Bespoke ADMB Model (GOA Pollock)

Getting started with {afscOSA}

To get started you’ll need to install afscOSA, which also relies on {compResidual}. To install these libraries from Github, using the following commands:

# downloading compResidual:
# https://github.com/fishfollower/compResidual#composition-residuals for
# installation instructions

# TMB:::install.contrib("https://github.com/vtrijoulet/OSA_multivariate_dists/archive/main.zip")
# remotes::install_github("fishfollower/compResidual/compResidual", force=TRUE)

# remotes::install_github("noaa-afsc/afscOSA", force=TRUE)

library(afscOSA)

In this vignette we show how to afscOSA with a bespoke ADMB model using an older GOA pollock assessment model as an example.

Once the data is loaded, the general workflow is as follows:

1. Structure the observed and predicted age/length compositions as matrices with nrows = number of years and ncols = number of age or length bins.

2. Calculate OSA residuals for each fleet using run_osa(). See details for inputs and outputs by running ??run_osa().

3. Plot OSA residuals and aggregate fits for one or more fits using plot_osa(). Input to plot_osa() is a list of output(s) from run_osa(). See more details by running ??plot_osa.

# load pollock data 
datfile <- afscOSA::goapkdat
repfile <- afscOSA::goapkrep

# fishery
ages <- 2:10
N <- datfile$multN_fsh # this gets rounded
keep <- which(N>=1)
N <- N[keep]
yrs <- datfile$fshyrs[keep]
obs <- repfile$Fishery_observed_and_expected_age_comp[keep,ages]
exp <- repfile$Fishery_observed_and_expected_age_comp[keep,10+ages]
out0 <- run_osa(fleet = 'Fishery', index_label = 'Age',
                obs = obs, exp = exp, N = N, index = ages, years = yrs)

# survey 1 
ages <- 3:10
# years with data
yrs <- datfile$srv_acyrs1
# observed age comps
obs <- repfile$Survey_1_observed_and_expected_age_comp[ ,ages]
# predicted age comps from assessment model
exp <- repfile$Survey_1_observed_and_expected_age_comp[ ,10+ages]
# assumed effective sample sizes
N <- datfile$multN_srv1 # this gets rounded
out1 <- run_osa(fleet = 'Survey1', index_label = 'Age',
                obs = obs, exp = exp, N = N, index = ages, years = yrs)
# out1$res # osa residual for each age and year
#out1$agg # observed and expected value for each age aggregated across all yrs

# survey2
ages <- 1:10
yrs <- datfile$srv_acyrs2
obs <- repfile$Survey_2_observed_and_expected_age_comp[ ,ages]
exp <- repfile$Survey_2_observed_and_expected_age_comp[ ,10+ages]
N <- datfile$multN_srv2 # this gets rounded
out2 <- run_osa(fleet = 'Survey2', index_label = 'Age',
                obs = obs, exp = exp, N = N, index = ages, years = yrs)

# survey3
N <- datfile$multN_srv3 # this gets rounded
keep <- which(N>0)
N <- N[keep]
yrs <- datfile$srv_acyrs3[keep]
obs <- repfile$Survey_3_observed_and_expected_age_comp[keep ,ages]
exp <- repfile$Survey_3_observed_and_expected_age_comp[keep ,10+ages]
out3 <- run_osa(fleet = 'Survey3', index_label = 'Age',
                obs = obs, exp = exp, N = N, index = ages, years = yrs)

# needs to be in list format
input <- list(out0,out1, out2, out3)
osaplots <- plot_osa(input)
#> Warning in plot_osa(input): The following Pearson residuals were set to 6 for
#> plotting: 6.01 14.03

Note on Residual Truncation:

The warning above indicates that extreme Pearson residuals (e.g., 6.01, 14.03) were capped at 6 for visualization. Truncating extreme outliers prevents them from compressing the overall scale, allowing residual patterns across age bins and years to remain clearly legible and directly comparable across different assessment models.