smsR is a seasonal stock assessment model package using TMB to estimate parameters. The model is the current assessment model for sandeel (4 stocks) and sprat in the North Sea.
You can install the development version of smsR as
remotes::install_github("https://github.com/nissandjac/smsR", dependencies = TRUE)The model can be run with the provided data set {r} sandeel_1r and the
Sandeel 1r standard configuration as
library(smsR)
# Set up sandeel for area 1r
df.tmb <- get_TMB_parameters(
mtrx = sandeel_1r$lhs, # List that contains M, mat, west, weca
Surveyobs = sandeel_1r$survey, # Survey observations
Catchobs = sandeel_1r$Catch, # Catch observations
years = 1983:2021, # Years to run
nseason = 2, # Number of seasons
useEffort = TRUE, # Use effort to calculate F
ages = 0:4, # Ages of the species
recseason = 2, # Season where recruitment occurs
CminageSeason = c(1, 1), # Minimum catch age per season
Fmaxage = 3, # Fully selected fishing mortality age
Qminage = c(0, 1), # Minimum age in surveys
Qmaxage = c(1, 3), # Max age in surveys
Fbarage = c(1, 2), # Age use to calculate Fbar
effort = sandeel_1r$effort, # Effort input
blocks = c(1983, 1999), # Blocks with unique selectivity
nocatch = sandeel_1r$nocatch, # Seasons where F is not calculated
surveyStart = c(0.75, 0), #
surveyEnd = c(1, 0), #
surveySeason = c(2, 1), #
surveyCV = list(c(0, 1), c(1, 2)),
catchCV = list(c(1, 3), c(1, 3)), # Catch CV groupings
estCV = c(0, 2, 0), # Estimate CVs for 1) survey, 2) catch, 3) Stock recruitment relationship
beta = 105809, # Hockey stick break point
nllfactor = c(1, 1, 0.05) # Factor for relative strength of log-likelihood
)
parms <- getParms(df.tmb)
sas <- runAssessment(df.tmb, parms)
#> Congrats, your model convergedThe resulting fit to the data can then be visualized in a summary output showing SSB, Catch, recruitment and Fbar as
plot(sas, printFig = FALSE)
Or to evaluate the retrospective patterns as
mr <- mohns_rho(df.tmb, peels = 5, parms = parms, plotfigure = FALSE)
mr$p1()
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent