Warning: this information will not be complete until Friday Nov 22nd, so students will need to check back at that time.
Integrating environmental impacts on stock productivity in Management Strategy Evaluation models (ICES webpage)
Repository for training course material
In order to save some time during the course, make sure you have the following R-packages installed. We grouped those by each practical/exercise in case you have some troubles installing a few packages, so you could at least follow the other tutorials.
- from CRAN:
install.packages( c("raster","terra","maps","sf",
"kohonen","corrplot",
"PCDimension","devtools",
"cleanrmd","basetheme"))
# additionally some packages are already archived on CRAN, so you need to install the last available version
devtools::install_version("maptools", version = "1.1.8", repos = "http://cran.us.r-project.org")
devtools::install_version("rgeos", version = "0.6.4", repos = "http://cran.us.r-project.org")- from github:
devtools::install_github("BernhardKuehn/marmalaid")
devtools::install_github("coatless-rd-rcpp/rcpp-and-doparallel")
devtools::install_github("BernhardKuehn/fast.EOT")- from CRAN:
install.packages(c("raster","terra","maps",
"reshape2","qmap","ggplot2",
"ggdark","cleanrmd","basetheme",
"devtools","parallel","doParallel","foreach"))
# additionally some packages are already archived on CRAN, so you need to install the last available version
devtools::install_version("maptools", version = "1.1.8", repos = "http://cran.us.r-project.org")
devtools::install_version("rgeos", version = "0.6.4", repos = "http://cran.us.r-project.org")- from github:
devtools::install_github("BernhardKuehn/marmalaid")- from CRAN:
install.packages(c("raster","terra","maps","plyr",
"BVAR","scam","mgcv","reshape2",
"devtools","parallel","doParallel","doSNOW",
"foreach","funtimes","coda","tsDyn",
"gsignal","FNN","Rcpp","RcppEigen",
"RcppArmadillo","ggplot2","colorspace",
"patchwork","ggdark","cleanrmd","basetheme"))- from github:
devtools::install_github("BernhardKuehn/marmalaid")
install.packages(c(
"glmmTMB", "RTMB",
"MuMIn", "bbmle",
"broom.mixed",
"doParallel", "foreach",
"ggplot2", "patchwork"
))
install.packages(c("modelr","nlstools","MuMIn","rlist","formula.tools","corrplot"))
install.packages(c("corrplot", "doRNG", "dplyr", "forecast", "GA", "ggplot2",
"glmmTMB", "icesAdvice", "knitr", "memoise", "MuMIn", "parallel",
"patchwork", "tidyr"))To prepare for the section where we will run some MSEs with environmentally mediated data, you will need to install and experimental version of smsR in R, and read the WKECOMSE report available on the sharepoint. To install smsR directly from Github you need to have the library remotes installed.
if('remotes' %in% installed.packages()[,"Package"] ) == FALSE) install.packages('remotes')
remotes::install_github("https://github.com/nissandjac/smsR", dependencies = TRUE) # Change this link to the development versionThis will install smsR and the required packages needed to run the model (e.g., TMB). This installs the package smsR, which is a seasonal stock assessment model, that can be used to regular stock assessments, but it also has the potential to 1) include seasonality, 2) include environmental covariates in the recruitment estimation, 3) various options for natural mortality estimation as random walk, or with predator input.
To test if the model is properly working you can run the sandeel stock assessment included in the package 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)
And if the run was successful you can plot the output results easily as
plot(sas) # Plots general stock assessment output
This is a fairly simple model that runs without any climatic or other external data, but works as a baseline assessment model.
from zip: ALADYMTools
from Cran:gridExtra, akima, stringr, Hmisc, timeDate,reshape, scales, FLBRP
from http://flr-project.org/R: FLCore, ggplot2, FLasher
if (!("iterators" %in% installed.packages()[,1])) {
install.packages("iterators")
}
if (!("FLCore" %in% installed.packages()[,1])) {
#install.packages("FLCore",repos="http://flr-project.org/R")
remotes::install_github("flr/FLCore")
}
if (!("ggplot2" %in% installed.packages()[,1])) {
install.packages("ggplot2")
}
if (!("gridExtra" %in% installed.packages()[,1])) {
install.packages("gridExtra")
}
if (!("akima" %in% installed.packages()[,1])) {
install.packages("akima")
}
if (!("stringr" %in% installed.packages()[,1])) {
install.packages("stringr")
}
if (!("RGtk2" %in% installed.packages()[,1])) {
install.packages("RGtk2")
}
if (!("Hmisc" %in% installed.packages()[,1])) {
install.packages("Hmisc")
}
if (!("timeDate" %in% installed.packages()[,1])) {
install.packages("timeDate")
}
if (!("reshape" %in% installed.packages()[,1])) {
install.packages("reshape")
}
if (!("scales" %in% installed.packages()[,1])) {
install.packages("scales")
}
if (!("tryCatchLog" %in% installed.packages()[,1])) {
install.packages("tryCatchLog")
}
if (!("FLasher" %in% installed.packages()[,1])) {
install.packages("FLasher",repos="http://flr-project.org/R")
}
if (!("FLBRP" %in% installed.packages()[,1])) {
# install.packages("FLBRP",repos="http://flr-project.org/R")
remotes::install_github("flr/FLBRP")
}
if (!("FLasher" %in% installed.packages()[,1])) {
# install.packages("FLBRP",repos="http://flr-project.org/R")
remotes::install_github("flr/FLasher")
}
RGtk2 installation
A specific procedure has to be carried out to install RGtk2:
-
Download GTK+ dependencies (gtk+-bundle_2.22.1-20101229_win64.zip file) from https://download.gnome.org/binaries/win64/gtk+/2.22/
-
Unzip it to the path of your choice.
-
Set the environment variable PATH to where GTK+ was unzipped. You could use this command from within R:
Sys.setenv(PATH = "Path\to\gtk+") example: Sys.setenv(PATH = "C:\Users\Utente\training")
-
Install RGtk2 from Togaware: install.packages("https://access.togaware.com/RGtk2_2.20.36.2.zip", repos=NULL)
or from Github:
devtools::install_github("cran/RGtk2")
-
Import the library: library(RGtk2)
-
A dialog window may appear asking you to intall GTK+. This should install some missing parts of the GTK+ or refer RGtk2 of the GTK+ installed in step 1.
-
Restart R.
- from CRAN:
install.packages( c("plyr","stringr","reshape2","glmmTMB",
"earth","ggplot2","patchwork"))- from FLR:
install.packages( c("FLCore", "FLFleet", "FLBEIA",
"FLash", "FLAssess"),
repos="http://flr-project.org/R")
