.330050e-001, 7.330523e-001, 5.984852e-001, 4.659308e-001, +5.196606e-001, 6.852880e-001, 6.073415e-001, 5.809532e-001, 5.569769e-001, 6.239471e-001, 4.902192e-001, 4.133932e-001, 4.705166e-001, 5.287888e-001, 5.176962e-001, 4.779585e-001, 6.077216e-001, 7.235245e-001, 5.548312e-001, 4.670361e-001, 6.485361e-001, 5.093144e-001, 6.434721e-001, 5.899456e-001, 4.719573e-001, 8.681082e-001), +.Names = NULL) + +, ind04 = structure(c( +5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001, 5.140870e-001), +.Names = NULL) + +, ind05 = structure(c( +6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001, 6.314872e-001), +.Names = NULL) + +), .Names = c("ind01", "ind02", "ind03", "ind04", "ind05")) + +, index.obs = structure(list( + +ind01 = structure(c( +1.674221e+004, 8.234540e+003, 1.494733e+004, 1.197186e+004, 1.335986e+004, 2.154839e+004, 1.470884e+004, 1.981878e+004, 1.260849e+004, 1.610102e+004, 1.061856e+004, 5.988320e+003, 3.010210e+003, 4.145320e+003, 3.378670e+004, 2.097274e+004), +.Names = NULL) + +, ind02 = structure(c( +4.599328e+004, 1.831868e+004, 4.839410e+003, 5.942610e+003, 6.925370e+003, 1.873068e+004, 5.889550e+003, 1.477438e+004, 2.700045e+004, 4.352388e+004, 3.045624e+004, 8.397880e+003, 6.074290e+003, 7.020750e+003, 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3.443400e+003, 1.953920e+003, 3.031200e+002, 3.025860e+003, 4.060280e+003, 1.983450e+003, 3.774110e+003, 1.986458e+003, 1.396288e+004, 2.327434e+003, 6.435453e+003, 1.728658e+003, 4.271380e+002, 1.141950e+002, 5.033060e+002, 1.197090e+002, 1.938090e+002, 3.205300e+001), +.Names = NULL) + +, ind04 = structure(c( +3.360000e+001, 2.730000e+001, 3.820000e+001, 1.125000e+002, 6.820000e+001, 4.730000e+001, 1.660000e+002, 5.150000e+001, 1.660000e+001, 2.220000e+001, 7.020000e+001), +.Names = NULL) + +, ind05 = structure(c( +4.373890e+001, 5.998100e+001, 2.465024e+002, 1.218222e+002, 7.870660e+001, 5.829530e+001, 4.890820e+001, 4.986990e+001, 6.645230e+001, 2.039022e+002, 2.281795e+002, 6.115700e+000, 2.352240e+001, 1.081660e+001, 4.295900e+000, 3.203500e+000, 1.421200e+000, 6.083000e-002), +.Names = NULL) + +), .Names = c("ind01", "ind02", "ind03", "ind04", "ind05")) + +, index.pred = structure(list( + +ind01 = structure(c( +1.966804e+004, 9.607514e+003, 9.324535e+003, 1.305066e+004, 1.658910e+004, 1.470273e+004, 1.684301e+004, 1.715186e+004, 1.759275e+004, 1.532196e+004, 1.134167e+004, 8.328957e+003, 6.019940e+003, 5.795615e+003, 1.113500e+004, 1.474386e+004), +.Names = NULL) + +, ind02 = structure(c( +4.329973e+004, 2.194753e+004, 7.601443e+003, 8.923474e+003, 9.165244e+003, 1.530885e+004, 1.936756e+004, 1.990546e+004, 2.521872e+004, 4.284318e+004, 3.580657e+004, 1.536062e+004, 7.757902e+003, 7.333748e+003, 5.006463e+003, 9.540512e+003, 3.715101e+004, 2.900248e+004, 1.055232e+004, 3.491349e+003, 1.834913e+003, 1.737431e+003, 2.434400e+003, 2.969111e+003, 2.707823e+003, +3.105180e+003, 2.994369e+003, 3.201576e+003, 2.629186e+003, 2.051294e+003, 1.499357e+003, 1.141846e+003, 1.127518e+003, 2.095551e+003, 2.610559e+003, 2.410594e+003, 2.237055e+003, 2.225806e+003, 2.349521e+003, 2.687468e+003, 2.136190e+003, 1.376847e+003, 6.353405e+002, 2.597750e+002, 9.526664e+001, 6.890027e+001, 1.106682e+002, 4.751162e+002), +.Names = NULL) + +, ind03 = structure(c( 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7.437076e+001, 5.852059e+001, 3.031582e+001, 2.693003e+001, 4.362383e+001, 6.297277e+001, 5.912613e+001, 6.238446e+001, 6.547594e+001, 5.639033e+001, 1.846731e+001, 7.068222e+000, 2.657707e+000, 1.562038e+000, 1.881390e+000, 9.889381e+000), +.Names = NULL) + +), .Names = c("ind01", "ind02", "ind03", "ind04", "ind05")) + +, index.std.resid = structure(list( + +ind01 = structure(c( +-4.613765e-001, -4.417433e-001, 1.351755e+000, -2.471561e-001, -6.201599e-001, 1.095042e+000, -3.881149e-001, 4.140010e-001, -9.542438e-001, 1.420705e-001, -1.887202e-001, -9.451077e-001, -1.985357e+000, -9.599872e-001, 3.179624e+000, 1.009474e+000), +.Names = NULL) + +, ind02 = structure(c( +1.517636e-001, -3.851584e-001, -1.019786e+000, -8.626237e-001, -5.314112e-001, 4.585532e-001, -2.503457e+000, -7.331496e-001, 1.550544e-001, 3.520270e-002, -3.864005e-001, -1.372284e+000, -4.824555e-001, -1.091497e-001, -2.343299e+000, -3.056587e+000, -1.753903e+000, -2.486037e-001, 2.093373e-001, 1.511062e-001, 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"2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021"))) + +, RMSE.n = structure(list( +rmse.n.catch.tot = 49, +rmse.n.discard.tot = 0, +rmse.n.ind01 = 16, +rmse.n.ind02 = 48, +rmse.n.ind03 = 47, +rmse.n.ind04 = 11, +rmse.n.ind05 = 18, +rmse.n.ind.total = 140, +rmse.n.N.year1 = 0.000000e+000, +rmse.n.Fmult.year1 = 0.000000e+000, +rmse.n.Fmult.devs.total = 0.000000e+000, +rmse.n.recruit.devs = 0.000000e+000, +rmse.n.fleet.sel.params = 0.000000e+000, +rmse.n.index.sel.params = 0.000000e+000, +rmse.n.q.year1 = 0.000000e+000, +rmse.n.q.devs = 0.000000e+000, +rmse.n.SR.steepness = 0.000000e+000, +rmse.n.SR.scaler = 0.000000e+000), +.Names = c("rmse.n.catch.tot", "rmse.n.discard.tot", "rmse.n.ind01", "rmse.n.ind02", "rmse.n.ind03", "rmse.n.ind04", "rmse.n.ind05", "rmse.n.ind.total", "rmse.n.N.year1", "rmse.n.Fmult.year1", "rmse.n.Fmult.devs.total", "rmse.n.recruit.devs", "rmse.n.fleet.sel.params", "rmse.n.index.sel.params", "rmse.n.q.year1", "rmse.n.q.devs", "rmse.n.SR.steepness", "rmse.n.SR.scaler")) + +, RMSE = structure(list( +rmse.catch.tot = 4.980141e-001, +rmse.discard.tot = 0, +rmse.ind01 = 1.174041e+000, +rmse.ind02 = 1.307136e+000, +rmse.ind03 = 1.527709e+000, +rmse.ind04 = 1.237837e+000, +rmse.ind05 = 2.317516e+000, +rmse.ind.total = 1.528983e+000, +rmse.N.year1 = 0.000000e+000, +rmse.Fmult.year1 = 0.000000e+000, +rmse.Fmult.devs.total = 0.000000e+000, +rmse.recruit.devs = 0.000000e+000, +rmse.fleet.sel.params = 0.000000e+000, +rmse.index.sel.params = 0.000000e+000, +rmse.q.year1 = 0.000000e+000, +rmse.q.devs = 0.000000e+000, +rmse.SR.steepness = 0.000000e+000, +rmse.SR.scaler = 0.000000e+000), +.Names = c("rmse.catch.tot", "rmse.discard.tot", "rmse.ind01", "rmse.ind02", "rmse.ind03", "rmse.ind04", "rmse.ind05", "rmse.ind.total", "rmse.N.year1", "rmse.Fmult.year1", "rmse.Fmult.devs.total", "rmse.recruit.devs", "rmse.fleet.sel.params", "rmse.index.sel.params", "rmse.q.year1", "rmse.q.devs", "rmse.SR.steepness", "rmse.SR.scaler")) + +, Neff.stage2.mult = structure(list( + +Neff.stage2.mult.catch = structure(c( +3.570134e-001), +.Names = NULL) + +, Neff.stage2.mult.discard = structure(c( +1.000000e+000), +.Names = NULL) + +, Neff.stage2.mult.index = structure(c( +1.003711e+000, 5.584156e-001, 5.218514e-001, 1.000000e+000, 1.000000e+000), +.Names = NULL) + +), .Names = c("Neff.stage2.mult.catch", "Neff.stage2.mult.discard", "Neff.stage2.mult.index")) + + +### Calling close_r_file -- last call in program. +), + +.Names = c("info", "parms", "options", "like", "like.additional", "initial.guesses", "control.parms", "sel.input.mats", "WAA.mats", "M.age", "maturity", "fecundity", "N.age", "Z.age", "F.age", "catch.obs", "catch.pred", "catch.std.resid", "discard.obs", "discard.pred", "discard.std.resid", "catch.comp.mats", "fleet.sel.blocks", "fleet.sel.start.age", "fleet.sel.end.age", + "fleet.sel.option", "fleet.sel.mats", "fleet.Fmult", "fleet.FAA", "fleet.prop.release", "fleet.catch.Neff.init", "fleet.catch.Neff.est", "fleet.discard.Neff.init", "fleet.discard.Neff.est", "q.indices", "F.report", "tot.jan1.B", "SSB", "exploitable.B", "Fref", "SR.parms", "SR.resids", "SR.annual.parms", "index.sel", "index.nobs", "index.year.counter", "index.year", "index.cv", "index.sigma", "index.obs", + "index.pred", "index.std.resid", "index.comp.mats", "index.Neff.init", "index.Neff.est", "RMSE.n", "RMSE", "Neff.stage2.mult")) diff --git a/content/data_files/NEFSC_YT_SIMPLIFIED.RDAT b/content/data_files/NEFSC_YT_SIMPLIFIED.RDAT new file mode 100644 index 0000000..a8a2b20 --- /dev/null +++ b/content/data_files/NEFSC_YT_SIMPLIFIED.RDAT @@ -0,0 +1,1815 @@ +### This file written with ADMB2R version 1.15 +### Read into R or S with x=dget('c:\users\chris.legault\documents\fims_demo\asap2fims\snemayt_2019\asap3.rdat') + +structure(list( + +info = structure(list( +date = "Friday, 08 Dec 2023 at 13:41:34", +program = "ASAP3"), +.Names = c("date", "program")) + +, parms = structure(list( +styr = 1973, +endyr = 2019, +nyears = 47, +nages = 6, +nfleets = 1, +nselblocks = 1, +navailindices = 5, +nindices = 2), +.Names = c("styr", "endyr", "nyears", "nages", "nfleets", "nselblocks", "navailindices", "nindices")) + +, options = structure(list( +isfecund = 0.000000e+000, +frac.yr.spawn = 0.000000e+000, +do.projections = 0, +ignore.guesses = 0.000000e+000, +Freport.agemin = 4, +Freport.agemax = 5, +Freport.wtopt = 1, +use.likelihood.constants = 1, +Fmult.max.value = 5.000000e+000, +N.year1.flag = 1, +do.mcmc = 0), +.Names = c("isfecund", "frac.yr.spawn", "do.projections", "ignore.guesses", "Freport.agemin", "Freport.agemax", "Freport.wtopt", "use.likelihood.constants", "Fmult.max.value", "N.year1.flag", "do.mcmc")) + +, like = structure(list( +lk.total = 2.410844e+003, +lk.catch.total = 2.797145e+002, +lk.discard.total = 0.000000e+000, +lk.index.fit.total = 7.408331e+002, +lk.catch.age.comp = 6.427388e+002, +lk.discards.age.comp = 0.000000e+000, +lk.index.age.comp = 7.475581e+002, +lk.sel.param.total = 0.000000e+000, +lk.index.sel.param.total = 0.000000e+000, +lk.q.year1 = 0.000000e+000, +lk.q.devs = 0.000000e+000, +lk.Fmult.year1.total = 0.000000e+000, +lk.Fmult.devs.total = 0.000000e+000, +lk.N.year1 = 0.000000e+000, +lk.Recruit.devs = 0.000000e+000, +lk.SR.steepness = 0.000000e+000, +lk.SR.scaler = 0.000000e+000, +lk.Fmult.Max.penalty = 0.000000e+000, +lk.F.penalty = 0.000000e+000), +.Names = c("lk.total", "lk.catch.total", "lk.discard.total", "lk.index.fit.total", "lk.catch.age.comp", "lk.discards.age.comp", "lk.index.age.comp", "lk.sel.param.total", "lk.index.sel.param.total", "lk.q.year1", "lk.q.devs", "lk.Fmult.year1.total", "lk.Fmult.devs.total", "lk.N.year1", "lk.Recruit.devs", "lk.SR.steepness", "lk.SR.scaler", "lk.Fmult.Max.penalty", "lk.F.penalty")) + +, like.additional = structure(list( +nfleets = 1, +nindices = 2, +nselparms = 2, +nindexselparms = 4, +lk.index.fit.ind01 = 3.714976e+002, +lk.index.fit.ind02 = 3.693354e+002, +lk.q.year1.ind01 = -0.000000e+000, +lk.q.year1.ind02 = -0.000000e+000, +lk.q.devs.ind01 = 0.000000e+000, +lk.q.devs.ind02 = 0.000000e+000, +lk.sel.param.01 = 0.000000e+000, +lk.sel.param.02 = 0.000000e+000, +lk.indexsel.param.01 = 0.000000e+000, +lk.indexsel.param.02 = 0.000000e+000, +lk.indexsel.param.03 = 0.000000e+000, +lk.indexsel.param.04 = 0.000000e+000), +.Names = c("nfleets", "nindices", "nselparms", "nindexselparms", "lk.index.fit.ind01", "lk.index.fit.ind02", "lk.q.year1.ind01", "lk.q.year1.ind02", "lk.q.devs.ind01", "lk.q.devs.ind02", "lk.sel.param.01", "lk.sel.param.02", "lk.indexsel.param.01", "lk.indexsel.param.02", "lk.indexsel.param.03", "lk.indexsel.param.04")) + +, initial.guesses = structure(list( + +SR.inits = structure(list( +is.SR.scaler.R = 1.000000e+000, +SR.scaler.init = 1.000000e+006, +SR_steepness.init = 9.900000e-001), +.Names = c("is.SR.scaler.R", "SR.scaler.init", "SR_steepness.init")) + +, NAA.year1.init = structure(c( +4.249100e+004, 1.812800e+004, 2.932200e+004, 1.683400e+004, 1.102700e+004, 1.326400e+004), +.Names = NULL) + +, Fmult.year1.init = structure(c( +7.000000e-001), +.Names = NULL) + +, q.year1.init = structure(c( +2.000000e-001, 2.000000e-001), +.Names = NULL) + +, release.mort = structure(c( +1.000000e+000), +.Names = NULL) + +, index.use.flag = structure(c( +0, 1, 1, 0, 0), +.Names = NULL) + +), .Names = c("SR.inits", "NAA.year1.init", "Fmult.year1.init", "q.year1.init", "release.mort", "index.use.flag")) + +, control.parms = structure(list( + +phases = structure(list( +phase.Fmult.year1 = 1, +phase.Fmult.devs = 2, +phase.recruit.devs = 3, +phase.N.year1.devs = 4, +phase.q.year1 = 1, +phase.q.devs = -7, +phase.SR.scaler = 1, +phase.steepness = -3), +.Names = c("phase.Fmult.year1", "phase.Fmult.devs", "phase.recruit.devs", "phase.N.year1.devs", "phase.q.year1", "phase.q.devs", "phase.SR.scaler", "phase.steepness")) + +, singles = structure(list( +lambda.N.year1.devs = 0.000000e+000, +N.year1.cv = 9.000000e-001, +lambda.recruit.devs = 0.000000e+000, +lambda.steepness = 0.000000e+000, +steepness.cv = 9.000000e-001, +lambda.SR.scaler = 0.000000e+000, +SR.scaler.cv = 9.000000e-001), +.Names = c("lambda.N.year1.devs", "N.year1.cv", "lambda.recruit.devs", "lambda.steepness", "steepness.cv", "lambda.SR.scaler", "SR.scaler.cv")) + +, mcmc = structure(list( +mcmc.nyear.opt = 0, +mcmc.n.boot = 0, +mcmc.n.thin = 0, +mcmc.seed = 0, +fillR.opt = 2, +Ravg.start = 0, +Ravg.end = 0), +.Names = c("mcmc.nyear.opt", "mcmc.n.boot", "mcmc.n.thin", "mcmc.seed", "fillR.opt", "Ravg.start", "Ravg.end")) + +, recruit.cv = structure(c( +6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, +6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001, 6.000000e-001), +.Names = NULL) + +, lambda.ind = structure(c( +1.000000e+000, 1.000000e+000), +.Names = NULL) + +, lambda.catch.tot = structure(c( +1.000000e+000), +.Names = NULL) + +, catch.tot.cv = structure(c( +1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, +1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001, 1.000000e-001), +.Dim = c(47,1), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"), +c("1"))) + +, lambda.Discard.tot = structure(c( +0.000000e+000), +.Names = NULL) + +, discard.tot.cv = structure(c( +1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, +1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002), +.Dim = c(47,1), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"), +c("1"))) + +, lambda.Fmult.year1 = structure(c( +0.000000e+000), +.Names = NULL) + +, Fmult.year1.cv = structure(c( +9.000000e-001), +.Names = NULL) + +, lambda.Fmult.devs = structure(c( +0.000000e+000), +.Names = NULL) + +, Fmult.devs.cv = structure(c( +9.000000e-001), +.Names = NULL) + +, lambda.q.year1 = structure(c( +0.000000e+000, 0.000000e+000), +.Names = NULL) + +, q.year1.cv = structure(c( +9.000000e-001, 9.000000e-001), +.Names = NULL) + +, lambda.q.devs = structure(c( +0.000000e+000, 0.000000e+000), +.Names = NULL) + +, q.devs.cv = structure(c( +9.000000e-001, 9.000000e-001), +.Names = NULL) + +, directed.fleet = structure(c( +1), +.Names = NULL) + +, WAA.point.bio = structure(c( +1, 1, 1, 1, 1, 1), +.Names = NULL) + +, index.units.aggregate = structure(c( +1.000000e+000, 1.000000e+000), +.Names = NULL) + +, index.units.proportions = structure(c( +2.000000e+000, 2.000000e+000), +.Names = NULL) + +, index.WAA.point = structure(c( +3, 3), +.Names = NULL) + +, index.month = structure(c( +1.000000e+000, 1.000000e+001), +.Names = NULL) + +, index.sel.start.age = structure(c( +1.000000e+000, 1.000000e+000), +.Names = NULL) + +, index.sel.end.age = structure(c( +6.000000e+000, 6.000000e+000), +.Names = NULL) + +, index.sel.choice = structure(c( +-1.000000e+000, -1.000000e+000), +.Names = NULL) + +, index.age.comp.flag = structure(c( +1, 1), +.Names = NULL) + +), .Names = c("phases", "singles", "mcmc", "recruit.cv", "lambda.ind", "lambda.catch.tot", "catch.tot.cv", "lambda.Discard.tot", "discard.tot.cv", "lambda.Fmult.year1", "Fmult.year1.cv", "lambda.Fmult.devs", "Fmult.devs.cv", "lambda.q.year1", "q.year1.cv", "lambda.q.devs", "q.devs.cv", "directed.fleet", "WAA.point.bio", "index.units.aggregate", "index.units.proportions", "index.WAA.point", "index.month", "index.sel.start.age", "index.sel.end.age", + "index.sel.choice", "index.age.comp.flag")) + +, sel.input.mats = structure(list( + +fleet.sel.ini = structure(c( +5.000000e-001, 7.500000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 2.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +5.000000e+000, 5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 2.000000e+000, 2.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 5.000000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+002, 1.000000e+002, 1.000000e+002, 1.000000e+002), +.Dim = c(12,4), +.Dimnames = list( +c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"), +c("1", "2", "3", "4"))) + +, index.sel.ini = structure(c( +5.000000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e-001, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 2.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e-001, +1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 1.000000e+000, 2.000000e+000, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.200000e-003, 6.836000e-001, 9.854000e-001, 9.970000e-001, 9.963000e-001, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.200000e-003, 6.836000e-001, +9.854000e-001, 9.970000e-001, 9.963000e-001, 1.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, +5.000000e+000, 5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 5.000000e+000, 5.000000e+000, 5.000000e+000, -5.000000e+000, -5.000000e+000, -5.000000e+000, 2.000000e+000, 2.000000e+000, 0.000000e+000, 0.000000e+000, 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"2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"))) + +, catch.std.resid = structure(c( +-1.006474e-001, +5.183824e-001, +4.374574e-001, +4.793542e-002, +-2.789751e-001, +4.927829e-001, +6.655944e-001, +-2.022629e-001, +-2.575847e-001, +-1.416324e-001, +4.960476e-001, +8.776312e-002, +2.254678e-001, +1.050034e+000, +1.084782e-001, +3.541589e-002, +6.482044e-001, +-6.718326e-002, +-2.003755e-002, +6.566446e-001, +2.993653e-001, +5.108935e-001, +-1.097526e-001, +-4.449082e-001, +-2.842401e-001, +-2.555340e-001, +-3.963114e-001, +-2.612352e-003, +-4.863516e-001, +5.451806e-001, +3.885269e-001, +7.085599e-001, +4.329054e-002, +-3.694647e-001, +-3.195315e-001, +-3.769039e-001, +-2.922067e-001, +-2.330338e-001, +-5.894955e-001, +-8.213856e-001, +-8.350963e-001, +-5.289176e-001, +-3.231262e-001, +-7.184249e-002, +-9.176365e-002, +-5.885528e-002, +-6.367863e-003), +.Dim = c(1,47), +.Dimnames = list( +c("1"), +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"))) + +, discard.obs = structure(c( +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, +0.000000e+000, 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"2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"))) + +, catch.comp.mats = structure(list( + +catch.fleet1.ob = structure(c( +5.456915e-003, 1.693968e-002, 4.444014e-001, 2.097826e-002, 3.097643e-001, 3.123255e-001, 6.535488e-003, 4.236780e-002, 1.845119e-003, 3.115818e-003, 3.557809e-002, 1.323012e-002, 1.328365e-001, 2.908061e-002, 1.758537e-001, 6.218812e-001, 9.907232e-004, 3.881273e-003, 2.446166e-002, 6.363097e-002, 6.845408e-003, 8.352997e-002, 1.025641e-003, 2.077922e-003, 6.062767e-003, +1.712329e-003, 2.121771e-002, 5.562423e-003, 0.000000e+000, 3.417969e-003, 2.100840e-003, 2.046414e-001, 4.318489e-002, 5.094905e-002, 7.211538e-003, 2.349256e-002, 1.521739e-002, 5.253940e-003, 2.110818e-002, 1.819620e-002, 2.187286e-002, 6.559767e-003, 6.691947e-004, 0.000000e+000, 6.666667e-002, 5.882353e-002, 1.250000e-001, +1.447847e-001, 5.557634e-001, 2.204036e-001, 6.691304e-001, 2.961716e-001, 5.257679e-001, 6.320450e-001, 4.458531e-001, 3.596353e-001, 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2016, 2017, 2018, 2019), +recruits = c(2.735023e+004, 1.446194e+004, 5.385304e+004, 2.630331e+004, 9.730808e+004, 9.209226e+004, 6.937174e+004, 8.690927e+004, 2.098913e+005, 8.397269e+004, 2.008875e+004, 3.146710e+004, 3.536270e+004, 1.131278e+004, 2.565348e+004, 2.101321e+005, 4.783534e+004, 1.553847e+004, 4.711568e+003, 4.015674e+003, 4.942271e+003, 9.889844e+003, 7.724175e+003, 6.324389e+003, 1.408113e+004, +8.799348e+003, 1.388973e+004, 8.025572e+003, 6.525992e+003, 2.580008e+003, 2.565408e+003, 3.243273e+003, 9.172707e+003, 6.990939e+003, 2.855663e+003, 4.249769e+003, 3.706037e+003, 3.094825e+003, 6.856937e+003, 2.039175e+003, 1.527097e+003, 2.958549e+002, 1.962491e+002, 2.081628e+002, 8.813495e+002, 1.407667e+003, 1.270313e+004), +R.no.devs = c(9.307809e+003, 9.307813e+003, 9.303542e+003, 9.280967e+003, 9.278384e+003, 9.282678e+003, 9.293144e+003, 9.298542e+003, 9.298644e+003, 9.300312e+003, 9.307127e+003, 9.306995e+003, 9.297267e+003, 9.274555e+003, 9.270390e+003, 9.248671e+003, 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"1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"), +class = "data.frame") + +, index.sel = structure(c( +4.800241e-002, 4.103419e-001, +5.875034e-001, 1.000000e+000, +9.757470e-001, 1.000000e+000, +9.991219e-001, 1.000000e+000, +9.999699e-001, 1.000000e+000, +1.000000e+000, 1.000000e+000), +.Dim = c(2,6), +.Dimnames = list( +c("1", "2"), +c("1", "2", "3", "4", "5", "6"))) + +, index.nobs = structure(c( +47, 47), +.Names = NULL) + +, index.year.counter = structure(list( + +ind01 = structure(c( +1.000000e+000, 2.000000e+000, 3.000000e+000, 4.000000e+000, 5.000000e+000, 6.000000e+000, 7.000000e+000, 8.000000e+000, 9.000000e+000, 1.000000e+001, 1.100000e+001, 1.200000e+001, 1.300000e+001, 1.400000e+001, 1.500000e+001, 1.600000e+001, 1.700000e+001, 1.800000e+001, 1.900000e+001, 2.000000e+001, 2.100000e+001, 2.200000e+001, 2.300000e+001, 2.400000e+001, 2.500000e+001, 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4.200000e+001, 4.300000e+001, 4.400000e+001, 4.500000e+001, 4.600000e+001, 4.700000e+001), +.Names = NULL) + +), .Names = c("ind01", "ind02")) + +, index.year = structure(list( + +ind01 = structure(c( +1.973000e+003, 1.974000e+003, 1.975000e+003, 1.976000e+003, 1.977000e+003, 1.978000e+003, 1.979000e+003, 1.980000e+003, 1.981000e+003, 1.982000e+003, 1.983000e+003, 1.984000e+003, 1.985000e+003, 1.986000e+003, 1.987000e+003, 1.988000e+003, 1.989000e+003, 1.990000e+003, 1.991000e+003, 1.992000e+003, 1.993000e+003, 1.994000e+003, 1.995000e+003, 1.996000e+003, 1.997000e+003, +1.998000e+003, 1.999000e+003, 2.000000e+003, 2.001000e+003, 2.002000e+003, 2.003000e+003, 2.004000e+003, 2.005000e+003, 2.006000e+003, 2.007000e+003, 2.008000e+003, 2.009000e+003, 2.010000e+003, 2.011000e+003, 2.012000e+003, 2.013000e+003, 2.014000e+003, 2.015000e+003, 2.016000e+003, 2.017000e+003, 2.018000e+003, 2.019000e+003), +.Names = NULL) + +, ind02 = structure(c( +1.973000e+003, 1.974000e+003, 1.975000e+003, 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1.176819e-002, 5.279613e-003, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 0.000000e+000, 1.676990e-002, 4.361658e-002, 0.000000e+000, 0.000000e+000, +8.474931e-003, 0.000000e+000, 1.034881e-002, 0.000000e+000, 0.000000e+000, 1.458614e-002, 9.402527e-002, 0.000000e+000, 0.000000e+000, 0.000000e+000, 7.551429e-003, 0.000000e+000, 0.000000e+000, 8.539447e-003, 3.450744e-002, 2.950727e-002, 6.132430e-002, 1.227374e-001, 1.323906e-001, 1.436286e-001, 0.000000e+000, 0.000000e+000), +.Dim = c(47,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"), +c("1", "2", "3", "4", "5", "6"))) + +, ind02.pr = structure(c( +1.277891e-001, 1.580766e-001, 6.087962e-001, 2.457792e-001, 6.360393e-001, 4.229391e-001, 3.227775e-001, 3.858696e-001, 5.357709e-001, 1.985531e-001, 9.847941e-002, 3.556707e-001, 4.388727e-001, 1.944455e-001, 5.067757e-001, 8.391031e-001, 1.394285e-001, 1.129704e-001, 9.496912e-002, 2.021634e-001, 3.103619e-001, 4.747505e-001, 2.685255e-001, 2.205064e-001, 4.471121e-001, +2.510922e-001, 3.939244e-001, 2.364277e-001, 2.619133e-001, 1.423520e-001, 2.034672e-001, 3.107499e-001, 5.395683e-001, 2.836004e-001, 1.237654e-001, 2.207815e-001, 2.014160e-001, 1.714565e-001, 3.292724e-001, 1.012232e-001, 1.128606e-001, 4.023214e-002, 5.731428e-002, 1.065626e-001, 4.092650e-001, 3.783768e-001, 7.571879e-001, +2.092669e-001, 3.744966e-001, 2.011876e-001, 6.620788e-001, 2.168611e-001, 5.254020e-001, 5.257290e-001, 4.082858e-001, 3.197823e-001, 6.632409e-001, 4.991482e-001, 2.614105e-001, 4.450323e-001, 6.695189e-001, 2.702023e-001, 1.331633e-001, 8.212760e-001, 4.063725e-001, 3.810976e-001, 2.937378e-001, 3.532600e-001, 3.331163e-001, 5.331829e-001, 4.104835e-001, 2.840284e-001, +5.662605e-001, 3.422615e-001, 5.563811e-001, 4.224103e-001, 5.001263e-001, 2.955533e-001, 3.456068e-001, 2.806518e-001, 5.632559e-001, 4.692139e-001, 2.300629e-001, 3.627139e-001, 3.327516e-001, 2.393669e-001, 5.306500e-001, 2.291405e-001, 3.024247e-001, 1.256209e-001, 1.520336e-001, 1.526646e-001, 3.933080e-001, 1.424142e-001, +3.111614e-001, 1.372718e-001, 1.041831e-001, 5.506271e-002, 1.336759e-001, 3.546326e-002, 1.422798e-001, 1.699757e-001, 1.027672e-001, 1.017569e-001, 3.511987e-001, 2.516500e-001, 6.097555e-002, 1.177771e-001, 1.973015e-001, 1.741600e-002, 3.399817e-002, 4.652027e-001, 2.938875e-001, 2.608088e-001, 1.475437e-001, 1.083950e-001, 1.323537e-001, 2.755082e-001, 1.529459e-001, +1.038505e-001, 2.101527e-001, 1.295533e-001, 2.467484e-001, 2.268984e-001, 3.156909e-001, 1.438262e-001, 9.871289e-002, 9.802277e-002, 3.267749e-001, 3.062420e-001, 1.363373e-001, 2.308608e-001, 1.762011e-001, 1.366642e-001, 4.045311e-001, 1.889820e-001, 2.905679e-001, 1.115957e-001, 7.961206e-002, 5.945224e-002, 6.264519e-002, +1.556031e-001, 1.567487e-001, 2.633888e-002, 2.079368e-002, 8.161328e-003, 1.480091e-002, 6.477601e-003, 3.380040e-002, 3.464311e-002, 2.613311e-002, 3.820213e-002, 1.156533e-001, 3.728896e-002, 9.990636e-003, 2.251698e-002, 9.190944e-003, 3.385157e-003, 1.346085e-002, 2.232164e-001, 1.404766e-001, 1.000574e-001, 3.713713e-002, 3.742871e-002, 6.228638e-002, 8.680045e-002, +4.538948e-002, 3.085643e-002, 6.226057e-002, 4.376393e-002, 1.030241e-001, 1.187234e-001, 1.268477e-001, 3.438531e-002, 3.032358e-002, 5.136525e-002, 1.949450e-001, 1.671375e-001, 8.232520e-002, 1.173891e-001, 9.376871e-002, 9.377603e-002, 2.881532e-001, 1.531642e-001, 2.247632e-001, 5.361593e-002, 3.006065e-002, 9.557330e-003, +9.229763e-002, 7.581493e-002, 2.800646e-002, 4.938894e-003, 2.925987e-003, 8.426805e-004, 2.497385e-003, 1.447913e-003, 6.623555e-003, 8.547769e-003, 9.258792e-003, 1.161643e-002, 1.568589e-002, 5.572530e-003, 1.743025e-003, 9.842838e-004, 1.700295e-003, 1.266699e-003, 5.938052e-003, 9.971865e-002, 5.093864e-002, 2.445552e-002, 1.249674e-002, 1.753380e-002, 1.925248e-002, +2.489494e-002, 1.303133e-002, 8.770875e-003, 2.010519e-002, 1.741559e-002, 5.227946e-002, 4.642764e-002, 2.941956e-002, 1.038810e-002, 1.572034e-002, 3.044708e-002, 1.057709e-001, 1.008852e-001, 4.208901e-002, 6.247671e-002, 6.378938e-002, 6.570971e-002, 2.277334e-001, 1.160749e-001, 1.070846e-001, 2.031522e-002, 4.904222e-003, +1.038819e-001, 9.759139e-002, 3.148781e-002, 1.134675e-002, 2.336375e-003, 5.520160e-004, 2.387111e-004, 6.206461e-004, 4.128726e-004, 1.768136e-003, 3.712737e-003, 3.999041e-003, 2.144634e-003, 2.695335e-003, 1.460466e-003, 1.423542e-004, 2.118482e-004, 7.267756e-004, 8.913628e-004, 3.094742e-003, 3.783838e-002, 2.214551e-002, 1.601248e-002, 1.368177e-002, 9.860711e-003, +8.512429e-003, 9.773647e-003, 6.606418e-003, 5.058864e-003, 1.018365e-002, 1.428581e-002, 2.654175e-002, 1.726216e-002, 1.440923e-002, 1.316025e-002, 1.752149e-002, 2.662442e-002, 8.172076e-002, 9.568157e-002, 7.521727e-002, 9.590234e-002, 1.144982e-001, 1.455994e-001, 2.889700e-001, 1.977578e-001, 1.184872e-001, 2.329114e-002), +.Dim = c(47,6), +.Dimnames = list( +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"), +c("1", "2", "3", "4", "5", "6"))) + +), .Names = c("ind01.ob", "ind01.pr", "ind02.ob", "ind02.pr")) + +, index.Neff.init = structure(c( +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001, +2.000000e+001, 2.000000e+001), +.Dim = c(2,47), +.Dimnames = list( +c("1", "2"), +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"))) + +, index.Neff.est = structure(c( +8.605105e+001, 1.002976e+002, +1.032045e+002, 1.429562e+001, +9.872289e+000, 4.675218e+001, +5.841043e+001, 4.900959e+001, +2.950518e+001, 1.895459e+001, +4.292042e+001, 1.597350e+001, +1.683497e+001, 2.906079e+001, +2.554618e+001, 1.250528e+001, +8.203261e+000, 2.122445e+001, +9.014158e+001, 2.335516e+001, +7.082840e+001, 5.473687e+001, +4.709510e+000, 6.847517e+001, +2.404542e+001, 1.353698e+001, +1.593800e+001, 5.494262e+001, +7.851721e+000, 4.650338e+002, +6.160180e+000, 4.296650e+001, +3.865671e+001, 1.083207e+001, +8.416155e+000, 1.826789e+001, +1.486054e+001, 3.949691e+000, +2.614926e+000, 3.176858e+000, +6.966111e+000, 4.357477e+000, +3.571315e+000, 1.385624e+002, +5.429003e+000, 1.818284e+001, +1.148654e+001, 5.237730e+000, +2.298034e+001, 4.644337e+000, +6.425961e+000, 5.066752e+001, +1.646915e+001, 8.401697e+000, +8.922562e+000, 7.218912e+000, +4.588158e+000, 2.987063e+001, +4.106901e+000, 3.875300e+001, +1.159498e+002, 2.174014e+000, +1.115041e+001, 1.560957e+001, +1.645881e+001, 1.408797e+001, +1.227350e+001, 7.059264e+001, +1.056685e+002, 1.322416e+001, +1.840691e+001, 3.240958e+001, +8.350476e+001, 4.411628e+001, +5.065519e+001, 1.327715e+001, +6.190095e+001, 1.046478e+001, +4.423422e+001, 5.570273e+001, +3.615652e+001, 3.547996e+001, +3.617117e+001, 4.097951e+001, +1.511008e+001, 4.219241e+000, +7.491924e+000, 1.296523e+001, +3.445391e+001, 2.130889e+001, +3.012535e+001, 1.183905e+001, +9.100280e+000, 4.793529e+000), +.Dim = c(2,47), +.Dimnames = list( +c("1", "2"), +c("1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", + "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"))) + +, RMSE.n = structure(list( +rmse.n.catch.tot = 47, +rmse.n.discard.tot = 0, +rmse.n.ind01 = 47, +rmse.n.ind02 = 47, +rmse.n.ind.total = 94, +rmse.n.N.year1 = 0.000000e+000, +rmse.n.Fmult.year1 = 0.000000e+000, +rmse.n.Fmult.devs.total = 0.000000e+000, +rmse.n.recruit.devs = 0.000000e+000, +rmse.n.fleet.sel.params = 0.000000e+000, +rmse.n.index.sel.params = 0.000000e+000, +rmse.n.q.year1 = 0.000000e+000, +rmse.n.q.devs = 0.000000e+000, +rmse.n.SR.steepness = 0.000000e+000, +rmse.n.SR.scaler = 0.000000e+000), +.Names = c("rmse.n.catch.tot", "rmse.n.discard.tot", "rmse.n.ind01", "rmse.n.ind02", "rmse.n.ind.total", "rmse.n.N.year1", "rmse.n.Fmult.year1", "rmse.n.Fmult.devs.total", "rmse.n.recruit.devs", "rmse.n.fleet.sel.params", "rmse.n.index.sel.params", "rmse.n.q.year1", "rmse.n.q.devs", "rmse.n.SR.steepness", "rmse.n.SR.scaler")) + +, RMSE = structure(list( +rmse.catch.tot = 4.210818e-001, +rmse.discard.tot = 0, +rmse.ind01 = 1.140115e+000, +rmse.ind02 = 1.555466e+000, +rmse.ind.total = 1.363697e+000, +rmse.N.year1 = 0.000000e+000, +rmse.Fmult.year1 = 0.000000e+000, +rmse.Fmult.devs.total = 0.000000e+000, +rmse.recruit.devs = 0.000000e+000, +rmse.fleet.sel.params = 0.000000e+000, +rmse.index.sel.params = 0.000000e+000, +rmse.q.year1 = 0.000000e+000, +rmse.q.devs = 0.000000e+000, +rmse.SR.steepness = 0.000000e+000, +rmse.SR.scaler = 0.000000e+000), +.Names = c("rmse.catch.tot", "rmse.discard.tot", "rmse.ind01", "rmse.ind02", "rmse.ind.total", "rmse.N.year1", "rmse.Fmult.year1", "rmse.Fmult.devs.total", "rmse.recruit.devs", "rmse.fleet.sel.params", "rmse.index.sel.params", "rmse.q.year1", "rmse.q.devs", "rmse.SR.steepness", "rmse.SR.scaler")) + +, Neff.stage2.mult = structure(list( + +Neff.stage2.mult.catch = structure(c( +2.849021e-001), +.Names = NULL) + +, Neff.stage2.mult.discard = structure(c( +1.000000e+000), +.Names = NULL) + +, Neff.stage2.mult.index = structure(c( +4.893687e-001, 5.618635e-001), +.Names = NULL) + +), .Names = c("Neff.stage2.mult.catch", "Neff.stage2.mult.discard", "Neff.stage2.mult.index")) + + +### Calling close_r_file -- last call in program. +), + +.Names = c("info", "parms", "options", "like", "like.additional", "initial.guesses", "control.parms", "sel.input.mats", "WAA.mats", "M.age", "maturity", "fecundity", "N.age", "Z.age", "F.age", "catch.obs", "catch.pred", "catch.std.resid", "discard.obs", "discard.pred", "discard.std.resid", "catch.comp.mats", "fleet.sel.blocks", "fleet.sel.start.age", "fleet.sel.end.age", + "fleet.sel.option", "fleet.sel.mats", "fleet.Fmult", "fleet.FAA", "fleet.prop.release", "fleet.catch.Neff.init", "fleet.catch.Neff.est", "fleet.discard.Neff.init", "fleet.discard.Neff.est", "q.indices", "F.report", "tot.jan1.B", "SSB", "exploitable.B", "Fref", "SR.parms", "SR.resids", "SR.annual.parms", "index.sel", "index.nobs", "index.year.counter", "index.year", "index.cv", "index.sigma", "index.obs", + "index.pred", "index.std.resid", "index.comp.mats", "index.Neff.init", "index.Neff.est", "RMSE.n", "RMSE", "Neff.stage2.mult")) diff --git a/content/figures/NEFSC_YT_compare_FRSSB3.png b/content/figures/NEFSC_YT_compare_FRSSB3.png new file mode 100644 index 0000000..f19e843 Binary files /dev/null and b/content/figures/NEFSC_YT_compare_FRSSB3.png differ diff --git a/content/figures/NEFSC_YT_compare_NAA2.png b/content/figures/NEFSC_YT_compare_NAA2.png new file mode 100644 index 0000000..7c9f0a7 Binary files /dev/null and b/content/figures/NEFSC_YT_compare_NAA2.png differ diff --git a/content/figures/NEFSC_YT_compare_catch.png b/content/figures/NEFSC_YT_compare_catch.png new file mode 100644 index 0000000..a750b9d Binary files /dev/null and b/content/figures/NEFSC_YT_compare_catch.png differ diff --git a/content/figures/NEFSC_YT_compare_index.png b/content/figures/NEFSC_YT_compare_index.png new file mode 100644 index 0000000..f342fb6 Binary files /dev/null and b/content/figures/NEFSC_YT_compare_index.png differ